Fuzzy clustering of electrical and seismic data for the detection of geophysical targets

Tomographic methods, such as electrical resistivity tomography (ERT), induced polarization (IP) and seismic refraction tomography (SRT) are often effective for detecting geophysical targets in disparate real-world scenarios. However, a final reconstruction expressed only in terms of individual geophysical parameters (resistivity, chargeability, P-wave velocity) leaves room for ambiguity in complex sites exhibiting several transitions between layers or zones having different geophysical properties. In such cases, the sensitivity of the geophysical parameters for the various methods can differ significantly, so that a univocal interpretation based only on a visual comparison of the different models is often ineffective. To overcome these limits, in this work we present a machine learning-based quantitative approach for the detection of geophysical targets associated with both geological and anthropogenic scenarios. We integrate two-dimensional ERT, IP and SRT tomographic data with a soft clustering analysis by the Fuzzy C-Means (FCM) to obtain a final combined section, where each pixel is characterized by a cluster index and an associated membership value. The membership function of the Fuzzy C-Means is a good estimator of the accuracy of the subsurface reconstruction, as it ranges from 0 to 1, with 1 reflecting a high reliability of the clustering analysis. We apply this method to two case studies, related to the detection of leachate accumulation areas in a municipal solid waste landfill and to the bedrock characterization in a site prone to instability. In both cases, we detect the cluster associated with the geophysical targets of interest and our final sections are validated by a good agreement with the available direct information (boreholes and wells). The accuracy of the reconstruction is consistently high across most areas (membership values > 0.75), even though it is reduced in areas where the resolution of geophysical data is lower. Therefore, this approach may be a valuable automatic tool for optimizing the cost-effectiveness of projects where new constructions or remediation interventions have to be planned.