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1. A robust satellite technique for monitoring seismically active areas:
The case of Bhuj–Gujarat earthquake
N. Genzano a
, C. Aliano a
, C. Filizzola b,1
, N. Pergola b,1
, V. Tramutoli a,⁎
a
University of Basilicata, Potenza, Italy
b
Institute of Methodology for Environmental Analysis, Tito Scalo (PZ), Italy
Received 14 January 2006; received in revised form 7 April 2006; accepted 10 April 2006
Available online 1 December 2006
Abstract
A robust satellite data analysis technique (RAT) has been recently proposed as a suitable tool for satellite TIR surveys in
seismically active regions and already successfully tested in different cases of earthquakes (both high and medium–low
magnitudes).
In this paper, the efficiency and the potentialities of the RAT technique have been tested even when it is applied to a wide area
with extremely variable topography, land coverage and climatic characteristics (the whole Indian subcontinent). Bhuj–Gujarat's
earthquake (occurred on 26th January 2001, MS ∼7.9) has been considered as a test case in the validation phase, while a relatively
unperturbed period (no earthquakes with MS ≥5, in the same region and in the same period) has been analyzed for confutation
purposes. To this aim, 6 years of Meteosat-5 TIR observations have been processed for the characterization of the TIR signal
behaviour at each specific observation time and location.
The anomalous TIR values, detected by RAT, have been evaluated in terms of time–space persistence in order to establish the
existence of actually significant anomalous transients. The results indicate that the studied area was affected by significant positive
thermal anomalies which were identified, at different intensity levels, not far from the Gujarat coast (since 15th January, but with a
clearer evidence on 22nd January) and near the epicentral area (mainly on 21st January). On 25th January (1 day before Gujarat's
earthquake) significant TIR anomalies appear on the Northern Indian subcontinent, showing a remarkable coincidence with the
principal tectonic lineaments of the region (thrust Himalayan boundary).
On the other hand, the results of the confutation analysis indicate that no meaningful TIR anomalies appear in the absence of
seismic events with MS ≥5.
© 2006 Elsevier B.V. All rights reserved.
Keywords: Earthquake; Thermal anomalies; Meteosat; Bhuj–Gujarat earthquake
1. Introduction
Several studies have been performed, in the past
years, reporting the appearance of space–time anoma-
lies in TIR satellite imagery,2
from weeks to days,
before severe earthquakes. Large-scale (up to several
hundred kilometres) increases (from 3 to 6 K) of TIR
Tectonophysics 431 (2007) 197–210
www.elsevier.com/locate/tecto
⁎ Corresponding author. Tel./fax: +39 0971205205.
E-mail address: valerio.tramutoli@unibas.it (V. Tramutoli).
1
Fax: +39 0971427271.
2
Earth's thermally emitted radiation measured from satellite in the
Thermal Infrared (8–14 μm) spectral range is usually referred to as TIR
signal and given in units of Brightness Temperature (BT) measured in
Kelvin degrees.
0040-1951/$ - see front matter © 2006 Elsevier B.V. All rights reserved.
doi:10.1016/j.tecto.2006.04.024

2. signal have been reported in some relation with
earthquakes which occurred in China (Qiang et al.,
1991; Qiang and Dian, 1992; Tronin, 1996; Qiang
et al., 1997; Tronin, 2000), Japan (Tronin et al., 2002),
Africa (Saraf and Choudhury, 2005a) and India
(Ouzounov and Freund, 2004; Saraf and Choudhury,
2005a).
Among the possible origins of such a connection, the
increase of green-house gas (such as CO2, CH4, etc.)
emission rates, the changes of ground water regime
(e.g. Hamza, 2001) and the rise of convective heat flux
have been chiefly invoked (Qiang et al., 1991; Tronin,
2000).
Despite the large number of such observations
made, since the pioneering work of Gorny et al.
