CubeNet: Array-Based Seismic Phase Picking with Deep Learning
Seismological Research Letters, 2022Abstract In recent years, a variety of deep learning (DL) models for seismic phase picking have attracted considerable attention and are widely adopted in many earthquake monitoring projects. However, most current DL models pick P and S arrivals trace by trace without simultaneously considering the spatial coherence of seismic phases ...
Guoyi Chen, Junlun Li
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An artificial neural network approach for broadband seismic phase picking
Bulletin of the Seismological Society of America, 1999AbstractThis article presents a method for picking broadband seismic phases by using backpropagation neural networks (BPNNs) as detectors. By combining the results from three BPNN detectors—long term, mid-term, and short term—the method combines the features of short term's higher accuracy and long term's lower false alarm rate.
Yue Zhao, Kiyoshi Takano
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PhaseMamba: A Mamba-Based Deep Learning Model for Seismic Phase Picking and Detection
IEEE Geoscience and Remote Sensing LettersHaoran Ren, Haofeng Wu
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Seismic horizon picking by integrating reflector dip and instantaneous phase attributes
Geophysics, 2019ABSTRACT Seismic horizons are the compulsory inputs for seismic stratigraphy analysis and 3D reservoir modeling. Manually interpreting horizons on thousands of vertical seismic slices of 3D seismic survey is a time-consuming task. Automatic horizon interpreting algorithms are usually based on the seismic reflector dip.
Yihuai Lou +3 more
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CapsPhase: Capsule Neural Network for Seismic Phase Classification and Picking
IEEE Transactions on Geoscience and Remote Sensing, 2022Omar M. Saad, Yangkang Chen
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MSNet: A Seismic Phase Picking Network Applicable to Microseismic Monitoring
IEEE Geoscience and Remote Sensing Letters, 2023Chengyu Feng +4 more
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Seismic Phase Picking Using Synchrosqueezed Transform and Attention Mechanism
2023 IEEE 5th International Conference on Civil Aviation Safety and Information Technology (ICCASIT), 2023Zejie Chen, Yao Du, Qian Liu
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Toward Robust Seismic Phase Picking in Realistic Multi-Event Scenarios
Accurate and robust seismic phase picking remains a fundamental challenge in automated earthquake monitoring, particularly under complex waveform conditions. While recent deep learning models such as PhaseNet and EQTransformer have demonstrated strong performance on commonly used benchmark datasets, their architectural design choices introduce ...Ching-Hung Wang +2 more
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QuakeMLab Phase I: Deep Learning-Based Automated Seismic Phase Picking Using PhaseNet
Phase picking in seismology is the first step of signal processing and locating a seismic event. At the beginning of seismological research, it is straightforward to manually pick P- and S-wave arrival times because the number of seismic stations is relatively small. Recently, instrumentation has improved, and the gap is lower than before.Timur Tezel +5 more
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Using a Deep Neural Network and Transfer Learning to Bridge Scales for Seismic Phase Picking
Geophysical Research Letters, 2020Chengping Chai +2 more
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