Identification of faults in the subsurface of Java Island using the ambient noise tomography method
Year: 2025
Type: article
Abstract: This study applies the Ambient Noise Tomography (ANT) method to identify faults in the subsurface seismic structure of Java Island, characterized by complex tectonic conditions. The research utilizes waveform data recorded in 2021 by 99 BMKG stationary seismic sensors distributed across Java Island. Data processing includes single data preparation, cross-correlation, stacking, dispersion curve measurement, group velocity tomography, and result interpretation. The inversion process generates tomographic images of Rayleigh wave group velocities ranging from 1.88 km/s to 2.60 km/s, revealing significant contrasts in velocity anomalies. These contrasts, located at the boundaries between low and high velocity zones, are strongly correlated with fault lines and volcanic zones across the island. The results demonstrate the capability of the ANT method to delineate subsurface geological structures, including active fault systems, with high precision.
Source: MAUSAM
Language: English
Topic: Seismic Waves and Analysis
Subfield: Geophysics
Field: Earth and Planetary Sciences
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4408919489