(1988), the claimed connection of TIR emission with
seismic activity has been for a long time considered
with some caution by the scientific community
mainly for the insufficiency of the validation data-
set and the scarce attention paid by those authors to
the possibility that other causes (e.g. meteorological)
than seismic activity could be responsible for the
observed TIR signal fluctuations. A new, robust and
exportable, satellite data analysis approach named
RAT has been proposed by Tramutoli (1998) and
successfully applied to all major natural and environ-
mental hazards (i.e. volcanic risk, Pergola et al., 2001;
Tramutoli et al., 2001c; Di Bello et al., 2004; Pergola
et al., 2004a,b; Bonfiglio et al., 2005; hydrological
risk, Tramutoli et al., 2001a; Lacava et al., 2005;
forest fire detection, Cuomo et al., 2001). Its appli-
cation to the monitoring of seismically active areas
has permitted to overcome the major weaknesses of
previous studies (in particular the absence of a con-
vincing definition of TIR “anomalies” and of suitable
methods for discriminating them from normally
occurring TIR signal fluctuations) demonstrating its
effectiveness over areas affected both by high magni-
tude (Mb N5.5) earthquakes (Irpinia, 23rd November
1980, Tramutoli et al., 2001b; Di Bello et al., 2004;
Athens, 7th September 1999, Filizzola et al., 2004;
Izmit, 17th August 1999, Tramutoli et al., 2005) and
medium–low magnitude (4bMb b5.5) seismic events
(Greece and Turkey, Corrado et al., 2005).
Unlike preceding methods, the RAT technique is
based on a preliminary multi-temporal analysis on
several years (from 4 to 10 depending on the availability
of homogeneous historical data-sets) of satellite TIR
records, which is devoted to characterize the TIR signal
(in terms of its expected value and variation range) for
each pixel of the satellite image to be processed. On this
basis, anomalous TIR patterns are identified by using
the RETIRA (Robust Estimator of TIR Anomalies,
Tramutoli et al., 2005) index:
DT r; tV
ð Þu tDTðr; tV
Þ−lDT ðrÞb
rDT ðrÞ
ð1Þ
where:
– r≡(x,y) represents geographic coordinates of the
image pixel centre;
– t′ is the time of acquisition of the satellite image at
hand;
– ΔT(r, t′) is the difference between the current (t=t′)
TIR signal value T(r, t′) at location r and its spatial
average T(t′), computed in place on the image at hand
considering cloud-free pixels only, all belonging to
the same, land or sea, class in the investigated area
(i.e. T(t′) is computed considering only sea pixels if r
is located on the sea and computed considering only
land pixels if r is located over the land). Note that the
choice of the differential variable ΔT(t′), instead of T
(t′), is expected to reduce possible contributions (e.g.
occasional warming) due to day-to-day and/or year-
to-year climatologic changes and/or season time-
drifts;
– μΔT (r) and σΔT (r) are the time average and standard
deviation values of ΔT(r, t′) computed on a ho-
mogeneous data-set {T⁎} of cloud-free satellite re-
cords collected at location r in the same time-slot
(hour of the day) and period of the year of the image at
hand (t′∈{T⁎}).
Images μΔT (r) and σΔT(r) describe, for each location
r (i.e. for each image pixel) and observation time t, the
normal behaviour of the signal and its range of var-
iability in observational conditions as similar as possible
to the ones of the image at hand. Hereafter we will refer
to them as reference images as they represent in (1) the
terms of comparison used to evaluate the significance of
observed ΔT(r, t′) space–time variations.
The local excess ΔT(r, t) −μΔT(r) represents the
Signal (S) to be investigated for its possible relation with
seismic activity. It is always evaluated by comparison
with the corresponding natural/observational Noise (N),
represented by σΔT(r,) which describes the overall (local)
variability of S including all (natural and observational,
known and unknown) sources of its variability as
historically observed at the same site in similar observa-
tional conditions. In this way, the relative importance of
the measured TIR signal (or the intensity of anomalous
TIR transients) can naturally be evaluated in terms of S/N
ratio by the RETIRA index ⊗ΔT(r, t). Moreover, the
198 N. Genzano et al. / Tectonophysics 431 (2007) 197–210

3. larger is σΔT(r), the lower will be ⊗ΔT(r, t), so that the
RETIRA index is intrinsically protected against false
alarm proliferation (robustness).
