Enhanced gravity anomaly separation using a Wiener and Preferential Bandpass Butterworth Filtering for structural identification in Telomoyo geothermal area

Abstract

Accurate anomaly separation in gravity data is crucial for geophysical investigations, particularly in resource exploration and subsurface analysis. Traditional wavelength filtering techniques often struggle to effectively balance noise suppression with anomaly preservation. In this study, we evaluate different wavelength filtering methods to identify the most effective approach for gravity anomaly separation. We propose a technique that combines the strengths of Wiener and Butterworth filters to achieve more accurate residual anomaly extraction than common high-pass and regular bandpass filtering. The Wiener filter is utilized for efficient noise reduction, i.e., mitigating terrain-induced effects, while the Butterworth filter is applied to isolate regional and residual anomalies. The methodology is first validated using synthetic gravity data generated from forward modeling of subsurface density variations, incorporating realistic terrain noise. Comparative analysis with common high-pass filtering techniques and regular bandpass filtering demonstrates the better performance of the proposed method in terms of accuracy and noise suppression. The approach is then applied to gravity data from the Telomoyo region, successfully extracting smoother residual anomalies with enhanced geological interpretability, especially in the subsurface structure delineation using edge detection analysis (e.g., Second Vertical Derivative and Improved Normalize Horizontal Tilt Angle). This method provides a robust and efficient tool for geophysical data processing, with significant implications for geothermal exploration, mineral prospecting, and subsurface structural analysis.