We developed an earthquake detection and location architecture for continuous seismograms that incorporates phase picking, phase association, location, and matched‐filter techniques (PALM). The PALM architecture incorporates two modules: (1) PAL, the initial detection following picking, association, and location processes, and (2) match, expand, shift, and stack (MESS), a matched‐filter detector that augments the template catalog. The effectiveness of PALM is demonstrated in building an early aftershock catalog for the 2019 Ridgecrest, California, earthquake. By comparing with Southern California Seismic Network arrival times, we show that the PAL picker combines the strengths of short‐term average/long‐term average and the kurtosis picker, realizing robust phase detection and precise picking. Our final MESS catalog is compared with two other matched‐filter catalogs by Ross, Idini, et al. (2019) and Shelly (2020). We find that PALM directly recovers unbiased and detailed features in seismicity from continuous seismograms, which can be efficiently implemented to scan continuous waveforms without the need for visual inspection.

The $Ms$ 6.0 earthquake in Changning, Sichuan, China, on 17 June 2019 was the largest recorded earthquake in the stable Sichuan basin. It occurred in a complicated region with salt mining and shale gas production. Whether this earthquake is induced raises concerns among the public and the scientific community. Furthermore, the relation between this earthquake and nearby industrial activities has also been of great interest. To address these questions, we estimated the nonstationary background seismicity rate and inverted for spatiotemporal stress changes. The results show that the background rate dramatically increased after hydraulic fracturing (HF) and remained at a high level until the present. Starting in 2005, the study region experienced an accelerating stress increase, and the rates of cumulative modified Coulomb stress changes were approximately $0.11MPa/yr$ from January 2005 to January 2015 and $0.24MPa/yr$ from January 2015 to December 2018. The 2019 Changning earthquake produced a stress step of 0.32 MPa. A clear difference between seismicity induced by salt mine injection and by HF is documented. Our results suggest that the Changning sequence might have been induced by long‐term injection for salt production. Furthermore, the seismicity‐stress inversion method provides a tool for using seismicity rate changes as a stress meter to monitor human‐induced seismicity.

Earthquake prediction is a long-standing problem in seismology that has garnered attention from the scientific community and the public. Despite ongoing efforts to understand the physical mechanisms of earthquake occurrence, there is no convincing physical or statistical model for predicting large earthquakes. Machine learning methods, such as random forest and long short-term memory (LSTM) neural networks, excel at identifying patterns in large-scale databases and offer a potential means to improve earthquake prediction performance. Differing from physical and statistical approaches to earthquake prediction, we explore whether small earthquakes can be used to predict large earthquakes within the framework of machine learning. Specifically, we attempt to answer two questions for a given region: (1) Is there a likelihood of a large earthquake (e.g., M ≥ 6.0) occurring within the next year? (2) What is the maximum magnitude of an earthquake expected to occur within the next year? Our results show that the random forest method performs best in classifying large earthquake occurrences, while the LSTM method provides a rough estimation of earthquake magnitude. We conclude that small earthquakes contain information relevant to predicting future large earthquakes and that machine learning provides a promising avenue for improving the prediction of earthquake occurrences.

The 2017 Mw 6.5 Jiuzhaigou earthquake occurred at the southeastern boundary of the Bayankala block in Tibetan Plateau. Here we investigate the spatial and temporal seismicity rate changes in the Jiuzhaigou region using the finite‐source epidemic‐type aftershock sequence model and stochastic declustering method. The background probabilities of all events are obtained, and the cumulative background seismicity is extracted and fitted by a linear function. Following from this, we argue that there was a decrease of background seismicity in Jiuzhaigou region immediately after the occurrence of the 2008 Mw 7.9 Wenchuan earthquake. On the other hand, we also calculate the static and viscoelastic stress changes in the area around the hypocenter of the Jiuzhaigou earthquake induced by the Wenchuan earthquake. Although opposite results are resolved by previous studies, we infer a negative Coulomb failure stress change under reasonable assumptions of parameters for this case. Combining these two observed empirical relationships, we conclude that the 2017 Jiuzhaigou earthquake is delayed by the Wenchuan earthquake and the former event possibly results from the southeastward movement of the Bayankala block, which in turn arises from the collision of Indian‐Australian Plate and the Eurasian Plate. These findings indicate that Bayankala block and its adjacent tectonic faults are still in the highly active seismic period that started in the 1990s.