The RETIRA index is based on the comparison
among homogeneous measurements which are per-
formed at the same ground pixel in the same
observational period (month) and daytime (hour of the
day). Mainly for this reason, it is expected to be
independent from (or to strongly reduce the effects of)
known sources of natural/observational noise (Tramu-
toli et al., 2005). For instance, the independence of the
RETIRA index from the orography and vegetation
coverage has been demonstrated in Tramutoli et al.
(2001b). Moreover abrupt (but spatially extensive)
temporal variations of surface temperature, related to
meteorological and/or climatological factors, are con-
trolled by the use of the differential variable ΔT(r, t)
instead of T(r, t) (see before).
Being based only on satellite data at hand, the RAT
approach turns out to be not only applicable to different
instrumental packages, but also completely exportable
over every geographical region. Moreover, it is
intrinsically robust (in a statistical sense, i.e. according
to Cressie's, 1993 and Menke's, 1984, definitions) and
gives the possibility of evaluating the meaningfulness
of observed TIR transients in terms of signal/noise ratio
(S/N) by means of the RETIRA index.
2. The case of Bhuj–Gujarat's earthquake
In this paper, the sensitivity of the RETIRA index has
been tested in the case of Gujarat's earthquake
(MS ∼7.9) which occurred on 26th January 2001, hav-
ing its epicentre at 23.36°N and 70.34°E (USGS). It was
an intraplate earthquake which has sadly gone to history
as one of the most deadly seismic events that hit the
Indian subcontinent, causing the death of about 20000
people and more than 160000 injured. It has been
studied by many authors (Dey and Singh, 2003;
Ouzounov and Freund, 2004; Dey et al., 2004; Okada
et al., 2004; Cervone et al., 2005; Saraf and Choudhury,
2005b) who claim a connection between variations of
several ground and atmospheric parameters and the
occurrence of the seismic event itself.
In this work, space–time TIR signal transients
have been analyzed and interpreted, both in the
presence (validation) and in the absence of (confuta-
tion) seismic events, glancing at possible space–time
relationships. To this aim, two homogeneous data-
sets, including all Meteosat-5 TIR images acquired
Fig. 1. Reference fields (time average μΔT (r) and standard deviation σΔT (r)) for the investigated area at 24:00 UTC for January and February
computed over the years 1999–2004.
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N. Genzano et al. / Tectonophysics 431 (2007) 197–210

4. from 1999 to 2004 in the same time of the day (24:00
UTC) during the months of January and February,
have been built. For each month (and corresponding
data-set) temporal average μΔT (r) and standard
deviation σΔT (r) have been computed to construct
two reference images that are shown in Fig. 1.
On this basis RETIRA index has been computed for
all the Meteosat imagery (from 1994 to 2004) in order to
perform the validation/confutation analyses.
For validation purposes, the months of January and
February 2001 (the year of Gujarat's earthquake) have
been considered, while, in the confutation phase, the
analysis has been performed considering the months of
January and February 2003: the only “unperturbed” (i.e.
without earthquakes with M≥5) period in the consid-
ered data set.
3. Validation
Fig. 2 shows the results of the RETIRA index (⊗ΔT
(r, t′)) computation since 5th January up to 15th February
2001. Pixels with ⊗ΔT(r, t′)≥ 3 (i.e. ΔT(r, t′)−μΔT(r)
excess greater than 3σΔT(r)) are depicted in red and
hereafter, for sake of simplicity, we will refer to them as
“anomalous”. Clouds have been detected by using the
OCA approach (Cuomo et al., 2004) and are represented
as black pixels.
