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Time-lapse analysis with earth resistance and electrical resistivity imaging
Time-lapse analysis with earth resistance and electrical resistivity imaging
Time-lapse analysis with earth resistance and electrical resistivity imaging
Time-lapse analysis with earth resistance and electrical resistivity imaging
Time-lapse analysis with earth resistance and electrical resistivity imaging
Time-lapse analysis with earth resistance and electrical resistivity imaging
Time-lapse analysis with earth resistance and electrical resistivity imaging
Time-lapse analysis with earth resistance and electrical resistivity imaging
Time-lapse analysis with earth resistance and electrical resistivity imaging
Time-lapse analysis with earth resistance and electrical resistivity imaging
Time-lapse analysis with earth resistance and electrical resistivity imaging
Time-lapse analysis with earth resistance and electrical resistivity imaging
Time-lapse analysis with earth resistance and electrical resistivity imaging
Time-lapse analysis with earth resistance and electrical resistivity imaging
Time-lapse analysis with earth resistance and electrical resistivity imaging
Time-lapse analysis with earth resistance and electrical resistivity imaging
Time-lapse analysis with earth resistance and electrical resistivity imaging
Time-lapse analysis with earth resistance and electrical resistivity imaging
Time-lapse analysis with earth resistance and electrical resistivity imaging
Time-lapse analysis with earth resistance and electrical resistivity imaging
Time-lapse analysis with earth resistance and electrical resistivity imaging
Time-lapse analysis with earth resistance and electrical resistivity imaging
Time-lapse analysis with earth resistance and electrical resistivity imaging
Time-lapse analysis with earth resistance and electrical resistivity imaging
Time-lapse analysis with earth resistance and electrical resistivity imaging
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Time-lapse analysis with earth resistance and electrical resistivity imaging

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A presentation by Rob Fry at the DART horizon Scanning workshop on the 17th September 2013

A presentation by Rob Fry at the DART horizon Scanning workshop on the 17th September 2013

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  • Hi, I’m Robert Fry and I have been looking into the time-lapse or ‘seasonality’ effect within earth resistance data over the study areas.
  • The problem of ‘seasonality’ for earth resistance survey is one which is often mentioned in literature, however, despite some very good early work on the problem of detection
  • From the data alone, an idea of the changing measured resistance can be plotted for each site. For all sites, there is a peak in resistance measurements at the beginning of the survey period, during the summer/autumn of 2011, which gradually decreases throughout the survey. The measured resistances are at their lowest during the summer/autumn 2012. Therefore a total lack of a ‘seasonal’ response can be demonstrated.
  • As the data on these greyscale images (show at ±1SD white to black) show, the success of the earth resistance technique varied from site to site. The data from the free-draining sites appear to show a relatively consistent feature throughout the fieldwork period, however the clay soils show that difficulties exist when trying to identify an archaeological feature. Perhaps the most alarming, is that in only one month was the ditch successfully interpreted within the dataset from Diddington Clay Field (Sept 2012) which has suffered from compaction at the surface due to the area formally being used as a tractor tramline.
  • This slide shows how the data might be interpreted if produced blind for a geophysical report. The interpreted ditch features are coloured in purple to stand out. Again it is clear that the free draining soils continuously produce a contrast which is substantial enough to be interpreted. The Clay soils at Quarry Field show a feature in 8 of 14 months (57% of the time), and Diddington Clay Field produces a substantial ditch response in 1 of 14 months (7% of the time).
  • Previous attempts have been made (Al Chalibi and Reese, Cott, Clark) however these have either focussed on measuring either the average magnitude of response, or looking at specific high and low datapoints within the range of data collected. This has been successful for studies over relatively simple environments where the ditches are always detectable, in soils where the contrast is always strong. The DART survey areas howeverdo not always produce a ditch anomaly (especially on clays), and are often affected by substantial ‘noise’ from surface compaction (see DCF). Such factors have meant that a single approach is not possible for the successful quantification of a contrast factor at these sites. It was necessary to introduce a function based on a statistical detection test, to quantify how different the data within the ditch area is to the background material. Once this score is above the required threshold, is can be multiplied by the magnitude test which takes a percentage difference between the centre of the ditch anomaly and the background response.
  • The results from the contrast factor tests confirm the response form the greyscale data seen a few slides earlier, with freely draining soils producing a constant contrast factor which is able to detect the ditch throughout the survey period. The clay sites, however highlight the difficulties in identifying the archaeological feature throughout the survey season, and also highlights the difference between when a suitable ditch response can be predicted.
