In this paper, the time series classification frontier method MiniRocket was used to classify earthquakes, blasts, and background noise. From supervised to unsupervised classification, a comprehensive analysis was carried out, and finally, the supervised method achieved excellent results. The relatively simple model, MiniRocket, is only a one-dimensional convolutional neural network structure which has achieved the best comprehensive results, and its computational efficiency is far stronger than other supervised classification methods. Through our experimental results, we found that the MiniRocket model could well-extract the decisive features of the seismic sensing signal. In order to try to eliminate the tedious work of making data labels, we proposed a novel lightweight collaborative learning for seismic sensing signals (LCL-SSS) based on the method of feature extraction in MiniRocket combined with unsupervised classification. The new method gives new vitality to the unsupervised classification method that could not be used originally and opens up a new path for the unsupervised classification of seismic sensing signals.

To obtain an accurate one-dimensional velocity model, we developed the EA_VELEST method based on the evolutionary algorithm and the VELEST program. This method can quickly generate a suitable 1D velocity model and finally input it into the 3D velocity inversion process using the TomoDD method. We adopt TomoDD methods to inverse the high-resolution three-dimension velocity structure and relative earthquake hypocenters for this sequence. This system processing flow was applied to the Sichuan Maerkang earthquake swarm in 2022. By collecting the seismic phase data of the Maerkang area between 1 January 2009 and 15 June 2022, we relocated the historical earthquakes in the area and obtained accurate 3D velocity imaging results. The relocated hypocenters reveal a SE-trending secondary fault, which is located ~5 km NW of the Songgang fault. In the first ten-hour of the sequence, events clearly down-dip migrated toward the SE direction. The inverted velocity structure indicates that the majority of earthquakes during the sequence occurred along the boundaries of the high and low-velocity zones or high and low-V_P/V_S anomalies. Especially both the two largest earthquakes, M_S 5.8 and M_S 6.0, occurred at the discontinuities of high and low-velocity zones. The EA_VELEST method proposed in this paper is a novel method that has played a very good enlightenment role in the optimization of the one-dimensional velocity model in geophysics and has certain reference significance. The 3D velocity results obtained in this paper and the analysis of tectonic significance provide a reference for the seismogenic environment of this Maerkang earthquake and the deep 3D velocity of the Ganzi block.

This study investigates spatiotemporal b value variations and seismic interaction networks preceding the M_wg 7.1 Dingri earthquake that struck southern Tibet on 7 January 2025. Using relocated earthquake catalogs (2021–2025) and dual-method analysis combining b value mapping with Granger causality network modeling, we reveal systematic precursory patterns. Spatial analysis shows that the most significant b value reduction (Δb > 0.5) occurred north of the mainshock epicenter at seismogenic depths (5–15 km), closely aligning with subsequent aftershock concentration zones. Granger causality analysis reveals a progressive network simplification: from 73 causal links among 28 nodes during the background period (2021–2023) to 49 links among 34 nodes pre-mainshock (2023–2025) and finally to 6 localized links post-rupture. This transition from distributed system-wide interactions to localized “locked-in” dynamics reflects the stress concentration onto the primary asperity approaching critical failure. The convergence of b value anomalies and network evolution provides a comprehensive framework linking quasi-static stress states with dynamic system behavior. These findings offer valuable insights for understanding earthquake nucleation processes and improving seismic hazard assessment in the Tibetan Plateau and similar complex tectonic environments.

Precise earthquake locations and InSAR (Interferometric Synthetic Aperture Radar) deformation observation are the major methods to understand the earthquake occurrence and disaster-causing process. This paper proposes a processing framework for analyzing strong earthquake mechanisms from one-dimensional velocity inversion to precise earthquake locations combined with InSAR deformation observation, and discusses earthquake-generating fault and dynamic mechanisms of tectonic deformation. We analyzed the Menyuan Ms 6.9 earthquake in 2022 and discuss the historical seismic activities and corresponding stress adjustment processes in the research region. To analyze and study the seismogenic structure and mechanism of the earthquake, we investigated the spatial and temporal distribution characteristics of the Menyuan earthquake sequence and analyzed the InSAR coseismic deformation field. We obtained the precise locations of the main shock and aftershocks and the coseismic InSAR deformation field of the main shock. It was confirmed that the Ms 6.9 earthquake was a shallow sinistral strike-slip earthquake, which led to the sequential activation of the Tuolaishan and Lenglongling faults. The main seismogenic fault of the mainshock was the northwestern end of the Lenglongling fault, and the earthquake rupture was segmented. It can be inferred that the earthquake was a stress-adjusted event triggered in the Qilian-Haiyuan tectonic belt caused by the northeasterly push of the Qinghai-Tibet Plateau. The risk of moderate to high earthquakes in the region remains high in the future, requiring enhanced seismic observations.