Glancing at the results on the whole (Fig. 2A–E),
five main sequences have been considered:
a) 5th–10th January (Fig. 2A): no anomalous pixel is
present.
b) 11th–20th January (Fig. 2B): anomalous pixels affect
the central region of India with a variable spatial
distribution.
c) 21st–26th January (Fig. 2C): TIR anomalies are
clearly visible over the northern Arabian Sea, close to
the Gujarat coast, reaching their maximum extension
on 22nd January and vanishing in the following days.
Moreover, significant TIR anomalies appear in the
northern area of the Indian subcontinent.
d) 27th–31st January (Fig. 2D): anomalous pixels affect
the eastern coast of India on 30th and 31st January.
e) 1st February–onwards (Fig. 2E): anomalous pixels
are identified on 1st February over the Bengala Gulf
and in the northern part of the Indian subcontinent.
Moreover, few isolated anomalous pixels can be
glimpsed on other images (for example on 7th, 8th,
9th February, indicated by red arrows in figure).
All the above mentioned TIR anomalies have been
submitted to a space–time persistence analysis in order
to establish the existence of actually significant
anomalous space–time transients. In fact, as already
discussed in previous works (see, for example, Filizzola
et al., 2004; Tramutoli et al., 2005), the RETIRA index,
being based on time averaged quantities, is intrinsically
not protected from the abrupt occurrence of signal
outliers related to particular natural (e.g. local warming
due to night-time cloud passages) or observational (e.g.
Fig. 2. Validation: results of the RETIRA index computation on the
investigated area before and after the Gujarat's earthquake (January
26, 2001 MS ∼7.9). Pixels with ⊗ΔT (r, t′)≥3 are depicted in red.
Residual clouds are black coloured. A) Until 10th January no
anomalous pixels are present. B) Since 11th January up to 19th
January anomalous pixels affect the central region of India, with a
variable spatial distribution. C) TIR anomalies are clearly detected
close to the Gujarat coast and in the northern area of the Indian
subcontinent. The green circle, on 26th January, indicates the
epicentral area of the Gujarat's earthquake. D) Anomalous pixels
affect the eastern coast of India on 30th and 31st January. E) TIR
anomalies are detected over the Bengal Gulf and in the northern part of
the Indian subcontinent on 1st February. Red arrows indicate isolated
less evident anomalous pixels which affect the area of study.
200 N. Genzano et al. / Tectonophysics 431 (2007) 197–210

5. errors in image navigation/co-location process) condi-
tions (examples of such known effects are given, for
instance, in Filizzola et al., 2004). But such conditions
have to be effectively sporadic in time (otherwise they
will be controlled and vanished by the time averaging
process used to build reference fields for the RETIRA
index) and relatively isolated in the space domain
(otherwise they will be controlled and vanished by the
spatial average process used to build the ΔT(r,t)=T(r,t) −
T(t) variable, starting from T(r,t) observations, Tramutoli
et al., 2005).
A spatial extension and persistence in time are then
further requirements to be satisfied (together with
intensity) in order to preliminarily identify significant
Fig. 2 (continued ).
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N. Genzano et al. / Tectonophysics 431 (2007) 197–210

6. TIR anomalies. This is, for instance, the case of the
anomalous pixels which are detected in the eastern coast
of India on 30th–31st January and 8th February (a local
warming effect due to the night time passage of a cloudy
system could be responsible for them) and along the
western coast of India on 7th February (a residual
navigation/co-location error is the cause of the effect).
On the basis of this analysis, three meaningful
sequences with time-persistent ⊗(r, t′)≥3 patterns have
been identified:
1. Some TIR anomalies appear over the northern
Arabian Sea, close to the Gujarat coast, on 15th
January and decrease their spatial extension on 16th
January. TIR anomalous signals reappear in the same
area on 21st January, reaching their maximum
extension on 22nd January and vanishing in the
following days.
