HARNETT_Dissertation

School of Earth and
Environmental
Sciences
Title: Time dependent
deformation in El
Hierro Basalt and the
associated risk of flank
instability.
Name: Claire Harnett
Course: BSc Geological Hazards
Student No: 647951
Year: 2014/15

Time dependent deformation in El Hierro Basalt and the associated risk of flank instability. 2015

School of Earth and Environmental Sciences
Time dependent deformation in El Hierro
Basalt and the associated risk
of flank instability.
Claire Harnett
647951
BSc (Hons) Geological Hazards
2015

Abstract
The volcanic island of El Hierro (Canary Islands, Spain) has historically suffered from large
scale gravitational collapses, and presently suffers from frequent smaller scale rockfalls.
Previous flank collapses have generated an island geomorphology that is vulnerable to whole
edifice instability. This dissertation presents new data exploring edifice stability with respect
to rock “creep” at elevated pressures/temperatures, in conjunction with local hazards from
slope stability and rockfall. Time dependent deformation (creep) is a process whereby a
brittle material fails at a stress much lower than the material strength, due to the application
of stress over a long period of time. In volcanic settings, this process may be accelerated due
to stress corrosion, the propagation of crack tips accelerated by active pore fluids, and
elevated temperatures, all of which are common in volcanic areas. To explore the effect of
time dependent deformation on flank stability, laboratory experiments were carried out to
measure creep at shallow depths under elevated temperatures. This study finds a decrease in
strength (up to 32%) with increasing temperatures of 90°C, 120°C, 140°C and 180°C, in
addition to an inverse relationship between temperature and strain rate. Strain rates are
observed up to 10-9
. These data are compared to an extensive review and field mapping of the
geology, geomorphology, hazard, vulnerability and risk in the study area. Analysis from
historical rockfalls was also incorporated into the field dataset, showing that the trajectories
of potential rockfalls directly threaten the town of Sabinosa. Taken together, this
multidisciplinary evaluation of the hazard presented to the northwest of El Hierro suggests
that the greatest hazard occurs at Playa de Arena Blancas and the highest economic
vulnerability in Pozo de la Salud. However, the combination of all data, including population
density identifies the region in the study with the highest overall risk is Sabinosa.

Contents
Acknowledgements i
1.0 Introduction 1
1.1 Rationale 1
1.2 Aims 1
1.3 Objectives 2
2.0 Desk Study 3
2.1 Geographical Location of Study Area 3
2.2 Tectonic Setting 4
2.3 Geology 5
2.3.1 Geological Setting 5
2.3.2 Gravitational Collapses 6
2.4 Historical Hazard Background 8
3.0 Field Data Collection and Analysis Techniques 11
3.1 Field Data Collection 11
3.1.1 Site Reconnaissance 11
3.1.2 Geological and Geomorphological Mapping 12
3.1.3 Falling Rock Hazard Surveys and Block Assessment 12
3.1.4 Field Observations 12
3.1.5 Sample Collection 13
3.2 Analysis Techniques 15
3.2.1 Hazard and Vulnerability Mapping 15
3.2.2 Risk Mapping 15
3.2.3 Rockfall Modelling 16
4.0 Results and Analysis 21
4.1 Geological Model 21
4.2 Geomorphological Model 21
4.3 Combined Hazard Map 25
4.4 Vulnerability Maps 29
4.5 Risk Analysis 34

4.6 Rockfall Modelling 36
5.0 Laboratory Investigation and Analysis 46
5.1 Triaxial Testing Methods 46
5.1.1 Sample Selection 46
5.1.2 Sample Preparation 48
5.1.3 Laboratory Method 50
5.2 Triaxial Results and Analysis 57
5.2.1 Brittle Creep 57
5.2.2 Acoustic Emissions 60
6.0 Discussion 62
7.0 Conclusions 65
7.1 Limitations 66
7.2 Recommendations 67
7.3 Further Work 69
8.0 References 70
9.0 Appendices 79
Appendix 1: Climate statistics for El Hierro 79
Appendix 2: Walking trails in El Hierro 80
Appendix 3: FRHI survey locations 81
Appendix 4: FRHI field surveys 82
Appendix 5: Zone numbering for hazard and risk maps 84
Appendix 6: Calculating average slope angle above Sabinosa 85
Appendix 7: RocFall slope spreadsheet 86
Appendix 8: Block analysis 87
Appendix 9: Logged scoria section 89

Appendix 10: Hotel details, Balneario Pozo de la Salud 91
Appendix 11: Petrological exploration of El Hierro basalt and spatter 92
Appendix 12: Flow texture visible in spatter sample 94
Appendix 13: InSite coordinates used as input parameters 96
Appendix 14: Excel spreadsheet used to calculate pressure requirements to simulate a
certain depth 97
Appendix 15: MATLAB code to correct time offset 98
Appendix 16: Invalid seismic ray paths in acoustic emission setup 100
Appendix 17: MATLAB code for velocity model, using the P-wave arrival times
exported from InSite 101
Appendix 18: MATLAB code for smoothing 106
Appendix 19: Raw strain rate data 108
Appendix 20: Data from creep experiments on Tenerife basalt, undertaken at the
University of Portsmouth 110
Appendix 21: Density of vegetation on slopes above Sabinosa 111
Appendix 22: Example size of landslide scar 112

List of Figures
2.0 Desk Study
Figure 2.1: Geographical location of El Hierro, and centre of the study area (Grafcan,
2014) 3
Figure 2.2: Geographical constraints of study area with areas of focus highlighted 3
Figure 2.3: Published geological map (Carracedo et al., 2001) 5
Figure 2.4: an example of an image taken from the Digital Elevation Model, created
using data provided by the Insituto Geográfico Nacional. 6
Figure 2.5: Map to show the section of road closed due to 2011 seismicity (Google Earth,
2014) 8
Figure 2.6: Seismic activity in El Hierro between July 2013 and July 2014, using data
from the IGN 9
Figure 2.7: Change in seismicity between July 2011 and July 2014, using number of
seismic events per month. 10
3.0 Field Data Collection and Analysis Techniques
Figure 3.1: Published geological map (Carracedo et al., 2001) and collected field samples
with detailed collection localities. 14
Figure 3.2: Google Earth image showing the profile of Slope 1, overlain by the published
geology map. 18
Figure 3.3: Google Earth image showing the profile of Slope 2, overlain by the published
geology map. 18
Figure 3.4: Frequency plot to show the masses of fallen boulders in the north west of the
island. 20
4.0 Results and Analysis
Figure 4.1: Geological model of north-western El Hierro 22
Figure 4.2: Geomorphological model of Sabinosa 23

Figure 4.3: Combined hazard map 27
Figure 4.4: Vulnerability map to show vulnerability to the economy in Sabinosa 30
Figure 4.5: Vulnerability map to show population vulnerability in Sabinosa 31
Figure 4.6: Vulnerability map to show vulnerability to economy in Pozo de la Salud 32
Figure 4.7: Vulnerability map to show population vulnerability in Pozo de la Salud 33
Figure 4.8: Risk map for Sabinosa, Pozo de la Salud and the surrounding area, using the
risk equation described 35
Figure 4.9: RocFall 5.0 model for Slope 1 with above parameters and a mass of 200kg
and a simulation of 50 rocks 36
Figure 4.10: Graph to show the endpoints of falling blocks for Slope 1 when simulation
50 rocks falling, each with a mass of 200kg 36
Figure 4.11: RocFall 5.0 model for Slope 1 with above parameters and a mass of 2000kg
and a simulation of 50 rocks 37
Figure 4.13: RocFall 5.0 model for Slope 1 with above parameters and a mass of
10000kg and a simulation of 50 rocks 38
Figure 4.15: RocFall 5.0 model for Slope 2 with parameters outlined in Section 3.2.3, a
mass of 200kg and a simulation of 50 rocks 39
Figure 4.16: Graph to show the endpoints of falling blocks for Slope 2 when simulating
Figure 4.17: Graph showing the profile of Slope 2, overlain with the kinetic energy from
a simulation of 50 rocks falling, each with a mass of 200kg 41

Figure 4.18: RocFall 5.0 model for Slope 2 with outlined parameters, a mass of 5700kg
and a simulation of 50 rocks. 42
Figure 4.19: Graph to show the endpoints of falling blocks for Slope 2 when simulating
Figure 4.20: Graph showing the profile of Slope 2, overlain with the kinetic energy from
a simulation of 50 rocks falling, with a mass of 5700kg 44
5.0 Laboratory Investigation and Analysis
Figure 5.1: Schematic to show the triaxial machine (Fazio et al., 2014) 50
Figure 5.2: Graph to show the occurrence of D’ 52
Figure 5.3: Cumulative frequency plot of AE survey success rate 54
Figure 5.4: Screenshot from InSite software 56
Figure 5.5: MATLAB plot to show the effect of data smoothing 57
Figure 5.6: Percentage of strength against strain rate and the effect of temperature in El
Hierro basalt (density ~ 2.86g/cm3, porosity ~ 3%) 58
Figure 5.7: Failure stresses of El Hierro basalt according to temperature, expressed as %
of material strength 59
Figure 5.8: continuous AE record of the last ten minutes of the experiment using sample
EHB04_04 at 140°C with Pc = 25.3MPa and Pp = 8.9MPa. AE event locations harvested
from continuous record. P-wave velocity adapted from original velocity model as
follows: (a) 80%; (b) 90%; (c) 100%; (d) 110%; (e) 120%. All views displayed with
azimuth = 105° and symbol size represents relative event magnitude. 60
6.0 Discussion
Figure 6.1: Laboratory seismicity in the lead up to failure 63

