Arrival times of seismic phases contribute substantially to the study of the inner working of the Earth. Despite great advances in seismic data collection, the usage of seismic arrival times is still insufficient because of the overload manual picking tasks for human experts. In this work we employ a deep‐learning method (PickNet) to automatically pick much more P and S wave arrival times of local earthquakes with a picking accuracy close to that by human experts, which can be used directly to determine seismic tomography. A large number of high‐quality seismic arrival times obtained with the deep‐learning model may contribute greatly to improve our understanding of the Earth's interior structure.

Accurate P-wave first-motion-polarity (FMP) information can contribute to solving earthquake focal mechanisms, especially for small earthquakes, to which waveform-based methods are generally inapplicable due to the computationally expensive high-frequency waveform simulations and inaccurate velocity models. In this paper, we propose a deep-learning-based method for the automatic determination of the FMPs, named “DiTingMotion”. DiTingMotion was trained with the P-wave FMP labels from the “DiTing” and SCSN-FMP datasets, and it achieved ∼97.8% accuracy on both datasets. The model maintains ∼83% accuracy on data labeled as “Emergent”, of which the FMP labels are challenging to identify for seismic analysts. Integrated with HASH, we developed a workflow for automated focal mechanism inversion using the FMPs identified by DiTingMotion and applied it to the 2019 M 6.4 Ridgecrest earthquake sequence for performance evaluation. In this case, DiTingMotion yields comparable focal mechanism results to that using manually determined FMPs by SCSN on the same data. The results proved that the DiTingMotion has a good generalization ability and broad application prospect in rapid earthquake focal mechanism inversion.

Optimized Deep Learning (DL)-based workflows can improve the efficiency and accuracy of earthquake detection and location processes. This paper introduces a six-step automated event detection, phase association, and earthquake location workflow, which integrates the state-of-the-art Pair-Input DL model and waveform Migration Location methods (IPIML). Applying IPIML on an 18-months dataset of Ghana Digital Seismic Network (GHSDN) recorded from 2012-2014, a catalog with 461 events is automatically obtained. Compared to other DL catalogs obtained using EQTransformer (EQT) and Siamese EQTransformer (S-EQT), the seismic event clusters in the IPIML catalog focus more on tectonically active regions or known seismogenic source areas and show a consistent depth distribution. The compiled catalog is 6.3 times larger than the reported catalog obtained by applying EQT with the default settings, indicating the importance of optimization and hyper-parameter tuning when applying DL models. As a result, a previously unknown seismogenic fault with a clear spatial trend has been identified using the new IPIML catalog, which provides more insights into the fault activities and seismic hazards in the region. The IPIML codes and data sets are available at the GitHub repository https://github.com/SigProSeismology/IPIML.git, contributing to the geoscience community.

An approximate 236‐year interval in major seismic activity near the southern Xianshuihe fault terminated on 5 September 2022 until a destructive M6.8 Luding earthquake. The strike‐slip mainshock, accompanied by potent normal‐faulting aftershocks, fell short of the previously anticipated M7+ event. Utilizing deep‐learning and source inversion techniques, we analyzed dense near‐field seismograms to examine detailed fault structures and rupture characteristics. Our high‐quality relocated catalog with 7,388 events revealed that the earthquake ruptured an unmapped multiscale fault network. The analysis of the mainshock and 43 M_L ≥ 2.8 aftershocks suggests normal‐faulting events relate to the vertical movement of the Gongga Mountain. The sequence length, seismic gaps, and asperities derived from seismicity and waveform modeling imply that the rupture of the M6.8 quake was incomplete, indicating a high risk of a future M6+ event at the northern Moxi segment. These findings hold crucial implications for assessing future seismic hazards in this region.

P-wave first-motion polarity is important for the inversion of earthquake focal mechanism solutions. The focal mechanism solution can further contribute to our understanding of the source rupture process, the fault structure, and the regional stress field characteristics. By using the abundant focal mechanism solutions of small and moderate earthquakes, we can deepen our understanding of fault geometry and the seismogenic environment. In this paper, we propose an automatic workflow, FocMech-Flow (Focal Mechanism-Flow), for identifying P-wave first-motion polarity and focal mechanism inversion with deep learning and applied it to the 2021 Yangbi earthquake sequence. We use a deep learning model named DiTingMotion to detect the P-wave first-motion polarity of 2389 waveforms, resulting in 98.49% accuracy of polarity discrimination compared with human experts. The focal mechanisms of 112 earthquakes are obtained by using the CHNYTX program, which is 3.7 times more than that of the waveform inversion method, and the results are highly consistent. The analysis shows that the focal mechanisms of the foreshock sequence of the Yangbi earthquake are highly consistent and are all of the strike-slip type; the focal mechanisms of the aftershock sequence are complex, mainly the strike-slip type, but there are also reverse and normal fault types. This study shows that the deep learning method has high reliability in determining the P-wave first-motion polarity, and FocMech-Flow can obtain a large number of focal mechanism solutions from small and moderate earthquakes, having promising application in fine-scale stress inversion.

Myanmar bears a high risk of destructive earthquakes, yet detailed seismicity catalogs are rare. We designed a deep‐learning‐based data processing pipeline and applied it to the data recorded by a large‐aperture (∼400 km) seismic array in central Myanmar to produce a high‐resolution earthquake catalog. We precisely located 1891 earthquakes at shallow (<50 km) depth, a 2‐fold increase compared to the traditional procedures. The new catalog reveals the Kabaw Fault seismicity disappears south of ∼22.8°N, where the deeper (20–40 km) seismicity appears west of the southern Kabaw Fault. Such seismicity contrast along the strike of the Kabaw Fault possibly implies an along‐strike change of deformation responses to the shortening process by the India plate oblique subduction. The middle segment of the Sagaing Fault is likely locked and prone to hosting large earthquakes according to the derived low b‐value.