MSNet: A Seismic Phase Picking Network Applicable to Microseismic Monitoring | IEEE Journals & Magazine | IEEE Xplore

MSNet: A Seismic Phase Picking Network Applicable to Microseismic Monitoring


Abstract:

In the process of microseismic monitoring, the accuracy of seismic phase picking has a great influence on the precision of seismic localization. Recently, neural network ...Show More

Abstract:

In the process of microseismic monitoring, the accuracy of seismic phase picking has a great influence on the precision of seismic localization. Recently, neural network models have achieved satisfying results in seismic phase picking. But these models are mainly applicable to natural earthquakes. In this letter, we present a fully convolutional network for microseismic phase picking called MSNet. The performance of the model is improved from three aspects: model structure, training data, and training strategy. MSNet has two decoders to allow for auxiliary learning, and its structure is improved with reference to the state-of-the-art fully convolutional network. Our training data derive from the Stanford Earthquake Dataset (STEAD). It is selected and processed to meet our requirements. In the training process, a special early stopping strategy is used to obtain the model with best generalization performance. The model is tested on fracture-induced microseismic monitoring data from a coal mine in Shaanxi Province, China. Methods for comparison include short- and long-time average ratio, the original PhaseNet, and a retrained PhaseNet. The number of correct P picks of MSNet is 18.7% more than PhaseNet, and MSNet achieves a 16.2% higher precision than PhaseNet on S phase. The event locating results verified the accuracy of MSNet. Our method provides an idea to build a microseismic phase picking model with better generalization performance and lower labor cost.
Published in: IEEE Geoscience and Remote Sensing Letters ( Volume: 20)
Article Sequence Number: 7505105
Date of Publication: 11 September 2023

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