Automated seismic waveform location using multichannel coherency migration (MCM)–I: theory. Issue 3 (29th March 2018)

• Record Type: Journal Article
• Authors: Shi, Peidong; Angus, Doug; Rost, Sebastian; Nowacki, Andy; Yuan, Sanyi
• Is Part Of: Geophysical journal international. Volume 216:Issue 3(2019)
• Page Start: 1842
• Page End: 1866
• Publication Date: 2018-03-29
• Subjects: Time-series analysis -- Computational seismology -- Earthquake monitoring and test-ban treaty verification -- Earthquake source observations; Geophysics -- Periodicals; 550
• DOI: 10.1093/gji/ggy132
• Languages: English
• ISSNs: 0956-540X

Abstract

SUMMARY: With the proliferation of dense seismic networks sampling the full seismic wavefield, recorded seismic data volumes are getting bigger and automated analysis tools to locate seismic events are essential. Here, we propose a novel multichannel coherency migration (MCM) method to locate earthquakes in continuous seismic data and reveal the location and origin time of seismic events directly from recorded waveforms. By continuously calculating the coherencies between waveforms from different receiver pairs, MCM greatly expands the available information which can be used for event location. MCM does not require phase picking or phase identification, which allows fully automated waveform analysis. By migrating the coherency between waveforms, MCM leads to improved source energy focusing. We have tested and compared MCM to other migration-based methods in noise-free and noisy synthetic data. The tests and analysis show that MCM is noise resistant and can achieve more accurate results compared with other migration-based methods. MCM is able to suppress strong interference from other seismic sources occurring at a similar time and location. It can be used with arbitrary 3-D velocity models and is able to obtain reasonable location results with smooth but inaccurate velocity models. MCM exhibits excellent location performance and can be easily parallelized, giving it large potential to be developed as a real-time location method for very large data sets.