Blockly earthquake transformer: A deep learning platform for custom phase picking
Artificial Intelligence in Geosciences, 2023 Deep-learning (DL) algorithms are increasingly used for routine seismic data processing tasks, including seismic event detection and phase arrival picking.
Hao Mai +4 more
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KVP: a multiscale kurtosis approach for seismic phase picking
Geophysical Journal InternationalSUMMARY Automatic event detection and phase picking are critical for processing the large volumes of data produced by modern seismological instrumentation. Accurate picking is especially challenging in Distributed Acoustic Sensing (DAS) recordings, where data quality can significantly vary along segments of the fibre due to localized ...
Hugo Latorre +5 more
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Seismic arrival-time picking on distributed acoustic sensing data using semi-supervised learning [PDF]
Nature Communications, 2023 Distributed Acoustic Sensing (DAS) is an emerging technology for earthquake monitoring and subsurface imaging. However, its distinct characteristics, such as unknown ground coupling and high noise level, pose challenges to signal processing.
Weiqiang Zhu +5 more
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Reducing the Parameter Dependency of Phase-Picking Neural Networks with Dice Loss
The Seismic RecordTraining a neural network for picking seismic phase arrivals has been commonly posed as a segmentation problem. It is a highly imbalanced segmentation problem in the sense that the background vastly dominates the foreground because we are trying to pick ...
Yongsoo Park, Gregory C. Beroza
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Seismic $P$ Phase Picking Using a Kurtosis-Based Criterion in the Stationary Wavelet Domain [PDF]
IEEE Transactions on Geoscience and Remote Sensing, 2008 The seismic P phase first arrival identification is a fundamental problem in seismology. The accurate identification of the P-wave first arrival is not a trivial process, particularly when the seismograms present a very low signal-to-noise ratio (SNR) or are contaminated with artificial transients that could produce false alarms.
Juan J. Galiana-Merino +2 more
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Detection and Monitoring of Mining-Induced Seismicity Based on Machine Learning and Template Matching: A Case Study from Dongchuan Copper Mine, China [PDF]
SensorsThe detection and monitoring of mining-induced seismicity are essential for understanding the mechanisms behind earthquakes and mitigating seismic hazards.
Tao Wu, Zhikun Liu, Shaopeng Yan
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Application of Neural Network Automatic Event Detection for Reservoir-Triggered Seismicity Monitoring Networks [PDF]
SensorsThis study examines reservoir-triggered seismicity (RTS) in Poland and Vietnam. The current state of individual RTS seismic networks necessitates detecting earthquakes from only a few stations.
Jan Wiszniowski +4 more
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A Microseismic Phase Picking and Polarity Determination Model Based on the Earthquake Transformer
Applied SciencesPhase 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.
Ling Peng, Lei Li, Xiaobao Zeng
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Picking Regional Seismic Phase Arrival Times with Deep Learning
SeismicaSparse instrumental coverage for much of the Earth requires working with regional seismic phases for effective seismic monitoring. Machine learning phase pickers to date have focused on local earthquake recordings.
Albert Leonardo Aguilar Suarez +1 more
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Phase picking errors on seismograms of seismic array
Mongolian Journal of Astronomy and GeophysicsBandpass filtering between 0.3 and 7 Hz is used in the daily seismic data analysis routine at IAG. I estimated the back azimuth of selected events from the ERMAR seismic array by F-k analysis and compared it with the back azimuth from the GSE bulletin for those events.
Tsagaan, Baasanbat
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