Enhancing Fault Detection through One-Dimensional Multiscale Wavelet Analysis of Potential Field Data

Identifying faults is pivotal in mineral exploration and volcanic research, presenting a formidable task for geoscientists. Multiscale wavelet analysis has emerged as a potent tool for filtering and denoising geophysical data, outperforming conventional Fourier methods, especially in scenarios with discontinuous signals. This paper introduces a novel approach utilizing one-dimensional multiscale wavelet analysis for fault identification from potential field data. By leveraging the discrete wavelet transform with the Daubachies wavelet, our method exploits breakline and discontinuity detection concepts to discern faults effectively. We validate our approach through synthetic and real potential field data from Dagang, southern China demonstrating its effectiveness.