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This article describes the use of k-means clustering, an unsupervised image classification technique, to help interpret subsurface targets. The k-means algorithm is employed to combine and classify the two-dimensional (2D) inverse resistivity models obtained from three different electrode arrays. The algorithm is initialized through the selection of the number of clusters, number of iterations and other parameters such as stopping criteria. Automatically, it seeks to find groups of closely related resistivity values that belong to the same cluster and are more similar to each other than resistivity values belonging to other clusters. The approach is applied to both synthetic and field data. The 2D postinversions of the resistivity data were preprocessed by resampling and interpolating to the same coordinate. Following the preprocessing, the three images are combined into a single classified image. All the image preprocessing, manipulation and analysis are performed using the PCI Geomatics software package. The results of the clustering and classification are presented as classified images. An assessment of the performance of the individual and combined images for the synthetic models is carried out using an error matrix, mean absolute error and mean absolute percent error. The estimated errors show that images obtained from maximum values of the reconstructed resistivity for the different models give the best representation of the true models. Additionally, the overall accuracy and kappa values show good agreement between the combined classified images and true models. Depending on the model, the overall accuracy ranges from 86 to 99 %, while the kappa coefficient is in the range of 54-98 %. Classified images with kappa coefficients greater than 0.8 show strong agreement, while images with kappa coefficients greater than 0.5 but less than 0.8 give moderate agreement. For the field data, the k-mean classifier produces images that incorporate structural features of the three electrode array configurations. Consequently, some clusters that overwhelmingly correspond to the lithologic units of the investigated areas are better identified than the tomographic images of each data set considered separately, underscoring the relevance of the unsupervised classification technique in this study.

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