Microseismic event detection in large heterogeneous velocity models using Bayesian multimodal nested sampling
In passive seismic and microseismic monitoring, identifying and characterizing events in a strong noisy background is a challenging task. Most of the established methods for geophysical inversion are likely to yield many false event detections. The most advanced of these schemes require thousands of...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Cambridge University Press
2021-01-01
|
| Series: | Data-Centric Engineering |
| Subjects: | |
| Online Access: | https://www.cambridge.org/core/product/identifier/S2632673621000010/type/journal_article |
| _version_ | 1811156489757261824 |
|---|---|
| author | Saptarshi Das Michael P. Hobson Farhan Feroz Xi Chen Suhas Phadke Jeroen Goudswaard Detlef Hohl |
| author_facet | Saptarshi Das Michael P. Hobson Farhan Feroz Xi Chen Suhas Phadke Jeroen Goudswaard Detlef Hohl |
| author_sort | Saptarshi Das |
| collection | DOAJ |
| description | In passive seismic and microseismic monitoring, identifying and characterizing events in a strong noisy background is a challenging task. Most of the established methods for geophysical inversion are likely to yield many false event detections. The most advanced of these schemes require thousands of computationally demanding forward elastic-wave propagation simulations. Here we train and use an ensemble of Gaussian process surrogate meta-models, or proxy emulators, to accelerate the generation of accurate template seismograms from random microseismic event locations. In the presence of multiple microseismic events occurring at different spatial locations with arbitrary amplitude and origin time, and in the presence of noise, an inference algorithm needs to navigate an objective function or likelihood landscape of highly complex shape, perhaps with multiple modes and narrow curving degeneracies. This is a challenging computational task even for state-of-the-art Bayesian sampling algorithms. In this paper, we propose a novel method for detecting multiple microseismic events in a strong noise background using Bayesian inference, in particular, the Multimodal Nested Sampling (MultiNest) algorithm. The method not only provides the posterior samples for the 5D spatio-temporal-amplitude inference for the real microseismic events, by inverting the seismic traces in multiple surface receivers, but also computes the Bayesian evidence or the marginal likelihood that permits hypothesis testing for discriminating true vs. false event detection. |
| first_indexed | 2024-04-10T04:51:27Z |
| format | Article |
| id | doaj.art-5ae0b2d92dda49a59b0186cc89057d9e |
| institution | Directory Open Access Journal |
| issn | 2632-6736 |
| language | English |
| last_indexed | 2024-04-10T04:51:27Z |
| publishDate | 2021-01-01 |
| publisher | Cambridge University Press |
| record_format | Article |
| series | Data-Centric Engineering |
| spelling | doaj.art-5ae0b2d92dda49a59b0186cc89057d9e2023-03-09T12:31:48ZengCambridge University PressData-Centric Engineering2632-67362021-01-01210.1017/dce.2021.1Microseismic event detection in large heterogeneous velocity models using Bayesian multimodal nested samplingSaptarshi Das0https://orcid.org/0000-0002-8394-5303Michael P. Hobson1Farhan Feroz2Xi Chen3Suhas Phadke4Jeroen Goudswaard5Detlef Hohl6Cavendish Astrophysics Group, Department of Physics, University of Cambridge, Cambridge CB3 0HE, United Kingdom Department of Mathematics, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Penryn Campus, Cornwall TR10 9FE, United Kingdom Institute for Data Science and Artificial Intelligence, University of Exeter, Laver Building, North Park Road, Exeter, Devon EX4 4QE, United KingdomCavendish Astrophysics Group, Department of Physics, University of Cambridge, Cambridge CB3 0HE, United KingdomCavendish Astrophysics Group, Department of Physics, University of Cambridge, Cambridge CB3 0HE, United KingdomCavendish Astrophysics Group, Department of Physics, University of Cambridge, Cambridge CB3 0HE, United KingdomShell India Markets Pvt