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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Deep Learning Phase Picking of Large-N experiments [PDF]
, 2019 The popularisation of the use of large-N arrays of seismometers has resulted in a significant increase of the size of the datasets recorded during these experiments. Therefore, new challenges have arisen on how to process all these data efficiently, and in an automated fashion.
Fernandez-Prieto, Luis +1 more
openaire +5 more sources
PickBlue: Seismic Phase Picking for Ocean Bottom Seismometers With Deep Learning
Earth and Space Science, 2023 Detecting phase arrivals and pinpointing the arrival times of seismic phases in seismograms is crucial for many seismological analysis workflows. For land station data, machine learning methods have already found widespread adoption.
T. Bornstein +7 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 +2 more sources
Earthquake transformer—an attentive deep-learning model for simultaneous earthquake detection and phase picking [PDF]
Nature Communications, 2020 The authors here present a deep learning model that simultaneously detects earthquake signals and measures seismic-phase arrival times. The model performs particularly well for cases with high background noise and the challenging task of picking the S ...
S. Mostafa Mousavi +4 more
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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
doaj +2 more sources
Improved methods for hydro‐frac event detection and phase picking [PDF]
Beijing 2009 International Geophysical Conference and Exposition, Beijing, China, 24–27 April 2009, 2009 The ability to detect small microseismic events and identify their P and S phase arrivals is a key issue in hydraulic fracture monitoring because of the low signal-to-noise ratios.
Fuxian Song +7 more
openaire +3 more sources
Robust Phase Association and Simultaneous Arrival Picking for Downhole Microseismic Data Using Constrained Dynamic Time Warping [PDF]
SensorsAccurate phase association and arrival time picking are pivotal for reliable microseismic event location and source characterization. However, the complexity of downhole microseismic wavefields, arising from heterogeneous subsurface structures, variable ...
Tuo Wang +5 more
doaj +2 more sources
Making Phase-Picking Neural Networks More Consistent and Interpretable
The Seismic RecordImproving the interpretability of phase-picking neural networks remains an important task to facilitate their deployment to routine, real-time seismic monitoring. The popular phase-picking neural networks published in the literature lack interpretability
Yongsoo Park +2 more
doaj +2 more sources