Full-waveform centroid moment tensor inversion of passive seismic data acquired at the reservoir scale. Issue 3 (13th April 2022)

• Record Type: Journal Article
• Authors: Liu, Qiancheng; Waheed, Umair bin; Borisov, Dmitry; Simons, Frederik J; Gao, Fuchun; Williamson, Paul
• Is Part Of: Geophysical journal international. Volume 230:Issue 3(2022)
• Page Start: 1725
• Page End: 1750
• Publication Date: 2022-04-13
• Subjects: computational seismology -- waveform inversion -- earthquake source parameters; Geophysics -- Periodicals; 550
• DOI: 10.1093/gji/ggac137
• Languages: English
• ISSNs: 0956-540X

Abstract

SUMMARY: Passive seismic inversion at the reservoir scale offers the advantages of low cost, negligible environmental impact and the ability to probe a target area with low-frequency energy not afforded by even the most modern active-source seismic technology. In order to build starting models suitable for full-waveform wave speed tomography, characterization of earthquake sources is an indispensable first step. We present a workflow for the centroid moment tensor (CMT) inversion of seismic events identified in a passive seismic data set acquired by a large and dense array of three-component broad-band seismic sensors in a mountainous setting in the Himalayan foothills. The data set comprised 256 instruments operating for 2×4 months over an area of 8000 km 2 . An initial 3-D wave speed model was determined for the region via the analysis of first-arriving traveltime picks. Of the 2607 identified seismic events that were well recorded at frequencies between 0.2–50 Hz, 86 with magnitudes 1.3 ≤ M ≤ 3.0 initially had their CMT focal mechanisms determined by a waveform fitting procedure built on a Green's function approach in a 1-D layered average wave speed model, for stations within an offset of 10 km, in the frequency range 0.2–1.4 Hz. Here, we obtain updated CMT mechanisms for the 86 events in that catalogue via multicomponent full-waveform inversion in the 3-D wave speed model. Our workflow includes automated data- and model-driven data selection using a combination of different metrics derived from signal-to-noise considerations and waveform-fitting criteria, and relies upon spectral-element simulations of elastic wave propagation in the 3-D wave speed model, honouring topography. Starting from the initial CMT solutions, we seek improvement to the data fit within the frequency band 0.5–2.5 Hz by minimizing the waveform difference between observed and synthetic data, while accommodating wave speed-model errors by allowing for small time-shifts. We balance uneven data coverage and tune their contributions via data-space weighting functions. We quantify the improvements to the data fit in terms of different metrics. We summarize the changes to the CMT solutions, and present and analyse the resulting catalogue for the region, including their breakdown into double-couple and non-double couple components, and their relation to mapped faults.