List of Plates
2.0 Desk Study
Plate 2.1: small scale instabilities in the scoria deposits along HI-50 from Sabinosa to
Pozo de la Salud. 10
Plate 3.1: site used for block analysis at 27°45’03.7”N, 018°06’12.3”W 13
Plate 3.2: Photo showing rock fall along the coastal road joining HI-550 and HI-551, at
N27°45’36.5”, W018°06’57.8 16
Plate 4.1: exposed area on steep slopes above Sabinosa 24
Plate 4.2: Road cracking witnessed at 27°48’21.3”N, 017°55’00.3”W 28
Plate 4.3: Back view of Hotel Balneario Pozo de la Salud, at 27°45’21”N,
018°06’17.7”W 29
Plate 4.4: View from terrace of hotel to sea, to show existing wall 29
Plate 5.1: AE sensors connected to the rubber jacket 53
Plate 5.2: failed sample of EHB04_04 (140°C, Pp=8.9MPa and Pc = 25.3MPa), orientated
west, with fracture correlating to AE results shown in Figure 5.8. 61

List of Tables
2.0 Desk Study
Table 2.1: Main geological units on El Hierro 5
Table 3.1: Input parameters for RocFall models 19
Table 4.1: Input values for creating the combined hazard map 25
Table 4.2: Input values for the risk calculation 34
Table 5.1: Descriptions and ages of all collected samples 47
Table 5.2: Summary of the petrographic analysis 47
Table 5.3: Dimensions of cored samples 48
Table 5.4: Properties of cored samples, including calculated densities and porosities 49
Table 5.5: Acoustic emission sensor setup 53
Table 5.6: Processing parameters in InSite 55
Table 5.7: experiment list with corresponding sample identifications and temperature
conditions, assuming the set conditions of Pp=8.9MPa and Pc=25.3MPa 57

i
Acknowledgements
I would firstly like to acknowledge my tutor Dr. Carmen Solana, and my supervisor Dr.
Philip Benson. Carmen has always gone above and beyond her role as my tutor and I thank
her for planting the initial seed of interest in Canarian volcanism. It is Phil I must thank for
sparking my love of rock mechanics and he has since given me a wealth of opportunities and
support, ranging anywhere from laboratory advice, to scientific writing and poster creation, to
hiking El Hierro and surviving the giant lizards! Phil and Carmen have undoubtedly made me
the student and researcher I am today, who is capable of undertaking this project.
I would like to thank Marco Fazio for his endless hours of patience, for his singing and
dancing, and for his company during overnight experiments. He has been a fundamental part
of this project; the laboratory work and the maintenance of some level of sanity and a whole
bundle of enthusiasm would not have been possible without him. I would also like to
acknowledge the rest of the crew at the Rock Mechanics Laboratory, University of
Portsmouth. Thanks to Emily Butcher and Stephen Gehne for the helping in the coring and
preparation of my samples. Thank you also to Dr. Pete Rowley for keeping us awake at the
end of an experiment, and for keeping us laughing the rest of the time.
There are naturally a whole host of people who have enabled this project to grow in the way
that it has, whether that was via time shared in the field, advice over email, or a chat over a
beer. I would therefore like to acknowledge: Dr. Laura Becerril, Maria José Blanco, Dr. Dean
Bullen, Itaheza Dominguez, Dominik Fahrner, Dave Giles, Dr. Mike Heap, Dr. Phil
Meredith, Dr. Derek Rust and Dr. Sebastian Wiesmaier.
I’d like to thank Nicole Gilbert for her constant friendship and for sticking with me through a
bear-infested forest! Thanks also to Juliette Forgham for always being at the end of a phone.
Last but not least I would like to acknowledge Dan Harnett and Chris Harnett for their
support, in particular Dan for ensuring we were not stranded in Gran Canaria! I would like to
thank Chris Harnett for her assistance during fieldwork in El Hierro and for driving the most
treacherous roads just so I could reach the best field sites. I will continue to aspire to have
even half of her dedication, her curiosity and her kindness.

1
1.0 Introduction
El Hierro has historically been subjected to large scale flank collapse (Manconi et al., 2009;
Rivera et al., 2013) in addition to smaller scale rockfalls (Oglialoro, 2013), particularly in the
northwest of the island. It is estimated that 10% of the total edifice volume has been removed
in the last 200,000 years (Gee et al., 2001). The areas of Sabinosa and Pozo de la Salud are
considered as the areas with the highest susceptibility to rockfall detachment (Dupont, 2012)
and Becerril et al. (2014) consider these towns to be in a medium hazard area. The primary
hazard to these areas is rockfall but further hazards such as lava flows and flooding are
considered. Small scale rock detachments were witnessed daily during fieldwork, and it is
likely that these have the potential to trigger a larger collapse event.
Due to the constant tectonic stresses caused by the island remaining in the ‘juvenile’ stage of
its development (Carracedo et al., 2001), it is expected that suitable pressure, temperature and
stress/strain conditions exist within this volcanic edifice to allow the process of brittle creep
to occur (Heap et al., 2012). This may lead to edifice failure at a stress lower than the
expected peak strength, particularly when elevated temperatures are present due to the
circulation of magma in a volcanic setting. The combination of immediate rockfall and time
dependent deformation leads to a large decrease in overall slope stability.
1.1 Rationale
The hazard to the smaller communities of Sabinosa and Pozo de la Salud is as of yet
unassessed as these hazards only affect approximately 400 people in this area. The purpose of
this thesis is to provide a hazard assessment for these villages in addition to exploring the
effect of time dependent deformation through subcritical crack growth to determine whether
the field observations provide an underestimation of the hazard. Although literature exists
regarding the process of time dependent deformation, it is rarely researched as an applied
process within a specific context (Schoenball et al., 2014).
1.2 Aims
This project will assess the hazards presented to the areas in the northwest of the island of El
Hierro and the dangers these hazards pose to the communities in this area. The primary aim is
to create a multidisciplinary risk model that combines field observations, rockfall modelling
and laboratory experimentation to give a more realistic view of the hazard, vulnerability and
risk in this area.

2
1.3 Objectives
In order for this thesis to be successful in meeting its aims, it is important to consider the
hazard occurrence as well as the impact of these hazards on the vulnerable infrastructure and
population. The following objectives must be fulfilled:
 Documentation of geological and geomorphological field setting through field
mapping and desk study
 Laboratory testing on the basalt collected from the field to explore the effect of
elevated temperatures on time dependent deformation
 Slope stability analysis using computer simulations to determine runout distances and
the impact of these potential rockfalls on island infrastructure
 Consideration of both rockfall hazard and brittle creep to create an overall risk model

3
2.0 Desk Study
2.1 Geographical Location of Study Area
The island of El Hierro is the westernmost island in the Canary Islands that are located just
off the north-western coast of Africa (Figure 2.1). The study area is located in the northeast of
the island and focuses on the village of Sabinosa, the coastal area of Pozo de la Salud, and the
surrounding land (Figure 2.2).
Figure 2.1: geographical location of El Hierro, and centre of the study area marked with a star (Grafcan, 2014)
Figure 2.2: geographical constraints of study area, with areas of focus highlighted further.
N
N

4
El Hierro has fewer than 11000 inhabitants (Becerril et al, 2014) with the majority of these
living in the capital Valverde. Sabinosa has an approximate population of 300 (Spanish
Estate, 2014).
2.2 Tectonic Setting
El Hierro is the oldest island in the oceanic-island chain of the Canary Islands, aged between
1.2 and 2 million years old (Carracedo et al, 2012; Rivera et al, 2013). The island is the result
of hotspot activity and is thought to have formed over a stationery source of magma
(Carracedo et al, 2001). This movement of crust over the hotspot has created a three branched
rift system similar to that seen on Tenerife, and these rift systems are at 120° angles to each
other as this is determined as the ‘least effort angle’ (Carracedo et al, 1994). The rifts are
most commonly defined by alignment of parasitic vents. There is some controversy as to
whether this three armed system is formed by gravitational spreading due to the overlapping
of three individual volcanic edifices (Münn et al, 2006; Dupont, 2012). However the
suggestion of a complete island that has undergone frequent mass gravitational sliding is
more commonly accepted. It is because of this trilobate shape that rift zone generated large
scale landslides are common across the Canary Islands; this will be further explored in
Section 2.3.
There are thought to be two magma chambers beneath El Hierro, however the plumbing
system of these magma chambers is still highly debated throughout the literature. Through
the use of length-thickness ratios of feeder dykes, the depth of the source magma chamber
has been estimated at approximately 11-15km (Becerril et al, 2013b); this also corresponds to
the earthquake foci in the 2011/2012 eruption (IGN, 2014). However it has also been
suggested that this depth represents a shallow magma chamber that is secondary to a reservoir
chamber at approximately 18-23km (Becerril et al, 2013b). El Hierro and La Palma are the
two islands that are still considered to be in the ‘juvenile’ stage of development, whereas
other islands in this chain are considered to be in the ‘post-erosional’ stage (Carracedo et al,
2001); this gives a reason for the recent instability on the island.

5
2.3 Geological Setting and Effect on Collapse
2.3.1 Geological Setting
El Hierro is primarily basaltic with differentiated lavas that are thought to originate from
three main volcanic cycles (Oglialoro, 2013; Carracedo et al, 2001) shown in Table 2.1. The
only published geological map for the island is shown in Figure 2.3 (Carracedo et al, 2001);
there are no published geological maps at a higher resolution.
Name Age Geological
Period
Description
Tiñor 1.12-0.08Ma Matuyama Picritic to hawaiitic-tephritic lavas
El Golfo-Las Playas 545-176Ka Brunhes Nephelinitic to basanitic to trachytic
Rift volcanism 158Ka  Brunhes Alkali picrites and basanites with minor
tephrites
Table 2.1: main geological units on El Hierro.
Figure 2.3: published geological map of El Hierro (Carracedo et al, 2001)

6
2.3.2 Gravitational Collapses
This study focuses on the embayment of El Golfo, the largest gravitational collapse seen on
the island (Figure 2.4). This embayment was originally interpreted as the remains of a vast
caldera (Gee et al, 2001) but has more recently been identified as a landslide scar due to
advances in bathymetric exploration. The age of this landslide is estimated to be
approximately 15000 years, due to the dating of offshore sediments (Gee et al, 2001). It is
expected to have removed 150-180km3
of material and is clearly visible in the digital
elevation model shown in Figure 2.4, as it now stands at 15km across, 10km inland and has a
headwall of over 1.4km in height (Oglialoro, 2012).
Figure 2.4: an example of an image taken from the Digital Elevation Model, created using data provided by the
Insituto Geográfico Nacional.
In addition to the largest El Golfo collapse, further collapses have been identified on the
island. The El Tiñor collapse is the oldest and has likely been buried by subsequent
volcanism. The Las Playas collapse is on the south-eastern flank and is identified as a debris
avalanche resulting from partial flank failure. The south-western flank presents the El Julan
landslide as well as the San Andreas Fault; however the fault is now expected to be inactive
and so does not present a current hazard (Oglialoro, 2012).

