Moment Tensor Inversion Based on the Principal Component Analysis of Waveforms: Method and Application to Microearthquakes in West Bohemia, Czech Republic

Abstract

We develop and test a new hybrid approach of the amplitude and waveform moment tensor inversions, which utilizes the principal component analysis of seismograms. The proposed inversion is less sensitive to noise in data, being thus more accurate and more robust than the amplitude inversion. It also suppresses other unmodeled phenomena, like a directivity of the source, errors caused by local site effects at individual stations, and by time shifts in arrivals of observed and synthetic signals due to an inaccurate velocity model. This inversion is computationally less demanding than the full waveform inversion and thus applicable to large sets of earthquakes. The approach is numerically tested on synthetic data with various levels of noise. The applicability of the inversion is demonstrated on inverting more than 800 microearthquakes that occurred during the 2014 activity in West Bohemia, Czech Republic. The analysis revealed several distinct clusters of moment tensors. Focal mechanisms corresponding to moment tensors of three clusters are left-lateral strike slips associated with the most active fault in the focal zone. Another cluster is characterized by right-lateral strike slips associated with the fault conjugate to the main fault. Finally, we identified a cluster with pure reverse focal mechanisms that are anomalous and not expected to occur in the region. These mechanisms were not detected in previous seismic activity, and they have an unfavorable orientation with respect to regional tectonic stress. This might indicate a presence of local stress heterogeneities caused, for example, by an interaction of faults or fault segments in the focal zone.

Frequency‐Dependent Moment Tensors of Induced Microearthquakes

“…This causes a variation of the signal-to-noise (S/N) ratio with frequency (Figures 2b and S3 in the supporting information). According to the synthetic tests of the PCA-based MT inversion ( Figure 5 in Vavryčuk et al, 2017), such levels of noise (~1-0.01%) can only result in variations of DC, ISO, and CLVD components less than 0.1%, which are negligible with respect to the observations in this study. According to the synthetic tests of the PCA-based MT inversion ( Figure 5 in Vavryčuk et al, 2017), such levels of noise (~1-0.01%) can only result in variations of DC, ISO, and CLVD components less than 0.1%, which are negligible with respect to the observations in this study.…”

“…The P wave amplitudes extracted from the broadband waveform data using the principal component analysis (PCA) method were inverted for MTs (Vavryčuk et al, 2017). Moreover, this approach proves to be computationally very efficient (Vavryčuk et al, 2017). In comparison with the waveform inversion, it is less sensitive to inaccuracies in the velocity model and noise in the data.…”

“…Such type of noise is, however, very effectively suppressed by the PCA-based MT inversion (see Vavryčuk et al, 2017). This could also bias the result of MTs in different frequency bands.…”

Moment Tensors of Induced Microearthquakes in The Geysers Geothermal Reservoir From Broadband Seismic Recordings: Implications for Faulting Regime, Stress Tensor, and Fluid Pressure

“…Calculating accurate MTs of microearthquakes is challenged by a large number of events, complex waveforms, and a relatively low S/N ratio. To address these issues, a PCA‐based MT inversion was developed to estimate the MTs of large microseismic data sets (Vavryčuk et al, ). The key step in this approach is to automatically calculate the effective P wave amplitudes using the PCA.…”

“…In the broadband data, the low‐frequency components are originally recorded, which allow us to extract more accurate P wave amplitudes in a broader frequency range. (2) The MT inversion based on the principal component analysis (PCA) was recently developed and successfully applied to microearthquakes (Vavryčuk et al, ). The robustness and high computational efficiency of the method make it feasible to create a large and reliable MT catalog using microseismic data with a rather low signal‐to‐noise (S/N) ratio and with complex waveforms.…”