The extraction of travel-time curve of seismic phase is very important for the subsequent inference of the structural properties of underground media in seismology. In recent years, with the increase in the amount of data, manual processing is facing significant challenges, and automatic signal processing has gradually become the mainstream. According to the similarity of array signals and considering the elimination of outliers, we propose an improved multi-channel cross-correlation method using the $L_1$ norm measure to obtain preliminary results, which builds on a new controllable measurement mode. Then, the post-correction step is carried out in combination with the signal gain property of beamforming technique. Based on these two methods, this paper proposes a new scheme of automatic arrival time picking. We apply the scheme to actual data to verify the effects of the two methods step by step. The entire scheme achieves fine results: direct water waves, seismic waves refracted by the crust and seismic waves reflected by the upper mantle are automatically detected. In addition, compared with the two traditional methods, the scheme proposed in this paper has a better overall effect and a reasonable computation cost.

Seismic phase picking is a critical task for earthquake detection and localization, where traditional methods rely on manual parameter tuning and have great difficulty to capture complex temporal features. In this paper, we propose PhaseMamba, an automated seismic phase picking and detection model that leverages deep learning through a U-shaped architecture with skip connections for effective time-domain seismic signal analysis, while incorporating a state-space Mamba model to enhance long-term contextual dependency extraction capabilities. For training, validation, and testing, we utilize the open-source global seismic dataset, STEAD (STanford EArthquake Dataset), which provides a diverse range of high-quality seismic waveforms. Comprehensive experiments are conducted on this dataset to evaluate the model’s performance. The results demonstrate that PhaseMamba achieves superior performance in P-wave arrival picking compared to all state-of-the-art models (PhaseNet, EQTransformer, and SeisT), while showing comparable or slightly lower performance in S-wave arrival picking. These findings suggest that PhaseMamba is a promising tool for advancing seismic phase picking and contributing to broader seismic research applications.

Earthquake location is of great significance for earthquake early warning and earthquake research. Accurately determining the location of earthquake clusters provides valuable insights into understanding fault zone distributions. The traditional methods require more than four seismic stations, but it can be alleviated by the master events. Therefore, we proposed a unified spherical interpolation (SI) location framework, which can realize earthquake cluster relocation according to different observation conditions (the number of available stations and master events). We have validated the efficacy of SI relative locator through its successful application to 2019 Ridgecrest earthquake and 2023 Jishishan earthquake within a sparse network featuring only two seismic stations and a known set of master events. Our results provide good initial results for horizontal locations and are well suited for subsequent insertions of ray-tracing techniques based on finer models to determine depths more accurately.