2. Since 25th January (1 day before Gujarat's earth-
quake), TIR anomalies appear in the northern area of
the Indian subcontinent. In the following days, these
anomalous pixels slowly weaken until they disappear
on 30th January. The anomalous pixels are visibly
located near the Himalayan boundary, outlining the
tectonic limit (thrust boundary in Fig. 3) that marks
the overlapping of the Eurasian plate over the Indian
plate.
3. Thermal anomalies are visible over a period of 10
days (11th–21st January) in the central area of India.
A visual comparison between the spatial distribution
of the observed TIR anomalies (e.g. 15th January)
and a structural map of the area (Fig. 3) shows that
the anomalies seem to follow the main tectonic
structures (reported in Fig. 3). Nevertheless, it should
be noted that the time-persistent pattern of anomalous
values is detected under a condition of persistent
Fig. 2 (continued ).
202 N. Genzano et al. / Tectonophysics 431 (2007) 197–210

7. Fig. 2 (continued ).
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N. Genzano et al. / Tectonophysics 431 (2007) 197–210

8. cloud cover. For this reason, it should not be
excluded that meteorological factors are responsible
for the presence of the observed TIR anomalies.
If we focus our attention only on the two sequences
(1 and 2) which respond to a time–space persistence
criterion and, at the same time, appear to be free from
any possible meteorological influence, interesting
details can be revealed also considering lower intensity
anomalies (pixels characterized by ⊗ΔT(r, t′)≥2).
Concerning the first sequence, Fig. 4 shows that the
spatial structure and the time evolution of the observed
anomalous ⊗(r, t′)≥3 patterns on the sea can be better
appreciated by considering lower intensity anomalies.
Moreover, a closer view of the Gujarat area shows that
anomalous values at different levels of intensity are
present also on the land: epicentral area appears to be
interested by anomalous values mainly on 21st January
(5 days before the seismic event) and 28th January
(2 days after the seismic event).
In Fig. 5, a zoom is reported for the second sequence
from 25th to 28th January, i.e. straddling the day of
Gujarat's earthquake. Drafting the thrust boundary over
such images, a clearer correspondence is evident between
anomalous pixels and the tectonic boundary, confirming
and reinforcing the impressions mentioned above. In fact,
Fig. 3. Tectonic map of the Indian subcontinent (ASC, 2004).
204 N. Genzano et al. / Tectonophysics 431 (2007) 197–210

9. Fig. 4. Validation: a closer view of the epicentre zone.
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N. Genzano et al. / Tectonophysics 431 (2007) 197–210

10. Fig. 5. Validation: lower intensity TIR anomalies straddling the day of Gujarat's earthquake.
206 N. Genzano et al. / Tectonophysics 431 (2007) 197–210

11. near the Himalayan border, lower intensity TIR anomalies
better describe the structure which is already outlined by
pixels with ⊗ΔT(r, t′)≥3. They appear on 25th January
(1 day before Gujarat's earthquake), reach their maximum
extension during the day of the main shock (26th of
January) and gradually disappear in the following days.
Fig. 6. Confutation: results of the RETIRA index computation over the investigated area for the relatively unperturbed year 2003 (no earthquake with
M≥5 occurred). Like in Fig. 2, clouds are depicted in black and pixels with ⊗ΔT (r, t′)≥3 are red. A) The red bordered images are the only affected
by anomalous pixels. B) A closer view of the red bordered images.
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N. Genzano et al. / Tectonophysics 431 (2007) 197–210

12. Fig. 6 (continued ).
208 N. Genzano et al. / Tectonophysics 431 (2007) 197–210

13. 4. Confutation
The confutation step has been performed by consider-
ing January and February 2003, in order to verify the
absence of TIR anomalies in seismically “unperturbed”
periods. The selection of “2003” for confutation purposes
has been done consulting the IRIS (2004) seismic cata-
logue: within the range 1999–2004 (corresponding to the
processed data-set): no seismic event with magnitude
greater than (or equal to) 5 is reported over the investigated
area during the months of January and February 2003.
The same analysis as performed in the validation
phase has been used, in the confutation step, to identify
possible meaningful TIR anomalous values.
