Investigating the applicability of the Social Spider Optimization for the inversion of magnetic anomalies caused by dykes

Abstract

Dipping dykes are geologically important structures since they are very important structures for hydrogeological, geothermal and hydrocarbon research. Many methods have been introduced by researchers to interpret dykes from magnetic anomaly data, but each of these methods have limitations. Therefore, new techniques are constantly being developed to achieve better results. This study introduces a novel method based upon the Social Spider Optimization algorithm and tests the method using synthetic examples. The algorithm developed to decipher the source body properties, is presented in detail. The test data consists of synthetic anomalies corrupted by different levels of random noise and field anomalies from mining records in China and Turkey. The obtained results have showed that Social Spider Optimization is a reliable, stable and efficient tool for deciphering the physical properties of deep and shallow located dykes from magnetic data. In addition, the proposed method is recommended for inversion of other geophysics data such as self-potential and gravity data.