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[HTT18-P03] An analysis of resistivity structure and electrode displacement by time-lapse resistivity survey
Keywords:Time-lapse resistivity survey, resistivity, electrode displacement, sparse regularization
1. Introduction
Time-lapse resistivity survey is capable of detecting a resistivity change in the ground by repeatable measurements at the same site. This technique is used for various purposes. For example, delineating a variation of ground water level, and assessing a ground improvement.
Time-lapse resistivity survey is generally used to detect a temporal change of resistivity in the ground. However, the study of Wilkinson et al (2010) showed that electrode displacement caused by an active landslide was predicted using a change of apparent resistivity obtained by time-lapse resistivity monitoring. Also, the study of Loke et al (2018) showed that joint inversion of resistivity and electrode displacement by using smoothness-constrained least squares optimization technique was developed. Analyzing a resistivity and electrode displacement using time-lapse resistivity data has a possibility of detecting a precursor of slope failure.
In this study, a new method for estimating resistivity change and electrode displacement was developed by using a sparse regularization technique. A 2D numerical simulation was conducted to clarify the effectiveness of the developed method.
2. Methodology
The time-lapse resistivity inversion method by using a sparse regularization technique (Kisanuki et al, 2022) was adjusted for analyzing electrode displacement. Electrode displacement of horizontal and vertical directions to the survey line was added to unknown parameters. The method assumes the sparsity to the temporal change of resistivity and electrode displacement from initial value.
3. Numerical simulation
A 2D numerical simulation was conducted to evaluate the performance of the developed inversion method. An initial data was calculated under the condition that the resistivity model was two layered structures, the number of electrodes was 21 and the electrode configuration was Pole-Pole array with 50cm spacing. The resistivity model reconstructed from the initial data was used for an initial resistivity model of the developed method. Next, the resistivity value of the first layer was changed. In addition, two types of electrode displacement that one electrode was moved in horizontal or vertical direction were considered. The displacement was 1, 2, 5, and 10cm in each situation. The calculated data in each case was inverted by the developed method. As a result, the capability of the developed method for estimating the resistivity and the electrode displacement was confirmed, although the estimated electrode displacement contained an error of up to 3% to the electrode spacing.
4. Conclusion
The inversion method for estimating the resistivity and the electrode displacement by using sparse regularization technique has been developed. Numerical simulation result shows that the developed method estimated both parameters. For demonstrating the effectiveness of this method, applying it to field data is needed in future studies.
References:
Wilkinson, P.B., Chambers, J.E., Meldrum, P.I., Gunn, D.A., Ogilvy, R.D. and Kuras, O., 2010. Predicting the movements of permanently installed electrodes on an active landslide using time-lapse geoelectrical resistivity data only, Geophys. J. Int., 183, 543-556.
Loke, M.H., Wilkinson P.B., Chambers, J.E. and Meldrum P.I., 2018. Rapid inversion of data from 2D resistivity surveys with electrode displacements, Geophysical Prospecting, 66, 579-594.
Kisanuki, H. and Sakurai, K., 2022. Time-lapse inversion of resistivity data by using sparse modeling, JPGU 2022.
Time-lapse resistivity survey is capable of detecting a resistivity change in the ground by repeatable measurements at the same site. This technique is used for various purposes. For example, delineating a variation of ground water level, and assessing a ground improvement.
Time-lapse resistivity survey is generally used to detect a temporal change of resistivity in the ground. However, the study of Wilkinson et al (2010) showed that electrode displacement caused by an active landslide was predicted using a change of apparent resistivity obtained by time-lapse resistivity monitoring. Also, the study of Loke et al (2018) showed that joint inversion of resistivity and electrode displacement by using smoothness-constrained least squares optimization technique was developed. Analyzing a resistivity and electrode displacement using time-lapse resistivity data has a possibility of detecting a precursor of slope failure.
In this study, a new method for estimating resistivity change and electrode displacement was developed by using a sparse regularization technique. A 2D numerical simulation was conducted to clarify the effectiveness of the developed method.
2. Methodology
The time-lapse resistivity inversion method by using a sparse regularization technique (Kisanuki et al, 2022) was adjusted for analyzing electrode displacement. Electrode displacement of horizontal and vertical directions to the survey line was added to unknown parameters. The method assumes the sparsity to the temporal change of resistivity and electrode displacement from initial value.
3. Numerical simulation
A 2D numerical simulation was conducted to evaluate the performance of the developed inversion method. An initial data was calculated under the condition that the resistivity model was two layered structures, the number of electrodes was 21 and the electrode configuration was Pole-Pole array with 50cm spacing. The resistivity model reconstructed from the initial data was used for an initial resistivity model of the developed method. Next, the resistivity value of the first layer was changed. In addition, two types of electrode displacement that one electrode was moved in horizontal or vertical direction were considered. The displacement was 1, 2, 5, and 10cm in each situation. The calculated data in each case was inverted by the developed method. As a result, the capability of the developed method for estimating the resistivity and the electrode displacement was confirmed, although the estimated electrode displacement contained an error of up to 3% to the electrode spacing.
4. Conclusion
The inversion method for estimating the resistivity and the electrode displacement by using sparse regularization technique has been developed. Numerical simulation result shows that the developed method estimated both parameters. For demonstrating the effectiveness of this method, applying it to field data is needed in future studies.
References:
Wilkinson, P.B., Chambers, J.E., Meldrum, P.I., Gunn, D.A., Ogilvy, R.D. and Kuras, O., 2010. Predicting the movements of permanently installed electrodes on an active landslide using time-lapse geoelectrical resistivity data only, Geophys. J. Int., 183, 543-556.
Loke, M.H., Wilkinson P.B., Chambers, J.E. and Meldrum P.I., 2018. Rapid inversion of data from 2D resistivity surveys with electrode displacements, Geophysical Prospecting, 66, 579-594.
Kisanuki, H. and Sakurai, K., 2022. Time-lapse inversion of resistivity data by using sparse modeling, JPGU 2022.