Adaptive moment-tensor joint inversion of clustered microseismic events for monitoring geological carbon storage

Summary An accurate and efficient method for picking the first arrival of microseismic signals is crucial for processing microseismic monitoring data. However, the weak magnitude and low signal-to-noise ratio (SNR) of these signals make picking arrivals challenging. Recent advancements in deep learning-based methods for picking the first arrivals of microseismic signals have effectively addressed the inefficiencies and inaccuracies of traditional methods. Nevertheless, these methods often require many training samples, and the substantial size and labelling effort significantly hinder the development of deep learning-based first-arrival picking methods. This study introduces Semi-Picking: a semi-supervised method for picking the first arrival of microseismic signals, utilising the TransUGA network and SimMatch. This approach automatically labels microseismic signals following sample augmentation by establishing a semi-supervised learning framework, significantly reducing the time required for sample labelling. Initially, the TransUNet model is enhanced by incorporating the Self-Supervised Predictive Convolutional Attention Block (SSPCAB) module to create a Deep-TransUNet architecture, which more effectively separates signal from noise in microseismic signals with low SNR and improves the accuracy of first-arrival picking. Subsequently, the datasets for this study are compiled from microseismic traces collected from field monitoring records. Finite-difference forward modelling is applied to the microseismic data to train the network, and hyperparameter tuning is performed to optimise the UGATIT and Deep-TransUNet architecture. The outcomes of the arrival-picking experiments, conducted under conditions of low SNR using both synthetic and real microseismic records, demonstrated that Semi-Picking offers robust resistance to incorrect labels. This resilience stems from the synergistic use of the semi-supervised learning framework and self-attention mechanisms. The proposed method demonstrates superiority over the TransUNet, the SSPCAB-TransUNet, the UNet++, and the traditional STA/LTA method, respectively, with the picking error rate of the Semi-Picking Net being less than 0.1 s. The proposed method outperforms the commonly used deep learning-based approaches for picking the first arrivals of microseismic signals, exhibiting superior performance.

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Research on focal mechanism of microseismic events and the regional stress during hydraulic fracturing at a shale play site in southwest China

Chen,

Meng,

Chen

et al. 2024

GEOPHYSICS

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We propose a waveform matching inversion method to determine the focal mechanism of microseismic events recorded by a single well observation system. Our method employs the cross-correlation technique to mitigate the influence of anisotropy on the S-wave. Then by conducting a grid search for strike, dip, and rake, we match the observed waveforms of P- and S-wave with the corresponding theoretical waveforms. A synthetic test demonstrates the robustness and accuracy of our method in resolving the focal mechanism of microseismic events under a single well observation system. By applying our method to the events that have been categorized into two clusters based on spatial and temporal evolution recorded during the hydraulic fracturing operation in the Weiyuan shale reservoir, we observe the two clusters have distinct focal mechanism and stress characteristics. The events in remote cluster (cluster A) exhibits consistent focal mechanisms, with a concentrated distribution of P-axis orientations. And the inverted maximum principal stress direction of cluster A aligns with local maximum principal stress direction (SHmax). It implies events in cluster A occur in a uniform stress condition. In contrast, the other cluster (cluster B) near the injection well exhibits significant variation in focal mechanisms, with a scattered distribution of P-axis orientations. And the inverted maximum principal stress direction deviates from local maximum principal stress direction (SHmax), indicating that events in cluster B occur in a complicated stress condition.

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Adaptive moment-tensor joint inversion of clustered microseismic events for monitoring geological carbon storage

Cited by 5 publications

References 43 publications

Convolutional neural network-based moment tensor inversion using domain adaptation for microseismicity monitoring

Convolutional neural network-based moment tensor inversion using domain adaptation for microseismicity monitoring

Semi-Picking: A semi-supervised arrival time picking for microseismic monitoring based on the TransUGA network combined with SimMatch

Research on focal mechanism of microseismic events and the regional stress during hydraulic fracturing at a shale play site in southwest China

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