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Analysis of surface and seismic sources in dense array data with match field processing and Markov chain Monte Carlo sampling
- Source :
- Geophysical Journal International. 218:1044-1056
- Publication Year :
- 2019
- Publisher :
- Oxford University Press (OUP), 2019.
-
Abstract
- SUMMARY We introduce a methodology based on array processing to detect and locate weak seismic events in a complex fault zone environment. The method is illustrated using data recorded by a dense array of 1108 vertical component geophones in a 600 m × 600 m area on the Clark branch of the San Jacinto Fault. Because surface and atmospheric sources affect weak ground motion, it is necessary to discriminate them from weak seismic sources at depth. Source epicentral positions and associated apparent velocities are extracted from continuous seismic waveforms using Match Field Processing (MFP). We implement MFP at specific frequencies targeting surface and subsurface sources, using for computational efficiency a forward model of acoustic source in a homogenous medium and Markov Chain Monte Carlo sampling. Surface sources such as Betsy gun shots and a moving vehicle are successfully located. Weak seismic events are also detected outside of the array, and their backazimuth angle is retrieved and found to be consistent with the fault geometry. We also show that the homogeneous acoustic model does not yield satisfying results when extracting microseismic event depth, because of the ambiguity between depth and the apparent velocity based on surface data.
- Subjects :
- Surface (mathematics)
Dense array
010504 meteorology & atmospheric sciences
Field (physics)
Wave propagation
010502 geochemistry & geophysics
01 natural sciences
Computational physics
Geophysics
Geochemistry and Petrology
Markov chain monte carlo sampling
Geology
0105 earth and related environmental sciences
Computational seismology
Subjects
Details
- ISSN :
- 1365246X and 0956540X
- Volume :
- 218
- Database :
- OpenAIRE
- Journal :
- Geophysical Journal International
- Accession number :
- edsair.doi...........d760a4d4cf06e3312f98931dfe8d6865