Phase Neural Operator for Multi‐Station Picking of Seismic Arrivals
Geophysical Research Letters, 2023 Seismic wave arrival time measurements form the basis for numerous downstream applications. State‐of‐the‐art approaches for phase picking use deep neural networks to annotate seismograms at each station independently, yet human experts annotate seismic ...
Hongyu Sun +3 more
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Test-Time Augmentations and Quality Controls for Improving Regional Seismic Phase Picking [PDF]
SensorsRegional seismic phases are essential for imaging Earth’s internal structure. Although extensive regional seismic networks are publicly available worldwide, only a small fraction of recorded phase arrivals are picked for constraining earthquake source ...
Bingyao Han +4 more
doaj +7 more sources
Seismic Phase Picking Using a Cross-Attention Network on NVIDIA Jetson Xavier NX
IEEE AccessThis paper introduces a neural network model for seismic phase picking tailored for edge intelligence. The model architecture is meticulously designed to accommodate the resource constraints of edge computing platforms, enabling real-time seismic phase ...
Bo Lan +5 more
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EdgePhase: A Deep Learning Model for Multi‐Station Seismic Phase Picking
Geochemistry, Geophysics, Geosystems, 2022 In this study, we build a multi‐station phase‐picking model named EdgePhase by integrating an Edge Convolutional module with a state‐of‐the‐art single‐station phase‐picking model, EQTransformer.
Tian Feng, Saeed Mohanna, Lingsen Meng
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Seismic Phase Picking Algorithm Based on Improved Bi-LSTM
Taiyuan Ligong Daxue xuebao, 2021 In order to solve the problem of traditional waveform detection algorithms such as relying on manual setting of thresholds and low precision of phase picking, a seismic phase picking algorithm based on improved Bi-LSTM was proposed.
Zhenhua HAN +4 more
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MFU-Net: a multi-scale fusion U-Net for seismic phase picking [PDF]
Scientific ReportsSeismic phase picking aims to accurately identify and label the arrival times of different types of seismic waves (e.g., P-waves and S-waves) from waveform data, serving as a fundamental step in seismological research and related applications.
Lihua Wu +5 more
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Application of a convolutional neural network for seismic phase picking of mining-induced seismicity. [PDF]
Geophys J Int, 2021 SUMMARYMonitoring mining-induced seismicity (MIS) can help engineers understand the rock mass response to resource extraction. With a thorough understanding of ongoing geomechanical processes, engineers can operate mines, especially those mines with the propensity for rockbursting, more safely and efficiently. Unfortunately, processing MIS data usually
Johnson SW +3 more
europepmc +4 more sources
EPick: Attention-based multi-scale UNet for earthquake detection and seismic phase picking
Frontiers in Earth Science, 2022 Earthquake detection and seismic phase picking play a crucial role in the travel-time estimation of P and S waves, which is an important step in locating the hypocenter of an event. The phase-arrival time is usually picked manually. However, its capacity
Wei Li +17 more
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Automatic seismic phase picking and consistent observation error assessment: application to the Italian seismicity [PDF]
Geophysical Journal International, 2006 ISSN:0956 ...
R Di Stefano +2 more
exaly +3 more sources
DTPP:An efficient depthwise separable TCN for seismic phase picking
Artificial Intelligence in GeosciencesWith the rapid development of artificial intelligence in seismology, various deep learning-based seismic phase picking models have emerged in recent years.
Shuai Lv, Yuxiang Peng
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