Algoritma Differential Evolution untuk Estimasi Parameter Sumber Anomali Self-Potential

Abstract

Self-Potential (SP) anomaly is naturally occurring potential differences due to electrochemical, electro-kinetic, and thermoelectric sources in the subsurface. The Source of SP anomaly can be modeled as a simple-geometry body, e.g: spheres, cylinders, and inclined sheets. The model parameter of SP anomaly is generally estimated using local optimization such as gradient-search-based methods. However, these methods have some drawbacks. Therefore, this problem needs to address using global optimization, namely Differential Evolution (DE) algorithm. DE is one of the metaheuristic algorithms adopting biological evolution in the optimization process. In this work, the DE algorithm is implemented to estimate the parameters of SP anomaly sources.  There are two stages in this work, e.g: synthetic test and field data inversion. In the synthetic test, DE is built and implemented in synthetic data generated from a cylinder body contaminated by noise. This test shows that DE can estimate the parameters of the cylinder body (SP anomaly source) well. In the field data inversion, DE is implemented to estimate the SP Surda anomaly which has been studied by other methods. The results of DE estimation are comparable to the previous studies, and able to provide uncertainty information. DE algorithm can be implemented to characterize the source of SP anomaly for futher study.