EPP

Seismic hazard assessment and risk mitigation depend critically on rapid analysis and characterization of earthquake sequences. Increasing seismicity in shale gas blocks of the Sichuan Basin, China, has presented a serious challenge to monitoring and managing the seismicity itself. In this study, to detect events we apply a machine-learning-based phase picker (PhaseNet) to continuous seismic data collected between November 2015 and November 2016 from a temporary network covering the Weiyuan Shale Gas Blocks (SGB). Both P- and S-phases are picked and associated for location. We refine the velocity model by using detected explosions and earthquakes and then relocate the detected events using our new velocity model. Our detections and absolute relocations provide the basis for building a high-precision earthquake catalog. Our primary catalog contains about 60 times as many earthquakes as those in the catalog of the Chinese Earthquake Network Center (CENC), which used only the sparsely distributed permanent stations. We also measure the local magnitude and achieve magnitude completeness of M_L0. We relocate clusters of events, showing sequential migration patterns overlapping with horizontal well branches around several well pads in the Wei202 and Wei204 blocks. Our results demonstrate the applicability of a machine-learning phase picker to a dense seismic network. The algorithms can facilitate rapid characterization of earthquake sequences.

Anthropogenic induced seismicity has been widely reported and investigated in many regions, including the shale gas fields in the Sichuan basin, where the frequency of earthquakes has increased substantially since the commencement of fracking in late 2014. However, the details of how earthquakes are induced remain poorly understood, partly due to lack of high-resolution spatial-temporal data documenting the evolution of such seismic events. Most previous studies have been based on a diffusive earthquake catalog constructed by routine methods. Here, however, we have constructed a high resolution catalog using a machine learning detector and waveform cross-correlation. Despite limited data, this new approach has detected one-third more earthquakes and improves the magnitude completeness of the catalog, illuminating the comprehensive spatial-temporal migration of the emerging seismicity in the target area. One of the clusters clearly delineates a potential unmapped fault trace that may have led to the M_w 5.2 in September 2019, by far the largest earthquake recorded in the region. The migration of the seismicity also demonstrates a pore-pressure diffusion front, suggesting additional constraints on the inducing mechanism of the region. The patterns of the highly clustered seismicity reconcile the causal link between the emerging seismicity and the activity of hydraulic fracturing in the region, facilitating continued investigation of the mechanisms of seismic induction and their associated risks.

The Anninghe fault is a large left-lateral strike-slip fault in southwestern China. It has controlled deposition and magmatic activities since the Proterozoic, and seismic activity occurs frequently. The Mianning−Xichang segment of the Anninghe fault is a seismic gap that has been locked by high stress. Many studies suggest that this segment has great potential for large earthquakes (magnitude >7). We obtained three vertical velocity profiles of the Anninghe fault (between Mianning and Xichang) based on the inversion of P-wave first arrival times. The travel time data were picked from seismograms generated by methane gaseous sources and recorded by three linearly distributed across-fault dense arrays. The inversion results show that the P-wave velocity structures at depths of 0−2 km corresponds well with the local lithology. The Quaternary sediments have low seismic velocities, whereas the igneous rocks, metamorphic rocks, and bedrock have high seismic velocities. We then further discuss the fault activities of the two fault branches of the Anninghe fault in the study region based on small earthquakes (magnitudes between $ {M}_{L} $ 0.5 and $ {M}_{L} $ 2.5) detected by the Xichang array. The eastern fault branch is more active than the western branch and that the fault activities in the eastern branch are different in the northern and southern segments at the border of 28°21′N. The high-resolution models obtained are essential for future earthquake rupture simulations and hazard assessments of the Anninghe fault zone. Future studies of velocity models at greater depths may further explain the complex fault activities in the study region.