AN INTEGRATED APPROACH TO GRAVITY ANOMALY SEPARATION USING SPECTRAL DECOMPOSITION AND PIECEWISE LINEAR ANALYSIS

This paper examines the utilization of gravity methods in the discovery of subsurface geology, focusing on a specific case study conducted in Langsa City. We propose a new technique that combines Spectral Decomposition and Piecewise Linear Fit to separate and analyze regional and residual gravity anomalies. Subsurface geological variations are analyzed using satellite gravity data, and the data is processed using algorithms created on the open-source Python platform. The Piecewise Linear Fit approach is specifically developed to detect change points in gravity data, enabling the differentiation of intricate gravity anomalies. This methodology enhances the precision of distinguishing regional and residual anomalies, leading to a more distinct representation of mass distribution beneath the surface. The utilization of spectral analysis in conjunction with piecewise linear segmentation enables the detection of geological characteristics that are challenging to uncover using traditional techniques. The results indicate that the suggested method is superior in dealing with the difficulties commonly faced in gravity analysis, namely in detecting intricate underground formations such as faults, magmatic intrusions, and geological layer borders. As a result, this method makes important advancements in the field of gravity-based geophysical exploration techniques, providing researchers with a novel and precise instrument to enhance the interpretation of gravity data. This study is anticipated to provide novel guidelines for wider applications in geological mapping and exploration of natural resources.