7
A large amount of the present day land surface has been covered by the more recent rift
volcanism but the collapsed areas are still exposed due to their magnitude and the volume of
material that has been removed. It has been estimated that the recurrence interval for such
large scale landslides is 300,000 years for each of the Canary Islands whilst they are still in
the ‘shield building’ stage of development (Masson et al, 2002). Overall it is approximated
that from the four main landslides mentioned, 420km3
of material has been removed in the
last 200,000 years; this amounts to ~10% of the total island volume (Gee et al, 2001).
The three-armed rift system on the island is identified as one of the most common controls on
gravitational collapse, as the sector collapses bisect the angle formed by the two rifting zones
(McGuire, 2003). It is suggested that large scale landsliding is key in the evolution of
volcanic islands still in the shield building phase (Masson et al, 2002) and as such the
volcano undergoes unsustainable volumetric expansion and so the material must collapse in
order to regain slope stability (Manconi et al, 2009). Furthermore the displacement associated
with long term edifice spreading (McGuire et al, 2003; Münn et al, 2006) acts as an internal
trigger for collapse. Seismic activity is also likely to trigger collapse events which may
present a very recent hazard due to the increase in seismicity in recent years, as discussed in
Section 2.4.
Limited external triggers are also expected to influence gravitational collapses on the island
such as precipitation and sea level change (McGuire, 2003). Although the island receives an
average of 170mm of rain a year (Instituto Nacional de Meteorologíca, 2001; Appendix 1),
8% of this is heavy rain with a further 3% during thunderstorms (WeatherSpark, 2014).
Therefore there is a possibility that heavy rainfall could increase the pore pressure within a
geological unit already prone to landsliding, thus reducing its strength and triggering
collapse.

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2.4 Historical Hazard Background
Two subaerial prehistoric eruptions have been identified on El Hierro; Tanganasoga ~4000
years ago and Montaña Chamuscada ~2500 years ago (Rivera et al, 2013). There have been
two further episodes of unrest in the last 600 years (Becerril et al, 2014). The island has been
in a state of rest until the 2011/2012 eruption, when a new vent opened 2km offshore at a
depth of 300m (Becerril et al, 2013a). A seismic crisis was declared due to a large increase in
seismicity in the north of the island. This seismicity migrated south, and was later replaced
with a constant harmonic tremor (Carracedo et al, 2012). The surface deformation throughout
the eruptive period was ~40mm (Carracedo et al, 2012) and there were more than 12500
seismic events for the entire eruptive period (Rivera et al, 2013). The eruption lasted 138
days and led to the evacuation of La Restinga in the south of the island. It is now predicted
that the probability of unrest in the next 20 years is “still significantly large” (Becerril et al,
2014).
As a result of the seismic activity associated with the 2011/2012 eruption, the Canarian
government evacuated 300 people from the regions surrounding Frontera, including those
from the town of Sabinosa (Gobierno de Canarias, 2011). One of the primary hazards related
to the seismic swarm was the landsliding across the island, particularly in the El Golfo area
(LaInformacion.com, 2011). The Daily Mail reported numerous rockfalls across the island
due to detachment via seismic shaking; this led to the closure of the Los Roquillos tunnel
linking the two main towns on the island: Valverde and Frontera (Worden, 2011). This
remained closed for 16 days (Gobierno de Canarias, 2011), increasing journey time between
the two towns by approximately 30 minutes. The HI-50 was also shut between the junctions
with HI-553 (Figure 2.5), effectively cutting off the entirety of Sabinosa (Gubin, 2011).
Figure 2.5: a map to show the section of the road closed due to 2011 seismicity (Google Earth, 2014)
N

9
The eruption negatively impacted tourism on the island, which is one of the three main
income sources and accounts for 32% of the GDP (Vervaeck and Daniell, 2011). The
eruption increased water temperatures surrounding the island from 24°C to 35.3°C, thus
killing the majority of marine life (Vervaeck and Daniell, 2011). Previously El Hierro was a
popular dive site (PADI, 2015) and so this has negatively impacted future island tourism.
The eruption was officially declared over in 2012 but seismic activity continued across the
island. Figure 2.6 shows the earthquakes that have occurred on El Hierro between July 2013
and July 2014.
Figure 2.6: seismic activity in El Hierro between July 2013 and July 2014, using data from the IGN.
The seismic activity that has occurred since the end of the eruption has been low in
magnitude, however there are features across the island that are likely to be vulnerable to
even these low magnitudes. For example, there are several scoria cones across the island that
are poorly consolidated and therefore very friable. Instabilities, small landslides or even
larger scale collapses could therefore be caused by the shaking caused by the low magnitude
seismic events. Some evidence of scoria instability was observed in the road cutting along
HI-50 that leads from Sabinosa to Pozo de la Salud (Plate 2.1).

10
There has been a general decreasing trend in seismicity since the eruption in 2011/12 (Figure
2.7), however there have still been further instances of seismic swarming in this period of
time, for example in March 2013 and a smaller swarm in October 2014. This suggests that the
area is still active, perhaps due to a recovery from the 2011/2012 event.
Plate 2.1: small scale instabilities in the scoria deposits along HI-50 from Sabinosa to Pozo de la Salud, photo
taken at 27°45’01.3”N, 018°05’58.5”W. Wall = 1m for scale.
Figure 2.7: the change in seismicity between July 2011 and July 2014, using number of seismic events per
month.

11
Fieldwork was a key element of the project as the study area lacks comprehensive literature,
particularly for the areas of Sabinosa and Pozo de la Salud. The primary field investigation
most importantly enabled sample collection for laboratory testing. The primary investigation
also enabled the preparation of several more detailed field maps than those currently
published. The entire primary data collection focused on the southeast escarpment of the El
Golfo collapse and comprised of the following:
 Geological Model
 Geomorphology Map
 Falling Rock Hazard Index Surveys and Block Analyses
 Field Observations
 Sample Collection
The analysis of this information lead to the addition of the following:
 Vulnerability Maps (Economical and Population)
 Hazard Maps
 Combined Risk Map
 Rock Fall Modelling
3.1 Field Data Collection
3.1.1 Site Reconnaissance
A preliminary visit was undertaken to El Hierro with the aid of Dr. Philip Benson, Dr.
Carmen Solana and Dr. Derek Rust. This allowed the findings of the desk study to be
confirmed and ensured the planned area for study was suitably accessible. An entire site
walkover was completed and sites for sample collection were identified. This also assessed
the safety of completing the primary field investigation alone, thus ensuring that the main
fieldwork trip continued with as few problems as possible.
The design of the primary field investigation used the walking trails on the island (TourMac,
2014) to ensure accessibility through the entire vertical section of the collapse scar (see
Appendix 2).

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3.1.2 Geological and Geomorphological Mapping
The geological and geomorphological mapping was generally undertaken on a 1:100m scale
to produce a more detailed geological map than that of Carracedo et al (2001) and due to the
size of the study area. Although the geological mapping was undertaken in more detail, it was
difficult to determine the difference between the older and fresher lavas in the field, therefore
the detail in the chronology was lost in this primary field investigation. It was identified that
the main area requiring geomorphological mapping was the scoria cone set that forms the
foundation of Sabinosa. In addition to this, the slope angles were calculated for all roads
within Sabinosa to help determine the runout of any larger boulders that would fall from the
steep slopes above the town. A series of landslides were identified in the northwest of the
region and these were mapped, however due to the scale and inaccessibility further
interpretation was carried out using Google Earth.
3.1.3 Rock Fall Assessment
Rock falls and landslides are identified as the greatest hazard on the island and as such were
investigated further to the geomorphological and hazard maps. Falling Rock Hazard Index
(FRHI) surveys were created by adapting the FRHI discussed by Singh (2004) and
undertaken at sites particularly vulnerable to rock fall across the island (see Appendices 3 and
4). In addition to this a block analysis was undertaken in the area surrounding Playa de
Arenas Blancas (Plate 3.1) as the FRHI surveys identified this as the locality most at risk of
rock fall. The block analysis records the dimensions of fallen blocks and their distance from
the base of the slope. By combining these data with the digital elevation model of the region,
it will allow a more accurate modelling of the rock fall runout (Evans and Hungr, 1993;
Okura et al, 2000; Dorren, 2003).
3.1.4 Field Observations
In addition to the rock fall assessments, further observational measurements have been taken
in the field. This included logging sections of volcanic deposits found in the area, particularly
scoria cone deposits. This logging process originates from the logging of sedimentary
lithologies (Tucker, 2011) but is adapted for igneous rocks, similarly to that used by Summer
(1998).

13
Plate 3.1: site used for block analysis at 27°45’03.7”N, 018°06’12.3”W
3.1.5 Sample Collection
Samples were collected from the field and Figure 3.1 shows the locations and descriptions of
the rock samples collected in the field. It is clear from the geological map also in Figure 3.1
(Carracedo et. al, 2001) that the majority of the samples have been collected from the main
study area described in Section 2.1 however two of the samples had to be collected to the east
of this region due to the lack of accessible exposure.
Samples were collected across the three main geological units in this area, however the main
rock type used in the laboratory element of this study comes from the pre glacial rift
volcanism unit, as this is the most representative rock type on the island.

14
Figure 3.1: published geological map (Carracedo et al., 2001) and collected field samples
with detailed collection localities.

