Kurama Okubo

and 2 more

We monitored the time history of the velocity change (dv/v) from 2002 to 2022 to investigate temporal changes in the physical state near the Parkfield Region of the San Andreas Fault throughout the interseismic period. Following the coseismic decrease in dv/v caused due to the 2003 San Simeon and the 2004 Parkfield earthquakes, the dv/v heals logarithmically and shows a net long-term increase in which the current dv/v level is equivalent to, or exceeding, the value before the 2003 San Simeon earthquake. We investigated this long-term trend by fitting the model accounting for the environmental and coseismic effects to the channel-weighted dv/v time series. We confirmed with the metrics of AIC and BIC that the additional term of either a linear trend term, or a residual healing term for the case where the healing had not been completed before the San Simeon earthquake occurred, robustly improved the fit to the data. We eventually evaluated the sensitivity of the dv/v time history to the GNSS-derived strain field around the fault. The cumulative dilatational strain spatially averaged around the seismic stations shows a slight extension, which is opposite to what would be expected for an increase in dv/v. However, the cumulative rotated axial strain shows compression in a range near the maximum contractional horizontal strain (azimuth of N35°W to N45°E), suggesting that the closing of pre-existing microcracks aligned perpendicular to the axial contractional strains would be a candidate to cause the long-term increase observed in the multiple station pairs.

Xiaotao Yang

and 5 more

Cross-correlations of ambient seismic noise are widely used for seismic velocity imaging, monitoring, and ground motion analyses. A typical step in analyzing Noise Cross-correlation Functions (NCFs) is stacking short-term NCFs over longer time periods to increase the signal quality. Spurious NCFs could contaminate the stack, degrade its quality, and limit its use. Many methods have been developed to improve the stacking of coherent waveforms, including earthquake waveforms, receiver functions, and NCFs. This study systematically evaluates and compares the performance of eight stacking methods, including arithmetic mean or linear stacking, robust stacking, selective stacking, cluster stacking, phase-weighted stacking, time-frequency phase-weighted stacking, $N^{th}$-root stacking, and averaging after applying an adaptive covariance filter. Our results demonstrate that, in most cases, all methods can retrieve clear ballistic or first arrivals. However, they yield significant differences in preserving the phase and amplitude information. This study provides a practical guide for choosing the optimal stacking method for specific research applications in ambient noise seismology. We evaluate the performance using multiple onshore and offshore seismic arrays in the Pacific Northwest region. We compare these stacking methods for NCFs calculated from raw ambient noise (referred to as Raw NCFs) and from ambient noise normalized using a one-bit clipping time normalization method (referred to as One-bit NCFs). We evaluate six metrics, including signal-to-noise ratios, phase dispersion images, convergence rate, temporal changes in the ballistic and coda waves, relative amplitude decays with distance, and computational time. We show that robust stacking is the best choice for all applications (velocity tomography, monitoring, and attenuation studies) using Raw NCFs. For applications using One-bit NCFs, all methods but phase-weighted, time-frequency phase-weighted, and $N^{th}$-root stacking are good choices for seismic velocity tomography. Linear, robust, and selective stacking methods are all equally appropriate choices when using One-bit NCFs for monitoring applications. For applications relying on accurate relative amplitudes, both the robust and cluster stacking methods perform well with One-bit NCFs. The evaluations in this study can be generalized to a broad range of time-series analysis that utilizes data coherence to perform ensemble stacking. Another contribution of this study is the accompanying open-source software, which can be used for general purposes in time-series stacking.