Hierarchical Bayesian Modeling for Improved High-Resolution Mapping of the Completeness Magnitude of Earthquake Catalogs
Assessing the completeness magnitude M is essential for most seismicity studies. However, when studying the spatial variation of M in a region, the conventional methods that compute M based on the frequency–magnitude distribution (FMD) tend to give gaps and large uncertainties of M in subregions of low seismicity, thus rendering high-resolution M mapping infeasible. To address the limitations of the FMD-based methods, the Bayesian magnitude of completeness (BMC) method was proposed a decade ago to incorporate a priori information about M derived from its empirical relationship to the seismic network spatial configuration M = f(d), with d being the distance to the kth (typically k = 4 or 5) nearest seismic station at each node in space. Although widely used, the BMC method has several critical shortcomings that have long been neglected. In this study, we propose a hierarchical Bayesian model that inherently overcomes these shortcomings of the BMC method for high-resolution M mapping coined hierarchical Bayesian magnitude of completeness (H-BMC), which provides a unified and more appropriate approach to the integration of a priori information and local observations concerning M. We use an earthquake catalog from the Taiwan region to demonstrate that, compared with the FMD-based methods based solely on observed magnitudes, the proposed H-BMC method effectively utilizes a priori information via prior distributions and thereby gives complete and more reliable high-resolution M mapping in terms of gap filling and uncertainty reduction. We also highlight that the H-BMC method for M mapping serves as a generic and flexible modeling framework for logically combining imprecise information about M from different sources.
First ; Corresponding
National Natural Science Foundation of China[U2039202]
|EI Accession Number|
Bayesian Networks ; Earthquakes
|ESI Classification Code|
Surveying:405.3 ; Seismology:484 ; Combinatorial Mathematics, Includes Graph Theory, Set Theory:921.4
|ESI Research Field|
Cited Times [WOS]:0
|Document Type||Journal Article|
|Department||Academy for Advanced Interdisciplinary Studies|
1.Institute of Risk Analysis,Prediction and Management (Risks-X),Academy for Advanced Interdisciplinary Studies,Southern University of Science and Technology,Shenzhen,China
2.Department of Earth and Space Sciences,Southern University of Science and Technology,Shenzhen,China
3.Department of Management,Technology and Economics,Swiss Federal Institute of Technology (ETH) Zürich,Zürich,Switzerland
|First Author Affilication||Academy for Advanced Interdisciplinary Studies; Department of Earth and Space Sciences|
|Corresponding Author Affilication||Academy for Advanced Interdisciplinary Studies; Department of Earth and Space Sciences|
|First Author's First Affilication||Academy for Advanced Interdisciplinary Studies|
Feng，Yu,Mignan，Arnaud,Sornette，Didier,et al. Hierarchical Bayesian Modeling for Improved High-Resolution Mapping of the Completeness Magnitude of Earthquake Catalogs[J]. SEISMOLOGICAL RESEARCH LETTERS,2022,93(4):2126-2137.
Feng，Yu,Mignan，Arnaud,Sornette，Didier,&Li，Jiawei.(2022).Hierarchical Bayesian Modeling for Improved High-Resolution Mapping of the Completeness Magnitude of Earthquake Catalogs.SEISMOLOGICAL RESEARCH LETTERS,93(4),2126-2137.
Feng，Yu,et al."Hierarchical Bayesian Modeling for Improved High-Resolution Mapping of the Completeness Magnitude of Earthquake Catalogs".SEISMOLOGICAL RESEARCH LETTERS 93.4(2022):2126-2137.
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