15
3.2 Analysis Techniques
3.2.1 Hazard and Vulnerability Mapping
For each of the hazard, vulnerability and risk maps, the areas of Sabinosa and Pozo de la
Salud are divided into 12 zones (see Appendix 5). This zoning is primarily based on
topography and land use to try to keep the features in each zone at a similar hazard and
vulnerability score.
The hazard across the region was split into the individual hazards of rock fall, flooding, air
fall (e.g. ballistics, ash), lava and pyroclastic density currents. Each of these hazards was
assigned a value between 0 and 1 and then a combined hazard (H) could be calculated by
averaging these hazards scores. The scores assigned for air fall, lava flow and pyroclastic
flow hazard originates from both field observations and the detailed hazard modelling done
by Becerril et al (2014).
The vulnerability was modelled by separating population and economic vulnerability. Both
were again given a score between 0 and 1. This largely depended on present day land use, i.e.
agricultural land presents a large economical vulnerability due to the reliance of the
community on agricultural income, however there is low exposure of the residents and so
therefore the vulnerability to the population is lower. The average vulnerability therefore is
the mean average of both vulnerability measurements.
3.2.2 Risk Mapping
The risk was then calculated using an equation commonly used in disaster management
(Wisner et al, 2003; Cançado et al, 2008). This equation has however been adapted by adding
the element of ‘coping capacity’. As discussed by Cardona et al (2012), coping capacity
reduces the risk by considering the ability of the communities in the hazardous areas to
respond to, to anticipate, and to recover from the hazard. The final equation used is therefore
as follows:
𝑅𝑖𝑠𝑘 =
𝐻𝑎𝑧𝑎𝑟𝑑 × 𝑉𝑢𝑙𝑛𝑒𝑟𝑎𝑏𝑖𝑙𝑖𝑡𝑦
𝐶𝑜𝑝𝑖𝑛𝑔 𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦

16
3.2.3 Rockfall Modelling
A key aim in this project to not only consider the time-dependent deformation of the rock
units, but also link it with rockfall analysis. During the time spent in the field rock falls were
witnessed daily in the area to north west of Sabinosa and Pozo de la Salud, in the vicinity of
Arenas Blancas (Plate 3.2). Coastal rockfalls particularly have been identified as a hazard
within the Canarian Archipelago (Fernández et al., 2014). Rockfalls in this area do not have a
direct impact on the town of Sabinosa, however it is likely that they would affect the
community of Pozo de la Salud. Additionally, rockfalls block the main road that lead out of
Sabinosa and therefore largely impact the coping capacity of the area. This area is much more
sparsely vegetated than the slopes above Sabinosa, which are also modelled here.
Plate 3.2: a photo showing rock fall along the coastal road joining HI-550 and HI-551, at
N27°45’36.5”, W018°06’57.8
To simulate the trajectories of falling blocks across 2-dimensional cross sections, the RocFall
5.0 software suite was used together with elevation profiles generated in Google Earth
(Appendix 6). The DEM obtained is not of a high enough resolution to enable the slope to be
imported directly and as such, elevation profiles are created in Google Earth. This allows a
.csv file to be imported into RocFall directly to ensure the slope geometry is not
oversimplified.

17
The software operates on the definition of rock fall as a combination of rolling, free falling
and bouncing after initial detachment of a boulder from the main rock slope. The equation on
which the simulation is based is:
𝑣 = 2√𝑔(𝑠𝑖𝑛𝛼 − 𝑡𝑎𝑛𝜃𝑐𝑜𝑠𝛼)𝑙
whereby v = velocity, g = gravity, α = slope inclination, θ = rolling friction angle and l =
block movement distance (Dupont, 2012). The element of collision is considered to derive
the bounce of each rock and therefore the corresponding loss of kinematic energy.
After creating a slope in RocFall, the slope material for each section of the slope must be
defined (Appendix 8). The geological map by Carracedo et. al (2001) has been overlain on
the Google Earth projection (Figures 3.2 and 3.3) to allow the slope materials to most
accurately be assigned. Table 3.1 shows the assigned values and is colour coded in
accordance to the geological map and the RocScience colour coding. This does not operate as
a cross section, simply as a representation of the surface conditions across the slope.
An initial seed is assigned at the top of each of the slopes to show the most likely origin of
the rockfalls. For Slope 1, the horizontal and vertical velocities are valued at 1m/s and 2m/s
respectively after experimentation from Dupont (2012). Slope 2 however has a horizontal
velocity of 0.5m/s and a vertical velocity of 1m/s to account for the denser vegetation. In
order to simplify these modelled scenarios, no rotational velocity is currently considered, and
only a singular seed is allocated.
To account for natural variability, a random number approach is taken to the simulation. This
ensures the simulation involves realistic iterations. As a result the simulation settings design a
model that uses Latin Hypercube sampling; this is used rather than Monte Carlo sampling to
ensure that there is no disadvantageous effect of clustering (Olsson et al, 2003). The analysis
method is defined as ‘Rigid Body’ rather than ‘Lump Mass’ as this allows the incorporation
of block shape into the simulation (Ashayer, 2007; RocScience, 2013). It has also been
determined that rigid body models allow the design of more reliable and more conservative
protection measures because they effectively model the worst case scenario (Dadashzadeh et
al,2014).

18
Figure 3.2: Google Earth image showing the profile of Slope 1, overlain by the published geology map.
Figure 3.3: Google Earth image showing the profile of Slope 2, overlain by the published geology map.

19
Input Parameter Citation
Coefficient of normal restitution 0.82 Pfeiffer and Higgins, 1990
Coefficient of tangential restitution 0.85
Angle of internal friction (°) 37 Blijenberg, 1995
Pyroclasts (rift volcanism, post date maximum
glacial)
Input Parameter Reference
Coefficient of normal restitution 0.25 Budetta, 2010
Angle of internal friction (°) 37 Kelfoun et al, 2009
Lava flows (rift volcanism, pre date maximum
glacial)
Coefficient of normal restitution 0.95 Zhao et al, 2010
Angle of internal friction (°) 30 Manga and Ventura, 2005
Lava flows (rift volcanism, post date maximum
glacial)
El Golfo Basaltic Eruptions
Table 3.1: input parameters for RocFall models.
Piedmont (talus)

20
In these models it is assumed that the falling block will originate from the seeder at the top of
the slope; it will therefore adopt the properties of rift volcanism lava flows. The density of
this rock unit has been calculated for the laboratory experiments and is therefore averaged
and entered as 2870kg/m3
.
The mass of the blocks is highly variable and therefore separate simulations are run to
account for the scenarios with different sized blocks. The shape is defined as a rhombus to
allow representation of the irregularity in block shape.
A block analysis was completed in the field (see Appendix 8) and uses the width, height,
depth and density to calculate the mass of fallen blocks (Figure 3.4).
Figure 3.4: frequency plot to show the masses of fallen boulders in the north west of the island.
Based on the peaks of this frequency model, 3 simulations were performed for Slope 1 at
masses of 200kg, 2000kg and 10000kg. RocFall 5.0 allows further modelling of the endpoints
of these boulders. For Slope 2, simulations were performed for masses of 200kg and 5700kg
(the explicit average of the block analysis).

21
4.1 Geological Interpretation
As a result of the field data collection a geological model for the area was created (Figure
4.1). The collapsed material to the northwest of the map shows the instability more clearly
than the published map (Carracedo, 2001) which shows this region as 4th
generation
piedmont. The spatial distribution of this collapsed material shown in Figure 4.1 is arguably
more extensive than that shown in the published geological model shown in Figure 2.3. The
extent of the collapsed debris shows that the west of Pozo de la Salud lies upon previously
collapsed material. The geological model created for this project also separates the scoria
cones in the region; this is due to the implication on stability as seismic waves would more
severely affect the stability of the friable deposits than the lava flows. Field observations also
showed a large number of dyke intrusions throughout the weathered basaltic lava unit shown
in Figure 2.3. These are genuinely darker grey, younger and much less weathered. The
juxtaposition of the younger dyke intrusions and older lavas should be strongly considered
when assessing overall edifice stability (Reid et al., 2001), however this exceeds the
limitations for this study.
The samples used for laboratory testing originate from the geological unit shown as the
weathered basaltic lava in Figure 4.1. This is because this geology makes up the majority of
the steep cliffs in the study area and therefore illustrates the most representative geology.
4.2 Geomorphological Model
A geomorphological map was created for the town of Sabinosa (Figure 4.2). The steep slopes
above Sabinosa have an average angle of 52°; the geomorphological map of Sabinosa shows
that the angular concave break in slope at the top of the town would mean that Sabinosa
effectively acts as a catchment ditch for any rock falls from the slope above. This is further
disadvantaged due to Sabinosa being built above two scoria cones (see Appendix 9) as this
does not provide a stable foundation to absorb any rock fall impact. Plate 4.1 shows an area
in the slopes above the town that is exposed and lacks vegetation. This suggests recent
movement which will be considered in the hazard model as it implies that the slope may have
recently been active (although not in recorded history).