  • By far the biggest influence on the resistivity response, is the effect of the weather on the soils under investigation. From the weather stations on-site, the rainfall and evapotranspiration figures were calculated and, from these a measurement of the cumulative moisture balance could be produced. This is an estimate of the fluctuating amount of moisture on the sites from when the weather stations were installed. From these measurements, the ER data can be further analysed for any significant correlations between the detection of a feature and the moisture flux.
  • Compared to the CMB data, the site at Cherry Copse at first showed a negligible correlation (-0.101). However, it was found that the cause of this was due to data that was collected over the drought period of 2011. These three months seemed to provide a contrast which was much less than would have been expected
  • With these outliers removed, the correlation improved substantially (0.82**). It would seem then, that the magnitude of the ditch response increases the drier it gets – up to a point. At the crucial point where the ditch fill also looses moisture due to drought conditions, the rule changes and the magnitude of the response decreases.
  • As with the results at Cherry Copse, the contrast of the ditch at Quarry Field provided a significant negative correlation, especially to the smallest probe separations. Unlike Cherry Copse, there appears to be no deterioration of the contrast over drought periods, with the response actually further increased by the dry soils. This is especially seen at the smallest probe separation.
  • The correlations at Diddington, interestingly, appear to have a positive correlation to the cumulative moisture balance. This was a rather unexpected trend, unlike the sites at Cirencester, appear to produce a better ER response as the ground becomes more saturated. This is the reason why the ditch is only seen in the last few months of survey at Diddington Clay Field.
  • As seen by the graph (top left), The main contrast we see in the resistivity is between the data in Context 9 (sandy loam), and those abutting it (limestone contexts 4 and 5). We would expect the loam to be very moisture retentive compared to these surrounding geological limestone contexts, which appears to cause the high resistivity contrast throughout the year. The graph may also indicate that the clay capping of Context 9 prevents it from losing too much moisture in times of drought, since it was the only archaeological context not to dramatically peak in resistivity in July 2011. The difference between the resisitivities of Context 9 and 4&5 (left) compared to the ER contrast factor results. Despite all the ER data having a substantial correlation to the measured differences between the contexts, the strongest correlation between the difference in the contexts and the ER contrast factor, occurs at the α-spacing of 0.75m. This matches well with the known depth of context 9, which at the centre of the ditch, is situated between 0.68 – 0.92m below ground surface. The effect of the changing resisitivities appears to be after a delay of 4 weeks from the changing weather variables, with a correlation of -0.697**.
  • The difference between the resisitivities of Context 9 and 4&5 (left) compared to the ER contrast factor results. Despite all the ER data having a substantial correlation to the measured differences between the contexts, the strongest correlation between the difference in the contexts and the ER contrast factor, occurs at the α-spacing of 0.75m. This matches well with the known depth of context 9, which at the centre of the ditch, is situated between 0.68 – 0.92m below ground surface. The effect of the changing resisitivities appears to be after a delay of 4 weeks from the changing weather variables, with a correlation of -0.697**.
  • At Quarry Field, due to the extremely homogenous nature of the soils at the site (clay background, with clay ditch fill) hardly any differences between the resisitivities of the soils at depth existed. What was noticed however is that within the topsoil, the area located directly above the location of the ditch contained substantially higher resistivity values than the soils from the same context (Top Left). When the averages of these two datasets are compared, the difference between the two samples is highest at the start of the fieldwork period (Orange and Blue lines – Left). The difference between the two samples is also plotted in black. This resistivity difference between the samples at Context 1 overlying the ditch cut and around the ditch cut was highly correlated with the contrast factor ER results with a co-efficient of 0.772 (significant to the 0.01 level). This also explains why the 0.25m twin-probe separation shows the greatest magnitude to the changing response.It appears that the old field drain within the ditch is still working, and acting as a sump for moisture to percolate. During dry or drought times, the moisture at the topsoil is both more prone to the effects of evapotranspiration, whilst the area above the ditch is also loosing moisture to a downward force caused by the sump. This is why the area above the ditch cut becomes a positive resistance anomaly, and is best detected over the driest periods of survey.
  • At Quarry Field, due to the extremely homogenous nature of the soils at the site (clay background, with clay ditch fill) hardly any differences between the resisitivities of the soils at depth existed. What was noticed however is that within the topsoil, the area located directly above the location of the ditch contained substantially higher resistivity values than the soils from the same context (Top Left). When the averages of these two datasets are compared, the difference between the two samples is highest at the start of the fieldwork period (Orange and Blue lines – Left). The difference between the two samples is also plotted in black. This resistivity difference between the samples at Context 1 overlying the ditch cut and around the ditch cut was highly correlated with the contrast factor ER results with a co-efficient of 0.772 (significant to the 0.01 level). This also explains why the 0.25m twin-probe separation shows the greatest magnitude to the changing response.It appears that the old field drain within the ditch is still working, and acting as a sump for moisture to percolate. During dry or drought times, the moisture at the topsoil is both more prone to the effects of evapotranspiration, whilst the area above the ditch is also loosing moisture to a downward force caused by the sump. This is why the area above the ditch cut becomes a positive resistance anomaly, and is best detected over the driest periods of survey.