Even a quick comparison between images in
validation (Fig. 2A–E) and confutation (Fig 6A) phases
allows us to realize that a substantial difference exists in
the occurrence of TIR anomalies. In fact, it is evident,
from the results shown in Fig. 6A, that only isolated (on
11th, 17th, 21st January and 2nd, 8th February) and not
time persistent (disappearing just in 1 day) anomalous
pixels are detected. Fig. 6B shows a closer view of these
sporadic anomalies.
5. Conclusions
RAT had already shown to give interesting results in
different test cases over the Anatolian region (Corrado
et al., 2005; Tramutoli et al., 2005), the Italian Peninsula
(Tramutoli et al., 2001a; Di Bello et al., 2004) and the
Hellenic region (Filizzola et al., 2004). The main
purpose of this paper was to verify the efficiency and
the potentialities of this technique in identifying thermal
anomalies (possibly in connection with seismic activity)
even over a very large area (about 4000×4000 km2
)
characterized by extremely variable topography, land
coverage, climatic regime and geo-tectonic setting. The
relatively limited appearance of anomalous pixels, both
in validation and confutation phases, demonstrates the
capability of the method to selectively identify anom-
alous signal transients even in a so variable context.
Therefore, also in a complex area like the Indian
subcontinent, the robustness of the RAT approach has
been successfully tested confirming the importance of
time–space persistence analyses in selecting meaningful
TIR anomalous patterns. Three anomalous (signal
variations exceeding 3σ) sequences, responding to the
space–time persistence criterion, have been isolated in
the validation phase:
1. A first series is represented by TIR anomalies which
affect the neighbourhood of the epicentral area
(mainly on the sea), beginning from 11 days before
the seismic event. They were detected very close to
Gujarat (Fig. 5) 5 days before the earthquake. Their
spatial–temporal dynamics seems to imply an
important meaning when it is compared to the time
and place of Gujarat's earthquake.
2. A second sequence is characterized by anomalous
pixels which seem to delineate, in a remarkable way,
the thrust boundary between Indian and Eurasian
plates since 1 day before Gujarat's earthquake. They
describe, for at least four consecutive days straddling
the seismic event, in a very surprising way, the trend
of the tectonic boundary (which is followed in great
detail), suggesting a possible (wide range) correlation
with Gujarat's earthquake occurrence.
3. A third series of TIR anomalies is visible before the
seismic event, over a period of 10 days (11th–21st
January) in the central area of India near an active
fault system (reported in Fig. 3). However, their
presence was identified under persistent cloud cover
conditions and the influence of meteorological
factors should not be excluded.
On the whole, the observations which have been
highlighted in the case of Gujarat's earthquake, together
with the results already achieved in the previous works
of the same authors about this topic, strongly encourage
the continuation of the studies in this direction in order
to reach a clearer vision of the possible correlation (in
terms of distance from epicentre, intensity, time delay)
of TIR anomalies appearance with earthquake occur-
rences. At the same time, once again, the use of remote
sensed data in the monitoring of seismically active areas
has proved to offer unexpected inputs to the geophysical
community. Our results, if confirmed by further studies
and/or independent observations, could induce, for
instance, to deeper reflections about a possible stricter
connection existing between phenomena related to
intraplate tectonic structures and plate boundaries. We
hope that this paper can help further studies in this
direction and, through active exchange of information
coming from different disciplines, we can contribute to a
better knowledge of the physical processes associated to
earthquake preparation phases.
Acknowledgements
The authors wish to thank Prof. Seiya Uyeda and
Sergey Pulinets for the contribution they gave to
improve clarity and completeness of this paper.
This work has been supported by the Italian Space
Agency (Contract No. I/R/173) within the framework of
209
N. Genzano et al. / Tectonophysics 431 (2007) 197–210

14. “SeisMASS” (Seismic Area Monitoring by Advanced
Satellite Systems) Project and by EC and ESA through the
Network of Excellence “GMOSS” (Contract No. SNE3-
CT-2003-503699) in the framework of GMES Program.
