CRUSTAL STRUCTURE IMAGING BENEATH WESTERN JAVA USING SEISMIC AMBIENT NOISE TOMOGRAPHY

The western part of Java, Indonesia, is an area prone to multiple geological hazards due to its proximity to the subduction of Australia Plate beneath Eurasian. In this area, there are several major urban agglomerations, including Jakarta, the capital city of Indonesia, and Bandung, the capital c...

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Bibliographic Details
Main Author: Rosalia, Shindy
Format: Dissertations
Language:Indonesia
Online Access:https://digilib.itb.ac.id/gdl/view/46725
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Institution: Institut Teknologi Bandung
Language: Indonesia
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Summary:The western part of Java, Indonesia, is an area prone to multiple geological hazards due to its proximity to the subduction of Australia Plate beneath Eurasian. In this area, there are several major urban agglomerations, including Jakarta, the capital city of Indonesia, and Bandung, the capital city of West Java Province, which are threatened by sources of seismic and volcanic activity. It is crucial to have a better understanding of the upper crustal structure to support seismic hazard and disaster mitigation efforts in this area. To image the upper crustal structure, we applied Ambient Noise Tomography to a new waveform dataset collected from 85 temporary seismometers deployed during 2016-2018. Cross-correlation of the waveform data was applied to retrieve empirical Rayleigh wave Green’s functions between station pairs, and the spatial distribution of group velocity was obtained by inverting the dispersion curves. We compared two different methods for group velocity inversion: iterative, least-squares subspace optimization, and probabilistic sampling based on the Trans-dimensional Bayesian method. The results show that, although computationally expensive, the Trans-dimensional Bayesian approach offered important advantages over optimization, including more effective and explorative of the model space and more robust characterization of the spatial pattern of Rayleigh wave group velocity. The Neighbourhood Algorithm was applied to depth invert the Rayleigh wave group velocity into a 1D shear-wave velocity profile, which then interpolated to produce the final 3D shear-wave velocity maps. The shear-wave velocity result imaged the geological structure up to 17 km depth. Our inversion of shear wave velocity showed that for shallow depth (1-6 km), shear velocity correlates well with surface geology, and for deeper depth (7-17 km), it correlates with crystalline crustal basement. The northern part of the study area has a thickening sediment layer to the north shown by low shear-wave velocity. The result from this study has important implications for the depth of the sediment layer in the western part of Java in relation to the seismic risk modeling. The 3D model could also be useful for the initial model of another seismological study in the area.