22
Figure 4.1: geological model of northwestern El Hierro.

23
Figure 4.2: geomorphological model of Sabinosa.

24
Plate 4.1: exposed area with lack of vegetation on steep slopes above Sabinosa.

25
4.3 Combined Hazard Map
Zone HR HF HA HL HP AvH H
1 0.90 0.30 0.35 0.00 0.90 0.49 0.58
2 0.60 0.00 0.40 0.00 0.40 0.28 0.38
3 0.20 0.30 0.35 0.00 0.70 0.31 0.15
4 0.20 0.70 0.30 0.00 1.00 0.44 0.17
5 0.20 0.70 0.30 0.10 0.90 0.44 0.17
6 0.60 0.00 0.45 0.00 0.50 0.31 0.38
7 0.10 0.70 0.30 0.30 0.80 0.44 0.11
8 0.50 0.00 0.40 0.10 0.80 0.36 0.33
9 0.30 0.00 0.38 0.80 0.80 0.46 0.23
10 0.80 0.00 0.40 0.90 0.80 0.58 0.53
11 0.70 0.00 0.40 0.30 0.80 0.44 0.46
12 0.40 0.00 0.35 0.10 0.30 0.23 0.26
Table 4.1: Input values for creating the combined hazard map, where HR = rockfall hazard, HF = flooding
hazard, HA = airfall hazard, HL = lava flow hazard, HP = pyroclastic flow hazard, AvH = average hazard and H
= combined hazard
Table 4.1 shows the values assigned to the individual hazards in the study area. The average
hazard is calculated using a mean average and gives equal weighting to each of the hazards.
This method however does not allow consideration of the frequency and occurrence
probability of the hazard (Kron, 2002). Direct field observations and the conduction of the
FRHI (Appendices 3 and 4) suggest that rockfall presents a much more consistent hazard than
the other input factors, as the probability of occurrence is 100% in any given day across the
entire study area. Lava flow hazard in comparison is very low as the last recorded surface
lava flows on the island were ~158,000 years ago. As a result of this, a hazard equation has
been established to calculate the combined hazard (H) and it is this H value that is used to
create the combined hazard map (Figure 4.3):
𝐻 = 𝑥(𝐻 𝑅) + 𝑦 (
𝐻 𝐹 + 𝐻𝐴 + 𝐻𝐿 + 𝐻 𝑃
4
)
where H = combined hazard, HR = rockfall hazard, HF = flooding hazard, HA = airfall hazard,
HL = lava flow hazard and HP = pyroclastic flow hazard.
This equation uses coefficients x=0.6 and y=0.4 to emphasise the greater impact of rockfall.
By incorporating the coefficients x and y, it allows rock fall to be considered as the main
hazard. The balance between the coefficients can then be altered to produce different hazard
models, depending on the weighting assigned to the impact of rock fall.

26
The hazard map highlights the northwest of the island and the land upon which Sabinosa lies
as the areas with the greatest hazard. The land nearest to the coast has the lowest hazard due
to their increased distance from the base of the slopes and their shallow gradient.
The hazard map however is limited to hazard parameters that could be observed in the field
or that have been numerically modelled in previous literature. As previously mentioned, it
should also be considered that Sabinosa lies on top of a scoria cone, thus providing an
unstable foundation. Field observations showed a crack in the road Ctra. El Pozo which leads
from Sabinosa to Pozo la Salud. Plate 4.2 shows the displacement in the road surface and this
is thought to have a seismogenic origin due to the correlation with the earthquake locations
shown in Figure 2.6.

27
Figure 4.3: combined hazard map.
N

28
Plate 4.2: road cracking witnessed at 27°48’21.3”N, 017°55’00.3”W

29
4.4 Vulnerability Maps
A large amount of Sabinosa has very low economic vulnerability due to the abundance of
uncultivated land (Figure 4.4). The highest economic vulnerability is seen in the areas with
agricultural land due to the reliance of the community on farming as a source of income. The
vulnerability to population depends on fewer factors and is therefore categorised inter fewer
classes (Figure 4.5). The agricultural and residential land are considered to exhibit equal
levels of vulnerability because of the likelihood of human exposure in both regions.
Pozo de la Salud has the both the highest and lowest economic vulnerability (Figures 4.6 and
4.7). The hotel “Balneario Pozo de la Salud” (Appendix 10) presents highest economic
vulnerability as it is one of the only hotels on the island and therefore benefits from a large
proportion of the tourism income of the island. It is clear from Plates 4.3 and 4.4 that
although the hotel is set back approximately 6m from the sea, it lacks elevation and
mitigation. Again the uncultivated land surrounding it has the lowest economic vulnerability.
The main roads in the area represent a high vulnerability to the population as the HI-551 is
the only route out of the village.
Plate 4.3 (left): back view of Hotel Balneario Pozo de la Salud, at 27°45’21”N, 018°06’17.7”W.
Plate 4.4 (right): view from terrace of hotel to sea, to show quality of existing wall.

30
Figure 4.4: vulnerability map to show vulnerability to the economy in Sabinosa

31
Figure 4.5: vulnerability map to show vulnerability to the population in Sabinosa

32
Figure 4.6: vulnerability map to show vulnerability to the economy in Pozo de la Salud.

33
Figure 4.7: vulnerability map to show vulnerability to the population in Pozo de la Salud.

34
4.5 Risk Analysis
Zone Vp Ve V H Cc R
1 0.10 0.30 0.20 0.58 1.00 0.12
2 0.00 0.00 0.00 0.38 1.00 0.00
3 0.40 0.60 0.50 0.15 0.40 0.19
4 1.00 1.00 1.00 0.17 0.60 0.28
5 0.40 0.40 0.40 0.17 1.00 0.07
6 0.00 0.00 0.00 0.38 1.00 0.00
7 0.20 0.30 0.25 0.11 1.00 0.03
8 0.20 0.60 0.40 0.33 0.40 0.33
9 0.20 0.60 0.40 0.23 0.40 0.23
10 0.80 0.80 0.80 0.53 0.60 0.71
11 0.80 0.75 0.78 0.46 0.60 0.59
12 0.40 0.20 0.30 0.26 1.00 0.08
Table 4.2: input values for the risk calculation used to create the risk map, where Vp = vulnerability of
population, Ve = vulnerability of economy, V = average vulnerability, H = combined hazard, Cc = coping
capacity and R = risk.
The risk map (Figure 4.8) uses the same coefficients as those used for the hazard calculation.
Two of the zones in this region have no identified vulnerable features or infrastructure and so
have been identified as regions of zero risk. The coastal area with the hotel presents a very
high risk despite the low hazard; this is due to the extremely high level of vulnerability to
both the economy and the population. The areas of highest hazard in the northwest of the
region do not have a correspondingly high risk due to the lack of vulnerable features.
The coping capacity across the zones is largely affected by the accessibility of the residential
areas; this is based on quality and location of the road network, i.e. main roads allow a greater
accessibility than unpaved tracks and therefore have a higher coping capacity score. Scoring
the coping capacity has also considered the ability of the community to plan for hazards. For
example, Sabinosa has a town hall which allows a disaster strategy to be relayed to the
residents. Pozo de la Salud does not have any public buildings and is therefore likely to not
have any planning strategies governed by the larger nearby area of Frontera. This has been
considered as a lower ability to cope as the resilience of the residents is not independent of
larger communities.
All three zones surrounding Sabinosa are classified as being at high risk; this allows a focus
area for the discussion and design of mitigation features. Sabinosa should be targeted first,
with secondary areas of the hotel by the coast and the area prone to rock fall in the north
west.

35
Figure 4.8: risk map for Sabinosa, Pozo de la Salud and the surrounding area, using the
equation described.
N

36
4.6 Rockfall Modelling
The following section shows the results obtained from RocFall 5.0 according to the outlined
parameters (Table 3.1).
Figure 4.9: RocFall 5.0 model for Slope 1 with above parameters and a mass of 200kg and a simulation of 50
rocks.
Figure 4.10: a graph to show the endpoints of falling blocks for Slope 1 when simulation 50 rocks falling, each
with a mass of 200kg.

37
Figure 4.11: RocFall 5.0 model for Slope 1 with above parameters and a mass of 2000kg and a simulation of 50
rocks.

38
Figure 4.13: RocFall 5.0 model for Slope 1 with above parameters and a mass of 10000kg and a simulation of
50 rocks.

39
Figure 4.15: RocFall 5.0 model for Slope 2 with parameters outlined in Section 3.2.3, a mass of 200kg
and a simulation of 50 rocks

40
Figure 4.16: a graph to show the endpoints of falling blocks for Slope 2 when simulating 50 rocks falling, each
with a mass of 200kg

41
Figure 4.17: a graph showing the profile of Slope 2, overlain with the kinetic energy
from a simulation of 50 rocks falling, each with a mass of 200kg.

42
Figure 4.18: RocFall 5.0 model for Slope 2 with outlined parameters, a mass of 5700kg and a simulation of 50
rocks.

43
Figure 4.19: a graph to show the endpoints of falling blocks for Slope 2 when simulating 50 rocks falling, each

44
Figure 4.20: a graph showing the profile of Slope 2, overlain with the kinetic energy
from a simulation of 50 rocks falling, each with a mass of 5700kg.

45
The results from the rock fall modelling show the slope above Sabinosa and the slope in the
northwest of the island that the Falling Rock Hazard Index surveys showed to be the most
hazardous. It is clear from these models that rockfall presents a large hazard to these areas as
the runout suggested by these models is too high to avoid the infrastructure.
The most conservative model simulates a 200kg mass of basalt, however even this approach
suggests that fallen blocks run out far further than the base of both modelled slopes. Figure
4.18 clearly shows that Sabinosa lies in the catchment area of falling blocks, whilst Figure
4.19 further shows that the peak kinetic energy is reached at the point at which the falling
boulders are most likely to collide with Sabinosa. The geomorphology map presented (Figure
4.2) shows that Sabinosa sits on a downhill slope which means there is no feature to dissipate
the kinetic energy held by falling blocks. In order to protect the town of Sabinosa and the
road in the northwest, it is vital that mitigation measures be considered. This will further be
discussed in Section 6.2.

46
Rock samples were collected for laboratory testing using standard triaxial testing methods,
but using deformation in constant stress mode, as opposed to more traditional constrain strain
methods. The aim of this testing is to explore the long term strength of the rock in the study
area, and to then link this new data to a short term rockfall assessment. This enables
assessment of the long term strength evolution of flank stability in El Hierro.
5.1 Triaxial Testing Methods
5.1.1 Sample Selection
In total, 7 samples of El Hierro Basalt (EHB) and one sample from a spatter deposit (EHS)
were collected. Table 5.1 describes the age of each of the samples according to the unit
boundaries described by Carracedo (2001). The GPS coordinates and elevations can be found
in Figure 3.1.
Sample
ID
Eruption
Age(see
2.3.1)
Description
EHB01 Rift
volcanism,
pre date
max. glacial
Matuyama
Heavily weathered to reddish brown with minor white-
grey alteration products, heavily populated with pyroxene
phenocrysts up to 7mm, often euhedral but some
subhedral, no vesicles visible.
EHB02 Rift
volcanism,
pre date
max. glacial
Partially weathered, pyroxene phenocrysts up to 6mm,
subhedral, no vesicles, cracks visible.
EHB03 El Golfo
Eruption
Bruhnes
Grey, dense, massive, no phenocrysts or vesicles visible to
naked eye, heavily faulted, fresh, only slightly weathered.
EHB04 Rift
volcanism,
pre date
max. glacial
Matuyama
Grey partially weathered blocky lava, massive, partially
weathered, hard, consolidated, some subhedral pyroxene
phenocrysts visible, no vesicles visible to naked eye.

