The phase arrival picking of the downhole microseismic dataset is a critical step in fracturing monitoring data processing. Recently, data-driven methods have been widely used in seismology studies, especially in seismic phase picking. The picking results heavily depend on whether large quantities of accurately labeled phase samples could be obtained to extract the characteristics of seismic waveforms. Also, there is a shortcoming of poor generalization ability in dealing with the cross-source transfer scenarios. In this paper, we propose a novel deep transfer learning method for microseismic phase arrival picking by fine-tuning one existing pretrained model based on a few phase samples. The pretrained model, which has been domain-adapted for phase picking, adopts 2D U-Net to both extract time and space features, thereby improving the overall picking accuracy. Moreover, the fully convolutional U-Net architecture has the ability to handle samples with variable sizes so could be used for bridging downhole microseismic data from different sources. The results of two transfer cases show that compared with the direct application of the pretrained model and a newly trained model, the proposed method could provide more satisfactory performance with only limited seismic phase samples. Also, our method significantly reduces the cost of labeling and saves time because of avoiding repeated training.

Reconstructing carbon release fluxes during ancient climatic warming events is important for improving predictions of carbon cycle and climate dynamics under future anthropogenic warming scenarios. We investigate the extent of biogenic methane release and its contribution to climate variability across the Toarcian Oceanic Anoxic Event (T-OAE) approximately 183 million years ago. To do this, we developed a global biogeochemical model and applied a Bayesian inversion using Markov Chain Monte Carlo (MCMC) simulations. Based on a high-resolution record of carbon isotope excursions from the Yorkshire section, our results indicate that a release of at least 4,700 Gt carbon from biogenic CH ₄ (with a carbon isotopic composition, δ ¹³ C of −50 to −70‰) is necessary to accurately reproduce the pronounced pulsed shift in the δ ¹³ C, as well as the inferred changes in atmospheric p CO ₂ and global temperature. This massive methane release may have led to a substantial increase in atmospheric p CH ₄ and contributed to additional global surface warming, perhaps by more than 2 °C. We further elucidate that the liberation of methane may have been facilitated by an upsurge in methanogenesis alongside a concomitant decline in methane oxidation within organic-rich, sulfate-depleted marine environments. An active CH ₄ cycle represents a positive feedback mechanism that exacerbates environmental deterioration during climatic warming events, ultimately contributing to mass extinction of marine life.

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.

The Holocene Okavango Rift Zone (ORZ) marks the southern terminus of the Western Branch of the East African Rift System. Detailed knowledge of the crustal and lithospheric mantle structure of the ORZ is essential to decipher the rifting mechanism and nature of the lithosphere of this incipient continental rift. A 3-D shear wave velocity model from the surface to 160-km depth is constructed by jointly inverting the Rayleigh wave phase velocity dispersion and receiver function data through a non-linear Bayesian Monte-Carlo algorism. The crustal thickness estimates from our new velocity model generally agree with previous receiver function investigations of the region. The crust beneath the ORZ is thinned compared with the cratonic regions to both sides of the rift, suggesting a certain degree of continental stretching. Our velocity model also reveals two low velocity anomalies in the crust and upper mantle beneath the incipient rift, respectively. The shallow low velocity anomaly is mainly confined in the upper and middle crust, and the deeper low velocity anomaly extends from the Moho to at least 160 km depth, with a high-velocity lower crust (~4.0 km/s) in between. Although the two low velocity anomalies are probably both caused by rift-related decompression melting, the structural feature imaged suggests that they are generated separately and individually. Based on our observations, we propose that thermal upwelling and decompression partial melting in the upper mantle of the ORZ have a limited contribution to the stretching and thinning of the crust during the initiation of the continental rifting. The crustal rifting could be induced by an intra-plate relative motion between the South African block and the rest of the African continent along a pre-existing weak zone.

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.