  • Transcript

    • 1. Time-lapse analysis with earth resistance and electrical resistivity imaging Robert Fry Ph.D Candidate The University of Bradford DART heritage remote sensing horizon scanning workshop September 2013
    • 2. Current English Heritage geophysical guidelines state that it is preferable to conduct earth resistance surveys: ‘when the moisture contrasts are at their most accentuated’ (David et al. 2008, 27) • An aspect we do not know how to predict, or how it will effect individual target features
    • 3. Aim • To attempt to better understand the earth resistance response over each study area, how and why it changes, and how to predict when archaeological features will best be detected using these techniques. In this presentation: • Introduce some of the data collected during the DART fieldwork period and demonstrate the problems with predicting resistance surveys • Introduce a new methodology for the quantification of geophysical contrast, the key to successful detection • Demonstrate how each study area interacts with weather, specifically the change in soil moisture • How a novel analysis of the ERI profiles may help untangle the cause of each anomaly, and create a basis for future modelling
    • 4. Earth Resistance analysis Geoscan Twin-Probe RM15 multiplexed earth resistance Creating a robust solution to measuring a contrast factor. Detection * Magnitude = Contrast
    • 5. Harnhill, Cirencester Diddington, Cambridgeshire Cherry Copse Quarry Field Diddington Clay FieldPasture Field Claysoils Free-drainingsoils
    • 6. Seasonal effect? June 2011 Vs. June 2012 Cherry Copse
    • 7. Harnhill, Cirencester Diddington, Cambridgeshire Free-drainingsoils Claysoils Cherry Copse Quarry Field Diddington Clay FieldPasture Field
    • 8. Harnhill, Cirencester Diddington, Cambridgeshire Free-drainingsoils Claysoils Cherry Copse Quarry Field Diddington Clay FieldPasture Field
    • 9. How can we quantify the success of the ER response? • A new 2-part methodology quantifies a contrast factor based on: – A detection test, based on non-parametric statistics which returns a score (from 0-1) determining how different the data from the ditch is from the background. If the score is above 0.4, the ditch is determined to be detectable within the dataset. Mann-Whitney Test Calculation of the Z statistic Calculation of Pearson’s r score Calculating the f(r) when r>0.4 – A magnitude test, where the average measurement from the centre of the ditch response, is compared against an average background measurement running parallel to the ditch. The percentage difference between the two measurements is the magnitude of the ditch anomaly. Calculation of the Specific Population Magnitude Factor – From these two calculations, the contrast factor of the response is calculated: Contrast Factor = f(r) * SPMF
    • 10. Harnhill, Cirencester Diddington, Cambridgeshire Free-drainingsoils Claysoils Cherry Copse Quarry Field Diddington Clay FieldPasture Field
    • 11. Weather data Evapotranspiration - Rainfall = Cumulative Moisture Balance
    • 12. Harnhill, Cirencester Diddington, Cambridgeshire Free-drainingsoils Claysoils Cherry Copse Quarry Field Diddington Clay FieldPasture Field
    • 13. Harnhill, Cirencester Diddington, Cambridgeshire Free-drainingsoils Claysoils Cherry Copse Quarry Field Diddington Clay FieldPasture Field
    • 14. Harnhill, Cirencester Diddington, Cambridgeshire Free-drainingsoils Claysoils Cherry Copse Quarry Field Diddington Clay FieldPasture Field
    • 15. Harnhill, CirencesterFree-drainingsoils Claysoils Cherry Copse Quarry Field Diddington Clay FieldPasture Field Diddington, Cambridgeshire
    • 16. Harnhill, Cirencester Diddington, Cambridgeshire Free-drainingsoils Claysoils Cherry Copse Quarry Field Diddington Clay FieldPasture Field At Cherry Copse – there is a negative correlation between contrast factor and weather, the drier the site, the better the earth resistance response – to a point. Once the ground becomes too dry the contrast factor reduces substantially. Showing a steady decrease in moisture retention from the ditch fills. Prolonged drought would probably result in further decrease in contrast. At Quarry Field – there is a negative correlation to the weather, with the driest conditions most likely to produce the best contrast factors to sufficiently detect the ditch. During wet periods, the contrast is significantly reduced. At Pasture Field – there is a positive correlation to weather, with wetter conditions providing a better ER contrast. The ditch can however be detected throughout the year. The magnitude of the anomaly appears to be less effected by the weather variables, due to the deeper cut of the ditch. At Diddington Clay Field – there is a positive correlation to the weather, however, the weak ER response is significant to detect a ditch anomaly only at the wettest points of survey. The rest of the survey the ditch is undetected within the data.
