A deep Gaussian Process model for seismicity rates
The spatio-temporal properties of seismicity give us incisive insight into the stress state evolution and fault structures of the crust. Empirical models based on self-exciting point-processes continue to provide an important tool for analyzing seismicity, given the epistemic uncertainty associated...
| Hlavní autoři: | , |
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| Médium: | Journal article |
| Jazyk: | English |
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Oxford University Press
2023
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| _version_ | 1826309741845938176 |
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| author | Muir, J Zachary, R |
| author_facet | Muir, J Zachary, R |
| author_sort | Muir, J |
| collection | OXFORD |
| description | The spatio-temporal properties of seismicity give us incisive insight into the stress state evolution and fault structures of the crust. Empirical models based on self-exciting point-processes continue to provide an important tool for analyzing seismicity, given the epistemic uncertainty associated with physical models. In particular, the epidemic-type aftershock sequence (ETAS) model acts as a reference model for studying seismicity catalogs. The traditional ETAS model uses simple parametric definitions for the background rate of triggering-independent seismicity. This reduces the effectiveness of the basic ETAS model in modelling the temporally complex seismicity patterns seen in seismic swarms that are dominated by aseismic tectonic processes such as fluid injection rather than aftershock triggering. In order to robustly capture time varying seismicity rates, we introduce a deep Gaussian process formulation for the background rate as an extension to ETAS. Gaussian processes (GPs) are a robust non-parametric model for function spaces with covariance structure. By conditioning the lengthscale structure of a GP with another GP, we have a deep-GP: a probabilistic, hierarchical model that automatically tunes its structure to match data constraints. We show how the deep-GP-ETAS model can be efficiently sampled by making use of a Metropolis-within-Gibbs scheme, taking advantage of the branching process formulation of ETAS and a stochastic partial differential equation (SPDE) approximation for Mat´ern GPs. We illustrate our method using synthetic examples, and show that the deep-GP-ETAS model successfully captures multiscale temporal behavior in the background forcing rate of seismicity. We then apply the results to two real-data catalogues: the Ridgecrest, CA July 5 2019 Mw 7.1 event catalogue, showing that deep-GP-ETAS can successfully characterize a classical aftershock sequence; and the 2016–2019 Cahuilla, CA earthquake swarm, which shows two distinct phases of aseismic forcing concordant with a fluid injection driven initial sequence, arrest of the fluid along a physical barrier, and release following the largest Mw 4.4 event of the sequence. |
| first_indexed | 2024-03-07T07:40:15Z |
| format | Journal article |
| id | oxford-uuid:f93fabfc-a684-46b4-99b7-f438fd6dd2d9 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T07:40:15Z |
| publishDate | 2023 |
| publisher | Oxford University Press |
| record_format | dspace |
| spelling | oxford-uuid:f93fabfc-a684-46b4-99b7-f438fd6dd2d92023-04-14T09:15:42ZA deep Gaussian Process model for seismicity ratesJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:f93fabfc-a684-46b4-99b7-f438fd6dd2d9EnglishSymplectic ElementsOxford University Press2023Muir, JZachary, RThe spatio-temporal properties of seismicity give us incisive insight into the stress state evolution and fault structures of the crust. Empirical models based on self-exciting point-processes continue to provide an important tool for analyzing seismicity, given the epistemic uncertainty associated with physical models. In particular, the epidemic-type aftershock sequence (ETAS) model acts as a reference model for studying seismicity catalogs. The traditional ETAS model uses simple parametric definitions for the background rate of triggering-independent seismicity. This reduces the effectiveness of the basic ETAS model in modelling the temporally complex seismicity patterns seen in seismic swarms that are dominated by aseismic tectonic processes such as fluid injection rather than aftershock triggering. In order to robustly capture time varying seismicity rates, we introduce a deep Gaussian process formulation for the background rate as an extension to ETAS. Gaussian processes (GPs) are a robust non-parametric model for function spaces with covariance structure. By conditioning the lengthscale structure of a GP with another GP, we have a deep-GP: a probabilistic, hierarchical model that automatically tunes its structure to match data constraints. We show how the deep-GP-ETAS model can be efficiently sampled by making use of a Metropolis-within-Gibbs scheme, taking advantage of the branching process formulation of ETAS and a stochastic partial differential equation (SPDE) approximation for Mat´ern GPs. We illustrate our method using synthetic examples, and show that the deep-GP-ETAS model successfully captures multiscale temporal behavior in the background forcing rate of seismicity. We then apply the results to two real-data catalogues: the Ridgecrest, CA July 5 2019 Mw 7.1 event catalogue, showing that deep-GP-ETAS can successfully characterize a classical aftershock sequence; and the 2016–2019 Cahuilla, CA earthquake swarm, which shows two distinct phases of aseismic forcing concordant with a fluid injection driven initial sequence, arrest of the fluid along a physical barrier, and release following the largest Mw 4.4 event of the sequence. |
| spellingShingle | Muir, J Zachary, R A deep Gaussian Process model for seismicity rates |
| title | A deep Gaussian Process model for seismicity rates |
| title_full | A deep Gaussian Process model for seismicity rates |
| title_fullStr | A deep Gaussian Process model for seismicity rates |
| title_full_unstemmed | A deep Gaussian Process model for seismicity rates |
| title_short | A deep Gaussian Process model for seismicity rates |
| title_sort | deep gaussian process model for seismicity rates |
| work_keys_str_mv | AT muirj adeepgaussianprocessmodelforseismicityrates AT zacharyr adeepgaussianprocessmodelforseismicityrates AT muirj deepgaussianprocessmodelforseismicityrates AT zacharyr deepgaussianprocessmodelforseismicityrates |