Microseismic monitoring plays an essential role for reservoir characterization and earthquake disaster monitoring and early warning. The accuracy of the subsurface velocity model directly affects the precision of event localization and subsequent processing. It is challenging for traditional methods to realize efficient and accurate microseismic velocity inversion due to the low signal-to-noise ratio of field data. Deep learning can efficiently invert the velocity model by constructing a mapping relationship from the waveform data domain to the velocity model domain. The predicted and reference values are fitted with mean square error as the loss function. To reduce the feature mismatch between the synthetic and real microseismic data, data augmentation is also performed using correlation and convolution operations. Moreover, a hybrid training strategy is proposed by combining synthetic and augmented data. By testing real microseismic data, the results show that the Unet is capable of high-resolution and robust velocity prediction. The data augmentation method complements more high-frequency components, while the hybrid training strategy fully combines the low-frequency and high-frequency components in the data to improve the inversion accuracy.

Phase arrival times and polarities provide essential kinematic constraints for and dynamic insights into seismic sources, respectively. This information serves as fundamental data in seismological study. For microseismic events with smaller magnitudes, reliable phase picking and polarity determination are even more challenging but play a crucial role in source location and focal mechanism inversion. This study innovatively proposes a deep learning model suitable for simultaneous phase picking and polarity determination with continuous microseismic waveforms. Building upon the Earthquake Transformer (EQT) model, we implemented structural improvements through four distinct decoders specifically designed for three tasks of P-wave picking, S-wave picking, and P-wave first-motion polarity determination and named the model EQT-Plus (EQTP). Notably, the polarity determination task was decomposed into two independent decoders to enhance the learning of polarity characteristics. Through training on a northern California dataset and testing on microseismic events (Md < 3) in the Geysers region, the results demonstrate that the EQTP model achieves superior performance in both phase picking and polarity determination compared to the PhaseNet+ model. It not only provides accurate phase picking but also shows higher consistency with manual picking results in polarity determination. We further validated the good generalization ability of the model with the DiTing dataset from China. This study not only advances the adaptation of the Transformer model in seismology but also reliably delivers fundamental information essential for refined microseismic inversion, offering an alternative and advanced tool for the seismological community.

Accurate source localization and source mechanism estimation in microseismic monitoring critically depend on precise subsurface velocity model reconstruction. Recent advances in data-driven approaches, such as neural network models, have demonstrated promising performance in seismic velocity inversion. However, purely data-driven methods are significantly constrained by their reliance on the completeness of the training dataset. Additionally, the performance of current neural network architectures remains suboptimal and requires further improvement. We propose a step-by-step training methodology that integrates the wave equation-based modeling with a modified U-shaped neural network (U-Net). This approach incorporates physical laws of wave propagation to constrain the updating process of the velocity model. The proposed method carries out the purely data-driven procedure first and then incorporates waveform modeling with finite-difference simulations. Besides, the new method defines dynamic weights within the loss function and introduces the Akaike information criterion (AIC) to detect and extract the P and S phases, which reduces the computational expenses as well as alleviates the artifacts in velocity models effectively. We conducted experiments with synthetic fault models and realistic layered models, and the results show that the wave equation-constrained method recovers the velocity model better than the purely data-driven algorithm. The approach leveraging physics-based and data-driven models can be easily adapted to other geophysical inversion tasks.