47
EHB05 Rift
volcanism,
post date
max. glacial
Grey, olivine and pyroxene phenocrysts up to 3mm
(subhedral to euhedral), vesicles <1mm but rare, some
cracks and fissures visible parallel and perpendicular to
coring direction.
EHB06 Rift
volcanism,
post date
max. glacial
Matuyama
Grey, porphyritic, olivine phenocrysts up to 4mm, vesicles
up to 6mm, rubbly exterior, and fresh surface appears to
have vesicles concentrated in bands.
EHS01 Rift
volcanism,
pre date
max. glacial
Spatter, consolidated, grey to reddish brown, some flow
texture visible around lava suggests ballistic origin (see
Appendix ?), reddish brown areas vesicular with vesicles
up to 2mm.
Table 5.1: descriptions and ages of all collected samples.
A selection of representative samples was further analysed through petrographic techniques.
This allowed an exploration of the heterogeneities within each sample in order to determine
the most suitable sample for laboratory experimentation. The findings of this analysis are
summarised in Table 5.2 and can be further explored in Appendix 11. Samples were prepared
by Geoff Long at the University of Portsmouth to a standard thickness of 0.03mm.
Sample ID Thin Section Findings Suitable for testing?
EHB01  some evidence of flow texture Yes
EHB02  high oxide content
 some evidence of flow texture
 high population of phenocrysts
No, crystals too large
EHB03  no thin sections cut due to heavy faulting – no
cores possible
No, too heavily
faulted
EHB04  groundmass lacks phenocrysts, aphanitic Yes
EHB05  groundmass with phenocrysts, porphyritic
 evidence of flow texture, trachytoid
No, flow texture
suggests too
heterogeneous
EHB06  no thin sections cut, vesicles up to 7mm, not
suitable for laboratory testing
No, too vesicular
EHS01  very highly vesicular No, spatter not basalt
Table 5.2: a summary of the petrographic analysis.

48
Identification of flow texture (Appendix 12) was of particular importance as this is an
influencing factor of micromechanical behaviour. Petrological analysis revealed EHB04 and
EHB01 to be the most suitable for testing, however EHB01 had pervasive fractures not
visible before coring. This meant 100mm cores were not able to be obtained. As a result of
this analysis and the coring results, the sample EHB04 was chosen for the laboratory testing
as it was deemed the most homogeneous and the most suitable sample.
5.1.2 Sample Preparation
Field samples were prepared using a hollow coring drill mounted in a radial arm drilling
machine. Once cored, samples were ground to a length of 100mm (±2.08mm) and a diameter
of 40mm (±0.07mm) using a Boxford CUD lathe. This maintains the length to diameter ratio
of approximately 2.5:1 (ISRM, 1983). Table 5.3 lists the samples used.
Sample ID Diameter (mm) Length (mm)
EHB04_01 40.03 100.56
EHB04_02 40.04 100.22
EHB04_03 40.02 100.86
EHB04_04 40.01 100.59
EHB04_05 40.07 97.92
EHB04_06 40.03 99.79
Table 5.3: dimensions of cored samples.
Once prepared, samples are placed in a drying oven at 105°C for 24 hours (IRSM, 1979).
This is shown as ‘Dry weight (g)’ in Table 5.4. Samples are then saturated by submerging in
water and placing in a vacuum oven for a minimum of 4 hours to remove all trapped air. With
such a low porosity, this minimum time was generally increased to 24 hours. This provides
the saturated weight. Samples are additionally suspended in a wire basket in order to measure
suspended weight.
Porosity was determined using the standard triple weight method (IRSM, 1979) through the
following procedure:
1. bulk volume = saturated weight – suspended weight
2. pore volume = saturated weight – dry weight
3. porosity = (100 x pore volume) ÷ bulk volume
4. density = dry weight ÷ bulk volume

49
Density
(g/cm3)
2.87
2.86
2.85
2.87
2.85
2.87
Porosity
(%)
2.936
3.191
3.120
3.099
2.909
3.101
Pore
Volume
(cm3)
3.71
4.02
3.93
3.92
3.9
3.58
Bulk
Volume
(cm3)
126.38
125.98
126.82
126.42
124.15
123.05
Suspended
Weight(g)
239.76
238.58
238.16
240.79
237.75
234.23
Saturated
Weight(g)
366.14
364.56
364.98
367.21
361.9
357.28
DryWeight
(g)
362.43
360.54
361.05
363.29
358
353.7
SampleID
EHB04_01
EHB04_02
EHB04_03
EHB04_04
EHB04_05
EHB04_06
Table 5.4: properties of cored samples, including calculated densities and porosities.
Sample porosity is a key parameter in creep experiments, as any large variations in the initial
state of the rock can have a significant impact on the creep behaviour of the sample (Heap et.
al, 2009). For this reason, the samples have been chosen that have porosities within a very
narrow range (+-0.3%).

50
5.1.3 Laboratory Method
The Triaxial Cell (Sanchez Technologies) is capable of precise confining pressure and pore
pressure control up to a temperature of 200°C. This permits the simulation of rock
deformation at representative conditions to ~4km (shallow depths).
The sample is encapsulated inside a rubber jacket (Yoshida et. al, 1998) embedded with
twelve acoustic emission sensors. The sample is orientated within the jacket, and then further
orientated with respect to a fixed coordinate system to ensure the coordinates of the sensor
locations are consistent (Appendix 13). The body of the machine can then be closed to keep
the sensors in contact with the sample, and to allow the vessel to be filled with silicone oil,
which acts as the confining medium.
The triaxial machine is then pressurised. For these experiments, a pore pressure of 8.9MPa
and a confining pressure of 25.3MPa were selected, resulting in an effective pressure of
16.4MPa. This represents a depth of approximately 900m (Appendix 14).
Figure 5.1: a schematic to show the triaxial machine at the University of Portsmouth (Fazio et al., 2014)

51
Creep, also known as static fatigue, is a process whereby crack growth and propagation
allows failure at stresses far below their short term strength (Brantut et. al, 2012). The
theoretical approach to brittle creep begins with Griffith’s theory (Jaeger et. al, 2007) in
which it is explained that all rock samples are ‘imperfect’ and as such, may be regarded as
“un-repeatably imperfect”. This is to say that microcracks are a fundamental part of a
material but are also never reproducible between rock samples.
During deformation, cracks coalesce and propagate, leading to brittle failure via the creation
of a pervasive fault in the rock sample. Crack growth occurs when the stress intensity factor
reaches a critical value that is required for the crack to propagate (Jaeger et al., 2007). Brittle
creep however is the process by which cracks grow more slowly at a stress intensity factor
lower than this critical value, thus termed “sub-critical crack growth” (Brantut et al., 2007).
This mode of deformation allows failure at a lower strength that the expected short term
failure strength of the material.
The dominant process that aids crack propagation is stress corrosion (Brantut et al., 2013).
This describes a process whereby chemically active pore fluid in a saturated rock reacts with
the vulnerable crack tips and aids the growth of the crack tips at a sub-critical value of the
stress intensity factor (Michalske & Freiman, 1982). The speed of the pore fluid-rock
interaction is increased with increased temperatures and therefore it is likely that this would
be the case in a volcanic environment due to the circulation and movement of magma.
A creep experiment is generally conducted by loading the specimen to approximately 85% of
its peak strength: this generally represents a stress lower than that required to generate the
onset of very rapid failure (Baud and Meredith, 1997). As this point can only be determined
retrospectively and the peak strength is known, D’ is used as a parameter that can be
determined in real time. D’ is the point at which the dominant deformation process in the
sample transitions from compression to dilatancy, meaning that all of the existing cracks have
closed and new cracks are being created (Heap et al, 2009). This can be determined by
examining the point of maximum pore volume as this occurs when the sample begins to
dilate (Figure 5.2).
The process is highly sensitive to the original damage state of the sample and its associated
porosity (Baud and Meredith, 1997; Heap et al, 2009; Heap et al, 2011). In order to reduce
the effect of sample variability, a stress-stepping methodology is adopted (Heap et al, 2009).
This allows a larger data capture from one single experiment and so reduces the samples

52
needed and the sample variability. During these stress-stepping experiments, the sample is
first loaded to D’ at a constant strain rate of 1.0x10-5
s-1
. Once D’ is reached, loading is
stopped and the sample left to deform at this constant level of stress. Once the sample
undergoes approximately 0.1mm of axial shortening and the deformation rate is linear, the
stress is stepped up by a small increment (generally 5MPa) and the process repeated. This
method has been verified as having results comparable with conventional creep experiments
but with the advantage of permitting quicker experiments, and is considered reliable (Heap et
al, 2009).
Figure 5.2: a graph to show pore volume against differential stress, highlighting the occurrence of D’ and
therefore the start of the stress stepping method.
A maximum temperature of 180°C has been chosen for this set of experiments. This
limitation is due to the triaxial machine, but is also deemed high enough to affect the creep
rate in water saturated porous rocks (Meredith and Atkinson, 1985). This is because the
sample must be kept in the brittle regime and the temperature is designed to impact the
chemical reaction of stress corrosion in the rock, rather than the mechanical work. This is
also considerably higher than previous studies, which were limited to temperatures of

