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Group velocity maps using subspace and transdimensional inversions: Ambient noise tomography in the western part of Java, Indonesia
Rosalia S.a, Cummins P.a,b, Widiyantoro S.a, Yudistira T.a, Nugraha A.D.a, Hawkins R.b
a Graduate Program of Geophysical Engineering, Faculty of Mining and Petroleum Engineering, Institute of Technology Bandung, Bandung, 40132, Indonesia
b Research School of Earth Sciences, Australian National University, Canberra, 2601, Australia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2019 The Author(s). Published by Oxford University Press on behalf of The Royal Astronomical Society.In this paper, we compare two different methods for group velocity inversion: iterative, least-squares subspace optimization and probabilistic sampling based on the transdimensional (Trans-D) Bayesian method with tree-based wavelet parametrization. The wavelet parametrization used a hierarchical prior for wavelet coefficients which could adapt to the data. We applied these inversion methods for ambient noise tomography of the western part of Java, Indonesia. This area is an area prone to multiple geological hazards due to its proximity to the subduction of the Australia Plate beneath Eurasia. It is therefore important to have a better understanding of upper crustal structure to support seismic hazard and disaster mitigation efforts in this area. We utilized a new waveform data set 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 dispersion curves. Our results show that, although computationally expensive, the Trans-D Bayesian approach offered important advantages over optimization, including more effective explorative of the model space and more robust characterization of the spatial pattern of Rayleigh wave group velocity. Meanwhile, the iterative, least-square subspace optimization suffered from the subjectivity of choice for reference velocity model and regularization parameter values. Our Rayleigh wave group velocity results show that for short (1-10 s) periods group velocity correlates well with surface geology, and for longer periods (13-25 s) it correlates with centres of volcanic activity.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Ambient noise tomographies,Bayesian approaches,Probabilistic sampling,Rayleigh wave group velocities,Regularization parameters,Subspace optimization,Volcanic activities,Wavelet coefficients[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Asia,Crustal structure,Seismic noise,Tomography[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text]We are grateful to the Australian National University for allowing their instruments to be used in this study, and the Meteorology, Climatology and Geophysics Agency of Indonesia (BMKG) for the support in the acquisition process. We are also grateful to the PMDSU scholarship granted to S. Rosalia from the Ministry of Research, Technology, and Higher Education of the Republic of Indonesia (2015–2019). This work was partially funded by the Ministry of Public Works and Housing, and the Indonesian Ministry of Research, Technology and Higher Education under WCU Program 2019 managed by ITB awarded to S. Widiyantoro. We thank Nick Rawlinson and Huajian Yao for their codes. We also thank the anonymous reviewers who helped to improve the quality of this paper. Figs 1(b), 2(a), S2 and S4 were prepared using the GMT Software (Wessel & Smith 1998). Figs 5–7, 9–12, S4, and S5 were prepared using the matplotlib package from Python (Hunter 2007). This research was undertaken with the assistance of resources from the National Computational Infrastructure, which is supported by the Australian Government.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1093/gji/ggz498[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]