A comparison study of different image appraisal tools for electrical resistivity tomography
D. Caterina, J. Beaujean, T. Robert and F. Nguyen
Issue: Vol 11, No 6, December 2013 pp. 639 - 657
Special Topic: Geotechnical Assessment and Geoenvironmental Engineering
Info: Article, PDF ( 4.66Mb )
To date, few studies offer a quantitative comparison of the performance of image appraisal tools. Moreover, there is no commonly accepted methodology to handle them even though it is a crucial aspect for reliable interpretation of geophysical images. In this study, we compare quantitatively different image appraisal indicators to detect artefacts, estimate depth of investigation, address parameters resolution and appraise ERT-derived geometry. Among existing image appraisal tools, we focus on the model resolution matrix (R), the cumulative sensitivity matrix (S) and the depth of investigation index (DOI) that are regularly used in the literature. They are first compared with numerical models representing different geological situations in terms of heterogeneity and scale and then used on field data sets. The numerical benchmark shows that indicators based on R and S are the most appropriate to appraise ERT images in terms of the exactitude of inverted parameters, DOI providing mainly qualitative information. In parallel, we test two different edge detection algorithms – Watershed’s and Canny’s algorithms – on the numerical models to identify the geometry of electrical structures in ERT images. From the results obtained, Canny’s algorithm seems to be the most reliable to help practitioners in the interpretation of buried structures. On this basis, we propose a methodology to appraise field ERT images. First, numerical benchmark models representing simplified cases of field ERT images are built using available a priori information. Then, ERT images are produced for these benchmark models (all simulated acquisition and inversion parameters being the same). The comparison between the numerical benchmark models and their corresponding ERT images gives the errors on inverted parameters. These discrepancies are then evaluated against the appraisal indicators (R and S) allowing the definition of threshold values. The final step consists in applying the threshold values on the field ERT images and to validate the results with a posteriori knowledge. The developed approach is tested successfully on two field data sets providing important information on the reliability of the location of a contamination source and on the geometry of a fractured zone. However, quantitative use of these indicators remains a difficult task depending mainly on the confidence level desired by the user. Further research is thus needed to develop new appraisal indicators more suited for a quantitative use and to improve the quality of inversion itself.