Ltd., Bengaluru, Karnataka 562149, IndiaShell India Markets Pvt Ltd., Bengaluru, Karnataka 562149, IndiaShell Global Solutions International BV, Grasweg 31, 1031 HW Amsterdam, The NetherlandsIn passive seismic and microseismic monitoring, identifying and characterizing events in a strong noisy background is a challenging task. Most of the established methods for geophysical inversion are likely to yield many false event detections. The most advanced of these schemes require thousands of computationally demanding forward elastic-wave propagation simulations. Here we train and use an ensemble of Gaussian process surrogate meta-models, or proxy emulators, to accelerate the generation of accurate template seismograms from random microseismic event locations. In the presence of multiple microseismic events occurring at different spatial locations with arbitrary amplitude and origin time, and in the presence of noise, an inference algorithm needs to navigate an objective function or likelihood landscape of highly complex shape, perhaps with multiple modes and narrow curving degeneracies. This is a challenging computational task even for state-of-the-art Bayesian sampling algorithms. In this paper, we propose a novel method for detecting multiple microseismic events in a strong noise background using Bayesian inference, in particular, the Multimodal Nested Sampling (MultiNest) algorithm. The method not only provides the posterior samples for the 5D spatio-temporal-amplitude inference for the real microseismic events, by inverting the seismic traces in multiple surface receivers, but also computes the Bayesian evidence or the marginal likelihood that permits hypothesis testing for discriminating true vs. false event detection.https://www.cambridge.org/core/product/identifier/S2632673621000010/type/journal_articleBayesian inference and evidenceDBSCAN clusteringmicroseismic event detectionnested samplingsurrogate meta-model |
| spellingShingle | Saptarshi Das Michael P. Hobson Farhan Feroz Xi Chen Suhas Phadke Jeroen Goudswaard Detlef Hohl Microseismic event detection in large heterogeneous velocity models using Bayesian multimodal nested sampling Data-Centric Engineering Bayesian inference and evidence DBSCAN clustering microseismic event detection nested sampling surrogate meta-model |
| title | Microseismic event detection in large heterogeneous velocity models using Bayesian multimodal nested sampling |
| title_full | Microseismic event detection in large heterogeneous velocity models using Bayesian multimodal nested sampling |
| title_fullStr | Microseismic event detection in large heterogeneous velocity models using Bayesian multimodal nested sampling |
| title_full_unstemmed | Microseismic event detection in large heterogeneous velocity models using Bayesian multimodal nested sampling |
| title_short | Microseismic event detection in large heterogeneous velocity models using Bayesian multimodal nested sampling |
| title_sort | microseismic event detection in large heterogeneous velocity models using bayesian multimodal nested sampling |
| topic | Bayesian inference and evidence DBSCAN clustering microseismic event detection nested sampling surrogate meta-model |
| url | https://www.cambridge.org/core/product/identifier/S2632673621000010/type/journal_article |
| work_keys_str_mv | AT saptarshidas microseismiceventdetectioninlargeheterogeneousvelocitymodelsusingbayesianmultimodalnestedsampling AT michaelphobson microseismiceventdetectioninlargeheterogeneousvelocitymodelsusingbayesianmultimodalnestedsampling AT farhanferoz microseismiceventdetectioninlargeheterogeneousvelocitymodelsusingbayesianmultimodalnestedsampling AT xichen microseismiceventdetectioninlargeheterogeneousvelocitymodelsusingbayesianmultimodalnestedsampling AT suhasphadke microseismiceventdetectioninlargeheterogeneousvelocitymodelsusingbayesianmultimodalnestedsampling AT jeroengoudswaard microseismiceventdetectioninlargeheterogeneousvelocitymodelsusingbayesianmultimodalnestedsampling AT detlefhohl microseismiceventdetectioninlargeheterogeneousvelocitymodelsusingbayesianmultimodalnestedsampling |