53
between 25°C to 75°C (Heap et al., 2009), reportedly to avoid the effects of thermal cracking.
However sub-critical crack growth has been studied at temperatures up to 300°C (Meredith
and Atkinson, 1985).
As described above, twelve acoustic emission (AE) sensors record the micro-fracturing of the
rock throughout the experiment (Plate 5.1). These are connected to a “Milne” computer unit
from Applied Seismology Consultants which records when a waveform, exceeding a preset
threshold, is detected by each sensor. A threshold (in mV) is set individually for each of the
sensors depending on the sensitivity of each sensor. They also connect to a “Richter” unit
which records all passive seismic activity throughout the laboratory test (Table 5.5).
Plate 5.1 (left): AE sensors connected to the rubber jacket containing the sample;
Table 5.5 (right): acoustic emission sensor setup with the location of each sensor and which computer channel
each lead corresponds to.
In addition to the ‘passive’ seismic activity that is recorded, active seismic data is also
recorded by the “Milne” unit; this is completed by pulsing each sensor in sequence with a
high voltage pulse. The time of flight for this to traverse the sample from the known sensor
locations are then used to trivially calculate the velocity along that raypath. Such surveys are
taken approximately once a minute for the complete duration of each experiment whereby
each sensor triggers a waveform to be detected by the other eleven sensors. From this, a
velocity model can be created to monitor the velocity of the acoustic emissions throughout
Location Lead Feedthrough
1N 1 1
2NE 2 2
3E 3 3
3W 13 5
4N 4 6
4E 6 4
4S 7 7
4W 14 8
5SW 18 10
6S 15 9
1S 20 11
6N 17 12

54
the experiment, i.e. to assess the change in velocity with the change in strain rate at each
different temperature.
Each survey is composed of twelve triggers (active events) that are then recorded on each of
the eleven remaining sensors. However, many surveys are incomplete. The cumulative
frequency plot (Figure 5.3) shows the survey success rate, which ranges from 15.9% to
99.8%. Only the surveys that have completed correctly are used in the creation of the velocity
model. By running surveys every minute, it ensures that there are enough surveys to account
for those that are incomplete. This velocity model is key, as it is subsequently needed to
locate the passive AE in 3D by using this velocity data with the known sensors location and
the time of the recorded passive AE event during the experiment.
Figure 5.3: a cumulative frequency plot of acoustic emission survey success rate.
Finally, as the AE data are recorded on a separate computer unit to the mechanical (triaxial
data) a small time offset between may exist. This is often in the order of seconds but must be
corrected to permit synchronisation. This is achieved using a MATLAB code which takes a
common signal (deformation or pore pressure) across all data logging computers to
synchronise times. This code can be found in Appendix 15; all MATLAB codes in this

55
project have been executed in MATLAB version 2008a. The code incorporates an additional
smoothing function (one hundred points) to encourage a higher degree of accuracy in the
picking of the common point in both units.
Once the time offset has been corrected, the surveys are imported into a seismic data
processing suite (InSite Seismic Processor, Applied Seismology Consultants). An
autopicking function in the software is used to find the P-wave arrivals in each survey to
reduce operator time, using settings displayed in Table 5.6. By manually checking the first
survey, all other surveys can be cross correlated to the P-wave picks in the master survey.
Low Frequency Cut-off 50KHz
High Frequency Cut-off 1MHz
Sampling Time 0.1 microseconds
P-wave Correction Time 5.1 microseconds
Table 5.6: processing parameters in InSite.
By using a low frequency filter, anomalously low frequency waves are removed from the
surveys to limit the noise in each signal. Certain ray paths are removed from the master
survey to ensure that they are not included in the cross correlation (Appendix 16). This is due
to the geometry of the AE sensor setup, which generates invalid raypaths such as two sensors
on the same side of the sample. This would yield anomalously low velocity data due to mode
conversion (P to S). Once these raypaths are excluded, and a cross correlation process is
complete, the P wave arrival times for all events are exported so that they can be checked for
obvious picking anomalies (Figure 5.4). A MATLAB code then generates a time varying
velocity model (Appendix 17) to ensure accuracy of the located AE hypocentres though time,
and as the deformation of the sample proceeds. This uses a transversely isotropic Simplex
Algorithm (Benson et al., 2007), using a minimum of 5 P-wave arrivals to minimise error in
3D space (Pettit, 1998).
This velocity model is used in application to the experiment data obtained from the “Richter”
unit; these are the passive events that have been caused by the micro fracturing of the rock,
rather than by triggering an active pulse. By using this “Richter” data instead of the
previously described “Milne” data, it allows for a larger number of events to be identified and
located. The locations be manually corrected in InSite; the aim for each located event is to
achieve a time error below 1 microsecond and a location error of below 0.004m.

56
Figure 5.4: a screenshot from InSite software that shows the potential error in P-wave arrival autopicking

57
5.2 Triaxial Results
Many variables exist within this experimental setup which affect strain rate. In order to keep
the project in a realistic timescale, the pore pressure and confining pressure conditions are
kept consistent in this set of experiments. The temperature is the controlled variable and
Table 5.7 shows the experiments undertaken.
Sample ID Temperature (°C) Experiment Type
EHB04_01 25 Conventional strain
EHB04_02 25 Stress Stepping
Table 5.7: experiment list with corresponding sample identifications and temperature conditions, assuming the
set conditions of Pp=8.9MPa and Pc=25.3MPa.
5.2.1 Brittle Creep
The creep rates for each stress step are characterised by the minimum strain rate during
secondary creep (Brantut et al, 2014). The strain rates are calculated in MATLAB (Appendix
18) that allows the user to pick each end point of the stress step. The data is first smoothed
(one in every ten points) to remove some of the scatter in the data. The strain rate is only
calculated from the second 50% of the data in each stress step to ensure that the segment
being assessed is linear.
Figure 5.5: a MATLAB plot to show the effect of data smoothing.

58
Figure 5.6: percentage of strength against strain rate and the effect of temperature in El Hierro basalt (density ~
2.86g/cm3
, porosity ~ 3%)
Results of the experiments conducted to determine the effect of elevated temperatures on the
creep process are shown in Figure 5.6. An anomaly in the experiment at 25°C caused the
loading to continue until approximately 95% of strength; these data are included but
neglected for the purposes of analysis. This suggests that the results for this experiment are
likely to be inaccurate, however the combination of the results at 25°C and 90°C suggest an
inverse relationship between temperature and strain rate. Interestingly, this study shows that
elevated temperatures lead to lower creep rates. Recent work has been carried out on creep at
temperature in Darley Dale sandstone (Heap et al., 2009) and in contrast to this work, showed
no temperature dependence in strain rate. This study shows less divergence at higher

59
temperatures, and a fluctuation at lower percentages of strength. The raw data for these
experiments can be found in Appendix 19.
Similar experiments were conducted on other Canary Island samples (Appendix 20) at the
Rock Mechanics Laboratory, University of Portsmouth. These samples were collected by
Sebastian Wiesmaier at coordinates 28°16'21.79"N, 16°43'31.96”W and are phonolites from
the Montaña Reventada composite lava flow in Tenerife (Wiesmaier et al., 2011).
These experiments again showed an inverse relationship between temperature and strain rate,
with the experiment at 25°C exhibiting far higher strain rates. This suggests the results from
the creep experiments on El Hierro basalt are not anomalous and that further testing is
required to constrain a more accurate and more detailed temperature/strain rate relationship.
In addition to calculating the strain rates, these experiments allow us to calculate the failure
strength of the samples, expressed as a percentage of the material strength as calculated by
the first conventional stress/strain experiment. This experiment calculated the strength of El
Hierro basalt as 440.8MPa and this is used as a control experiment to compare with the
subsequent testing. Figure 5.7 shows the decrease in strength with temperature, relative to a
material strength of 441MPa.
Figure 5.7: the failure stresses of El Hierro basalt according to temperature, expressed as % of material strength.

60
This shows a decrease in strength with increasing temperature; this could have large impacts
on overall flank stability in the Canary Islands as circulating magma in a volcanic setting is
likely to lead to elevated temperatures.
5.2.2 Acoustic Emissions
The final propagated fault is shown in Figure 5.8 by overlaying seismic events harvested
from the continuous AE record on a cylinder. These come from the last ten minutes of the
experiment, with the majority of the fault propagation occurring in the final 60 seconds. This
study shows no significant difference between the acoustic emission outputs in a creep study
and those in a conventional strain experiment, and additionally acknowledges no pattern in
acoustic emissions across the different temperatures. The impact of the velocity model is
explored using the same P-wave picks, to assess whether the fault is more accurately mapped
by the AE record when the P-wave velocity in the velocity model is altered.
Figure 5.8: continuous AE record of the last ten minutes of the experiment using sample EHB04_04 at 140°C
with Pc = 25.3MPa and Pp = 8.9MPa. AE event locations harvested from continuous record. P-wave velocity
adapted from original velocity model as follows: (a) 80%; (b) 90%; (c) 100%; (d) 110%; (e) 120%. All views
displayed with azimuth = 105° and symbol size represents relative event magnitude.
The velocity model has been altered using the premise that the process of brittle creep allows
the development of a dense crack network which is likely to slow down P-wave velocity but
this reduction is not measured by the velocity model. As a result of this, the velocity model
calculated using the “Milne” data could overestimate the P-wave velocity. The pervasive
crack network developed in the rock is likely to encourage different degrees of water
saturation within the cracks; this leads to differential velocities within the sample (Crampin,

61
1984). It is also likely that the angle at which the raypath hits the piezoelectric sensors may
affect the velocities of each raypath (Petitt, 1998) and this velocity variation is also not
accounted for within the velocity model.
Plate 5.2: failed sample of EHB04_04 (140°C, Pp=8.9MPa and Pc = 25.3MPa), orientated west, with fracture
correlating to AE results shown in Figure 5.8.
These results show that the velocity model that enabled the most accurate AE events was the
model calculated as 80% of the originally generated velocity model. This demonstrates that
the P-wave velocity is likely to be lower than forecasted by the velocity model. The average
P-wave velocity in basalt is ~3.2kms-1
(Heap et al., 2009a) compared to a velocity of 1.4kms-
1
(Jaeger et al., 2007) in water. As the crack network increases, the saturated pore spaces also
increase; this is likely to lead to a decrease in P-wave velocity that is not accurately modelled
by the velocity model. This effect of the velocity model on the AE location accuracy is not
modelled with temperature increase as it is reported that the decrease in velocity does not
exceed 5% over a temperature change of 100°C (Christensen, 1982; Guéguen and
Palciauskas, 1992).

