Self-potential data inversion through a Genetic-Price algorithm

• A hybrid algorithm is proposed for self-potential data inversion.
• Features of Controlled Random Search and Genetic Algorithms are combined.
• Accurate estimations of the SP source parameters are obtained.
• The proposed method is able to discriminate the shape of the anomaly source.

A global optimization method based on a Genetic-Price hybrid Algorithm (GPA) is proposed for identifying the source parameters of self-potential (SP) anomalies. The effectiveness of the proposed approach is tested on synthetic SP data generated by simple polarized structures, like sphere, vertical cylinder, horizontal cylinder and inclined sheet. An extensive numerical analysis on signals affected by different percentage of white Gaussian random noise shows that the GPA is able to provide fast and accurate estimations of the true parameters in all tested examples. In particular, the calculation of the root-mean squared error between the true and inverted SP parameter sets is found to be crucial for the identification of the source anomaly shape. Finally, applications of the GPA to self-potential field data are presented and discussed in light of the results provided by other sophisticated inversion methods.