References
ASC (Amateur Seismic Centre), http://www.asc-india.org.
Bonfiglio, A., Macchiato, M., Pergola, N., Pietrapertosa, C.,
Tramutoli, V., 2005. AVHRR automated detection of volcanic
clouds. International Journal of Remote Sensing 26 (1), 9–27.
Cervone, G., Singh, R.P., Kafatos, M., Yu, C., 2005. Wavelet maxima
curves of surface latent heat flux anomalies associated with Indian
earthquakes. Natural Hazards and Earth System Sciences 5, 87–99.
Corrado, R., Caputo, R., Filizzola, C., Pergola, N., Pietrapertosa, C.,
Tramutoli, V., 2005. Seismically active areas monitoring by robust
TIR satellite techniques: A sensitivity analysis on low magnitude
earthquakes occurred in Greece and Turkey since 1995. Natural
Hazards and Earth System Sciences 5, 101–108.
Cressie, N.A.C., 1993. Statistics for Spatial Data. J. Wiley and Sons,
Indianapolis, IN, USA.
Cuomo, V., Lasaponara, R., Tramutoli, V., 2001. Evaluation of a new
satellite-based method for forest fire detection. International Jour-
nal of Remote Sensing 22 (9), 1799–1826.
Cuomo, V., Filizzola, C., Pergola, N., Pietrapertosa, C., Tramutoli, V.,
2004. A self-sufficient approach for GERB cloudy radiance
detection. Atmospheric Research 72 (1–4), 39–56.
Dey, S., Singh, R.P., 2003. Surface latent heat flux as an earthquake
precursor. Natural Hazards and Earth System Sciences 3, 749–755.
Dey, S., Sarkar, S., Singh, R.P., 2004. Anomalous changes in column
water vapour after Gujarat earthquake. Advances in Space
Research 33, 274–278.
Di Bello, G., Filizzola, C., Lacava, T., Marchese, F., Pergola, N.,
Pietrapertosa, C., Piscitelli, S., Scaffidi, I., Tramutoli, V., 2004.
Robust satellite techniques for volcanic and seismic hazards
monitoring. Annals of Geophysics 47 (1), 49–64.
Filizzola, C., Pergola, N., Pietrapertosa, C., Tramutoli, V., 2004.
Robust satellite techniques for seismically active areas monitoring:
a sensitivity analysis on September 7th 1999 Athens's earthquake.
Physics and Chemistry of the Earth 29, 517–527.
Gorny, V.I., Salman, A.G., Tronin, A.A., Shilin, B.B., 1988. The Earth
outgoing IR radiation as an indicator of seismic activity.
Proceeding of the Academy of Sciences of the USSR 301, 67–69.
Hamza, V.M., 2001. Tectonic leakage of fault bounded aquifers subject
to non-isothermal recharge: a mechanism generating thermal
precursors to seismic events. Physics of the Earth and Planetary
Interiors 126, 163–177.
IRIS (Incorporated Research Institutions for Seismology), http://www.
iris.washington.edu.
Lacava, T., Cuomo, V., Di Leo, E.V., Pergola, N., Romano, F.,
Tramutoli, V., 2005. Improving soil wetness variations monitoring
from passive microwave satellite data: the case of April 2000
Hungary flood. Remote Sensing of Environment 96/2, 135–148.
Menke, W., 1984. Geographical Data Analysis: Discrete Inverse
Theory. Academic Press, New York.
Okada, Y., Mukai, S., Singh, R.P., 2004. Changes in atmospheric
aerosol parameters after Gujarat earthquake of January 26, 2001.
Advances in Space Research 33, 254–258.
Ouzounov, D., Freund, D., 2004. Mid-infrared emission prior to strong
earthquakes analyzed by remote sensing data. Advances in Space
Research 33, 268–273.
