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SUMMARY Geophysical imaging using the inversion procedure is a powerful toolfor the exploration of the Earth's subsurface. However, the interpretation ofinverted images can sometimes be difficult, due to the inherent limitations ofexisting inversion algorithms, which produce smoothed sections. In order toimprove and automate the processing and interpretation of inverted geophysicalmodels, we propose an approach inspired from data mining. We selected analgorithm known as DBSCAN (Density-Based Spatial Clustering of Applicationswith Noise) to perform clustering of inverted geophysical sections. Themethodology relies on the automatic sorting and clustering of data. DBSCANdetects clusters in the inverted electrical resistivity values, with no priorknowledge of the number of clusters. This algorithm has the advantage of beingdefined by only two parameters: the neighbourhood of a point in the data space,and the minimum number of data points in this neighbourhood. We propose anobjective procedure for the determination of these two parameters. The proof ofconcept described here is applied to simulated ERT (electrical resistivitytomography) sections, for the following three cases: two layers with a step,two layers with a rebound, and two layers with an anomaly embedded in the upperlayer. To validate this approach, sensitivity studies were carried out on bothof the above parameters, as well as to assess the influence of noise on thealgorithm's performance. Finally, this methodology was tested on real fielddata. DBSCAN detects clusters in the inverted electrical resistivity models,and the former are then associated with various types of earth materials, thusallowing the structure of the prospected area to be determined. The proposeddata-mining algorithm is shown to be effective, and to improve theinterpretation of the inverted ERT sections. This new approach has considerablepotential, as it can be applied to any geophysical data represented in the formof sections or maps.

A data mining approach for improved interpretation of ERT inverted sections using the DBSCAN clustering algorithm