62
6.0 Discussion
Volcanic ocean islands are frequently subject to large scale gravitational collapses and more
localised rockfalls. This project has examined the potential impact of these hazards on the
communities living in the escarpment of the El Golfo collapse. This was mainly undertaken
through an assessment of the primary rock unit. Laboratory experimentation showed
strengths of up to 440MPa, with densities of ~2.86g/cm3 and porosities of ~3.0%% were
measured in samples of El Hierro basalt (Table 5.4). This suggests that the rock is likely to be
strong enough to withstand a higher magnitude of seismic event, and a higher volume of
smaller swarm events than anticipated in the original risk model, before it experiences
cracking or ultimate failure. It is therefore likely that the hazard has therefore been overstated
in the original risk model presented in this study.
The creep experiments revealed an inverse relationship between strain rate and temperature,
however this was not entirely linear. This opposes the findings by Heap et al. (2009) and
suggests that magma circulation in a volcanic environment would reduce the strain rate in El
Hierro basalt. This confirms the concept that the risk model is likely to have overestimated
the hazard. The process of stress propagation at crack tips suggests that elevated temperatures
would increase the cracking, and therefore increase the strain rate. This study does not agree
with this concept. It is hypothesised from the results of this study that there could be a critical
temperature whereby the strain rate increases, therefore removing an obvious linear trend.
The crack density progression in a conventional stress experiment has been previously
modelled (Benson et al., 2006); it would be of use in this study to model the crack density
throughout the creep experiments to determine whether this is impacting the relationship
between strain and temperature.
The laboratory acoustic emission data (Figure 6.1) shows a medium to long period of
precursory seismicity, which was mirrored in the real life scenario of the 2011/12 eruption by
the swarm of precursory events. Considering the scaling factor for laboratory simulations, it
shows that an edifice failure in El Hierro is likely to be preceded by seismicity, unlike the
more recent eruption in Chile that lacked seismic precursors (Mortillaro, 2015). This is a
useful tool in the forecasting of eruptions of edifice failure and is hopefully able to aid
governmental organisations in their mitigation of possible events. The likelihood of swarms
however suggests that any volcanic activity could trigger localised rockfalls on the island.

63
However, the flanks of El Hierro may be losing stability through long term deformation, and
therefore could collapse without a seismic event triggering this gravitational process.
This study determined that the basalt tested from El Hierro has a relatively high density and
strength. Therefore it is probable that the risk model created in Section 4.5 has overestimated
the hazard and so can be reassessed using the results from the laboratory experiments.
Figure 6.1: laboratory seismicity in the lead up to failure.
This study emphasises the importance of assessing the large scale geodynamic setting on the
destabilisation of a volcanic flank, particularly through the consideration of geophysical and
geomechanical properties. The geological model also shows a complex dyke system
throughout the intermediate aged unit on the island. The dyke system is a younger, more
competent unit and therefore should be considered in the overall stability assessment of the
island. Forced injection of moderate-scale dykes is thought to be a direct cause of flank
instability due to the increase in pressures on the décollement (Elsworth and Voight, 1996;
Elsworth and Day, 1999); this stability loss occurs with dykes as small as 1m across and
these are features that are frequently observed on El Hierro. It is possible to argue that the
dyke complex could increase overall edifice stability due to being a harder, more
consolidated rock. However field observations showed that most of the dykes had two
perpendicular fault sets, therefore confirming that their overall impact is to decrease edifice
stability.
There are several scoria cones in the El Golfo embayment which provide the foundations for
small towns, for example Sabinosa. Therefore in addition to the primary hazard of flank
collapse, secondary rockfalls should be considered in relation to the scoria foundation on to
which the rocks are likely to fall. The lack of consolidation in the scoria deposits could lead

64
to a greater level of instability, any rockfalls could potentially lead to the destabilisation and
collapse of the scoria cones in the embayment. Alternatively, as scoria is more able to absorb
the energy from a rockfall than a solid rock unit, the presence of scoria cones could decrease
the overall impact of localised rockfalls. These two hypotheses have different outcomes
depending on the overall edifice stability and should be examined further to assess whether a
smaller rockfall at a higher elevation could lead to a larger scale collapse.
The El Golfo collapse has been categorised as a debris flow (Urgeles et al., 1997; Masson et
al., 2002) and is therefore said to be strongly related to debris avalanche events, which could
correspond to more localised rockfall events. However, debris flows are generally categorised
as having relatively narrow headwall regions (Masson et al., 2002) casting some doubt as to
the characterisation of the El Golfo landslide. Field observations showed evidence (Appendix
22) of very small arcuate head scars in comparison to the size of the resultant collapses.
These resultant flows have caused gullies on the scale of the whole El Golfo embayment,
clearly demonstrating that the size of the impact area does not necessarily correlate to that of
the source area, because the scar suggests a much smaller movement than the resultant gully.
There is strong evidence from studies in Tenerife that vertical collapses can trigger further
lateral flank collapses due to the stress concentrations caused by the initial failure (Martí et
al., 1997). This suggests that small arcuate head scars such as that demonstrated in Appendix
22 could still result in flank scale movement.
Slumps are a category of landslide (Varnes, 1958) that occur when a coherent mass of
material moves a short distance down a slope. Slumps are more likely to affect the edifice on
a larger scale and be related to stress accumulations at the décollement (Moore et al., 1994).
Large scale slump events have been noted elsewhere on El Hierro in the form of the El Julan
and Las Playas landslides; this could suggest that the mechanical stresses accumulated via the
process of brittle creep contribute to the stress mechanisms required to trigger a flank
collapse. These are generally also bounded by rifts, with the edifice-substrate interface at
their base (McGuire, 1996); this is also seen in the El Golfo depression as a result of the
island’s trilobate rift system. From this, it is likely that both rockfalls and brittle creep are
among the direct precursors of flank instability. The experiments conducted in this study
therefore agree with the hypothesis that a slump landslide can creep continuously and no
specific triggers are required to initiate the landsliding (McGuire, 1996; Rasà et al., 1996;
Masson et al., 2002).

65
7.0 Conclusions
The primary aim of this research was to create a multidisciplinary risk model through the
combination of field mapping, rockfall modelling and laboratory exploration to enable an
assessment of the impact of various hazards on the communities of Sabinosa and Pozo de la
Salud. The following conclusions have been drawn from this investigation:
 The field mapping shows Pozo de la Salud to be the area with the highest economic
vulnerability and Sabinosa to be the area with the highest population vulnerability,
with no mitigation strategies currently in place.
 The greatest rockfall hazard is in the west of the island and study area, with
infrastructure constantly being threatened by small scale movements.
 The laboratory experiments have shown the basalt collected from El Hierro to have a
high density of 2.87g/cm3
and a particularly high failure strength of ~440MPa.
 Conversely to previous studies, this study has identified an inverse relationship
between temperature and strain rate during constant stress deformation experiments.
Elevated temperatures are also identified as causing a linear decrease in strength
(failure stress) of the rock tested.
The combination of these findings suggests that elevated temperatures caused through
magmatic circulation in a volcanic edifice are likely to lead to an overall decrease in rock
strength. When this is considered in conjunction with the rock detachment triggered by
seismic activity, the general stability of the El Hierro edifice is severely compromised.
Although hazards exist on the island additionally to rockfall, this study has made it clear that
rock stability is the key hazard. This ultimately leads to the consideration of another large
flank collapse in the island’s future. This study deems this eventuality to be unlikely in its
frequency compared to smaller scale rockfalls, but catastrophic in the case that it occurs.
It is clear that if a flank collapse or large rockfall were triggered, the event would be
catastrophic for both the economy and the population of the island. This report discusses the
balance between the higher frequency of the smaller hazards with lower impacts, and the
lower frequency of a catastrophic flank collapse. The Spanish authorities should be
considering both in their overall risk assessment for the island.

66
7.1 Limitations
Although the simulations in RocFall try to model the scenarios most accurate to real life,
there are still limitations to these models. The angles of restitution and the angles of internal
friction are taken from similar materials; to more accurately determine these angles it would
be better to experiment on samples from each of the geological units. The use of field block
analysis aims to keep the mass measurements as accurate as possible, however it is not clear
from field observations whether these blocks are from the large El Golfo collapse or from a
later and smaller landslide. The velocity inputs for the model are also largely theoretical, and
an individual seeder is unlikely as more rocks are likely to detach as they collide with already
falling boulders. For this reason it could be suggested that these models offer a conservative
scenario. The models may initially seem extreme, however the study conducted by Dupont
(2012) confirms that the region surrounding Sabinosa is the area on the island most
susceptible to rock detachment and most in need of mitigation measures. Due to the location
of Sabinosa and Pozo de la Salud at the embayment extremities, the area experiences the
lowest vegetation levels and highest slope inclinations across the island. It is therefores
logical that these areas are so vulnerable to rockfall.
Limitations must also be considered in respect to the laboratory experiments. There are few
other geomechanical characterisations of basalt from this area, although there is a suggestion
of a maximum strength of 336MPa (Rodríguez-Losada, 2009; Oglialoro, 2013). This
contrasts with a maximum strength of 440.8MPa found in this study which suggests a
potential overestimation. With additional time and sample availability, it would be necessary
to test several samples using a conventional stress/strain experiment to obtain a more accurate
strength measurement. However all failure strengths after the creep process are explored as
percentages of this initial strength and so it may be argued that the same value is used in the
analysis of all experiments, thus reducing the margin of error.
Laboratory experiments generally operate at a strain rate of 10-5
s-1
, whereas tectonic strain
rates are expected in the region of 10-15
s-1
to 10-18
s-1
(White, 1994; Petford et al., 2000).
Although the lowest strain rate achieved in the experiments reported in this project are still
high in comparison, at 10-9
s-1
, they are still closer to the realistic (tectonic) values and
therefore help bridge the knowledge gap between experimental and recorded data. There have
been further creep studies conducted in a deep sea environment to allow ultra-long-term
experiments (Boon et al., 2010). By submerging the experimental apparatus at 2000m depth,

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