Pergola, N., Pietrapertosa, C., Lacava, T., Tramutoli, V., 2001. Robust
satellite techniques for monitoring volcanic eruptions. Annals of
Geophysics 44 (2), 167–177.
Pergola, N., Tramutoli, V., Marchese, F., 2004a. Automated detection
of thermal features of active volcanoes by means of infrared
AVHRR records. Remote Sensing of Environment 93, 311–327.
Pergola, N., Tramutoli, V., Scaffidi, I., Lacava, T., Marchese, F.,
2004b. Improving volcanic ash clouds detection by a robust
satellite technique. Remote Sensing of Environment 90 (1), 1–22.
Qiang, Z.-J., Dian, C.-G., 1992. Satellite thermal infrared impending
temperature increase precursor of Gonghe earthquake of magni-
tude 7.0, Qinghai Province. Geoscience 6 (3), 297–300.
Qiang, Z.-J., Xu, X.-D., Dian, C.-G., 1991. Thermal infrared anomaly
precursor of impending earthquakes. Chinese Science Bulletin 36
(4), 319–323.
Qiang, Z.-J., Xu, X.-D., Dian, C.-G., 1997. Thermal infrared anomaly-
precursor of impending earthquakes. Pure and Applied Geophysics
149, 159–171.
Saraf, A.K., Choudhury, S., 2005a. Satellite detects surface thermal
anomalies associated with the Algerian earthquakes of May 2003.
International Journal of Remote Sensing 26 (13), 2705–2713.
Saraf, A.K., Choudhury, S., 2005b. NOAA-AVHRR detects thermal
anomaly associated with the 26 January 2001 Bhuj earthquake,
Gujarat, India. International Journal of Remote Sensing 26 (6),
1065–1073.
Tramutoli, V., 1998. Robust AVHRR Techniques (RAT) for environ-
mental monitoring: theory and applications. In: Cecchi, Giovanna,
Zilioli, Eugenio (Eds.), Earth Surface Remote Sensing II, SPIE,
vol. 3496, pp. 101–113.
Tramutoli, V., Claps, P., Marella, M., Pergola, N., Pietrapertosa, C.,
Sileo, C., 2001a. Hydrological implications of remotely-sensed
thermal inertia. In: Owe, M., Brubaker, K., Ritchie, J., Rango, A.
(Eds.), Remote Sensing and Hydrology 2000, vol. 267. IAHS
Publ., pp. 207–211.
Tramutoli, V., Di Bello, G., Pergola, N., Piscitelli, S., 2001b. Robust
satellite techniques for remote sensing of seismically active areas.
Annals of Geophysics 44 (2), 295–312.
Tramutoli, V., Pergola, N., Pietrapertosa, C., 2001c. Training on
NOAAAVHRR of robust satellite techniques for next generation of
weather satellites: an application to the study of space–time
evolution of Pinatubo's stratospheric volcanic cloud over Europe.
In: Smith, W.L., Timofeyev, Yu.M. (Eds.), IRS 2000: Current
Problems in Atmospheric Radiation. VA' Deepak Publishing,
Hampton, pp. 36–39.
Tramutoli, V., Cuomo, V., Filizzola, C., Pergola, N., Pietrapertosa, C.,
2005. Assessing the potential of thermal infrared satellite surveys
for monitoring seismically active areas: The case of Kocaeli (Izmit)
earthquake, August 17, 1999. Remote Sensing of Environment 96,
409–426.
Tronin, A.A., 1996. Satellite thermal survey—a new tool for the study
of seismoactive regions. International Journal of Remote Sensing
41 (8), 1439–1455.
Tronin, A.A., 2000. Thermal IR satellite sensor data application for
earthquake research in China. International Journal of Remote
Sensing (16), 3169–3177.
Tronin, A.A., Hayakawa, M., Molchanov, O.A., 2002. Thermal IR
satellite data application for earthquake research in Japan and
China. Journal of Geodynamics 33 (4–5), 519–534.
210 N. Genzano et al. / Tectonophysics 431 (2007) 197–210