    • 17. Earth resistance time-lapse and weather data • Common misconceptions: •There is no ‘seasonality’ of data. The geophysical response does not change as a factor of a change in seasons. The data will not necessarily produce the same response at similar times of the year. •Wet / damp weather does not always make for the best detection of ditches using ER. •All archaeological features will produce different anomalies, at different times of the year, dependant on the soils, environmental constraints, and the percolation of moisture within those soils. • At the sites, the relationship between the cumulative moisture balance and the earth resistance contrast is linear. This linear trend is however not always the same between sites, and does have caveats (such as the drought period at Cherry Copse) • Understanding why the different sites produce a sufficient detectable contrast with earth resistance techniques will improve the detection of these features for future work and enable modelling of the sites to better ascertain the changing moisture dynamics through the soil profile. • Pursuing an overall model for the ‘best’ survey time for earth resistance survey is extremely difficult, especially for clay soils. ‘When moisture contrasts are at their most accentuated’ • What causes the contrast? – Is it the same for all sites? - Is the rule universal?
    • 18. Incorporating ERI analysis FlashRes 64 Electrical Imaging Using the ERI data to tell more about what is influencing the change in response
    • 19. Extraction of resistivity data from ERI into separate contexts
    • 20. Cherry Copse • The resisitivities between Contexts 3 (silty clay) & 7 (sandy clay) are extremely similar throughout. Suggesting that these contexts do not produce the ditch anomaly • Main contrast between is seen within the data around Context 9 (sandy loam) and the limestone geology abutting it (Contexts 4&5)
    • 21. Cherry Copse 0 20 40 60 80 100 120 140 Jun-11 Jul-11 Aug-… Sep-… Oct-… Nov-… Dec-… Jan-12 Feb-… Mar-… Apr-… May… Jun-12 Jul-12 Aug-… Sep-… DIfferenceinResistivityvalues (Ohm.m) Changing difference between the resistivity of Contexts 4 & 5 (averaged) to Context 9 ER contrast factor Correlation Contexts 0.25m 0.586* 0.5m 0.608* 0.75m 0.7** 1m 0.658* Difference in resistivity between Contexts 4&5 and 9 Earth resistance contrast factor Correlation Strong correlations: •The difference in resistivity between the anomaly producing contexts correlate well with the ER contrast factors • This is best seen at twin-probe spacing of 0.75m and 1m (known depth of Context 9 = 0.68-0.92m) • The time-lapse correlation shows there to be a 4 week delay between the surface increase in CMB to the effect on the resistivity values of the contexts at this depth
    • 22. Quarry Field • Only significant difference between contexts occurs within Context 1, between the topsoil directly above the ditch cut and the topsoil around it. • The higher resistivity of the soil above the ditch creates the resistance anomaly identified as the ditch (not the ditch itself) • A properly relating to the sump created by the field drain to which the ditch was used for
    • 23. Quarry Field • The ER contrast factor results correlate strongly to the calculated difference between the samples from context 1. (0.772 sig. 0.01 level) • The effect is best seen at the smaller probe separation linking to the topsoils. • Sump creates a downward force on the moisture above the ditch, which is most apparent during dry periods, becoming drier and a higher resistivity.
    • 24. Conclusions and future work A context specific extraction methodology can help disentangle the results of the ER data, and provide greater clarity to the specific problems of detection within each study area. Each survey area is different(!) – this makes life more interesting, however means any prediction to a ‘best survey time’ is limited. At Cherry Copse, the ditch anomaly is identified manly due to the differences in resisitivities between the archaeological Context 9 and geological Contexts 4 and 5. This change is most effective after a 4 week delay from weather variables. The anomaly at Quarry Field appears to have been created from a difference in soil resistivity directly above the ditch cut, rather than directly from the ditch fills themselves. A sump has been created which forces moisture from the topsoil downwards, creating a high resistance feature. The free draining soils appear to produce a clear anomaly throughout the fieldwork period, although the magnitude of the anomaly created changes differently to weather variables. The clay soils have indeed been ‘difficult’ for detection, however the ditches situated on these soils can be detected, provided the survey is timely, and after consideration of the weather variables and history of the sites. Importance of both the soils and weather variables – can this be spread to a wider area? Future Work: The research (and the PhD) is unfinished and still at a relatively early stage Modelling the data to create prediction and percolation routines for the sites (David Jordan has already started looking into this) Lots of scope for future research, different soils, scenarios, better temporal resolution etc.
    • 25. Thank you for listening

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