Accurate phase association and arrival time picking are pivotal for reliable microseismic event location and source characterization. However, the complexity of downhole microseismic wavefields, arising from heterogeneous subsurface structures, variable propagation paths, and ambient noise, poses significant challenges to conventional automatic picking methods, even when the signal-to-noise ratio (SNR) is moderate to high. Specifically, P-wave coda energy can obscure S-wave onsets analysis, and shear wave splitting can generate ambiguous arrivals. In this study, we propose a novel multi-channel arrival picking framework based on Constrained Dynamic Time Warping (CDTW) for phase identification and simultaneous P- and S-wave arrival estimation. The DTW algorithm aligns microseismic signals that may be out of sync due to differences in timing or wave velocity by warping the time axis to minimize cumulative distance. Time delay constraints are imposed to ensure physically plausible alignments and improve computational efficiency. Furthermore, we introduce a Multivariate CDTW approach to jointly process the three-component (3C) data, leveraging inter-component and inter-receiver arrival consistency across the entire downhole array. The method is validated against the Short-Term Average/Long-Term Average (STA/LTA) and waveform cross-correlation techniques using field data from a shale gas hydraulic fracturing. Results demonstrate that the proposed algorithm significantly enhances arrival time accuracy and inter-receiver consistency, particularly in scenarios involving P-wave coda interference and shear wave splitting.

Coal mining disrupts the stress equilibrium of the surrounding rock mass, and the rock mass cracks as a result of the changes in the stress field, releasing strain energy and causing microseismic events. Monitoring seismicitiy during coal mining is critical for ensuring safe production and preventing geological disasters. In this study, we deployed 29 surface seismic nodes above an underground coal mine in the Yulin region of Shaanxi Province, China, to monitor the mining operation for 665 hours. A large number of microseismic events have been detected from continuous monitoring data, and analyzed using event clustering, source location, and mechanism estimate. The results show that (1) the frequency and intensity of microseismic events are related to underground mining working conditions; (2) the temporal and spatial locations of the microseismic sources may be utilized for real-time tracking the location of the underground coal mining face; and (3) three rupture mechanisms of tension rupture, implosion rupture, and shear rupture reflect the triggering mechanisms of the coal pillar failure, roof breakage and movement and fault slip.

Seismic data acquired in the presence of mechanical vibrations or power facilities may be contaminated by strong interferences, significantly decreasing the data signal-to-noise ratio (S/N). Conventional methods, such as the notch filter and time-frequency transform method, are usually inadequate for suppressing non-stationary interference noises, and may distort effective signals if overprocessing. In this study, we propose a method for eliminating mechanical vibration interferences in seismic data. In our method, we extended the variational mode extraction (VME) technique to a multivariate form, called multivariate variational mode extraction (MVME), for synchronous analysis of multitrace seismic data. The interference frequencies are determined via synchrosqueezing-based time-frequency analysis of process recordings; their corresponding modes are extracted and removed from seismic data using MVME with optimal balancing factors. We used synthetic data to investigate the effectiveness of the method and the influence of tuning parameters on processing results, and then applied the method to field datasets. The results have demonstrated that, compared with the conventional methods, the proposed method could effectively suppress the mechanical vibration interferences, improve the S/Ns and enhance polarization analysis of seismic signals.