The self-potential anomalies produced by simple polarized geologic structures are used in the position detection of buried objects such as rocks or minerals. If these objects are adjacent, a mixed self-potential anomaly data will be measured. However, the detection of the objects position from this mixed self-potential anomaly data is usually not possible. In this paper, the mixed self-potential anomaly data is first separated by a blind signal separation technique called the independent component analysis (ICA), then the learning vector quantization (LVQ) neural network is used in the position detection of the separated self-potential anomalies. The proposed system achieves very high accuracy.
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