Results 1 to 10 of about 37,745 (280)
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
doaj +8 more sources
International Journal of Mining Science and Technology, 2018 Accurate detection and picking of the P-phase onset time in noisy microseismic data from underground mines remains a big challenge. Reliable P-phase onset time picking is necessary for accurate source location needed for planning and rescue operations in
Charles Mborah, Maochen Ge
doaj +5 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
doaj +3 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
doaj +3 more sources
Hybrid Validation of a Quality-Controlled, Waveform-Centered AI Framework with Optional Multi-Sensor Support for Seismic Monitoring [PDF]
SensorsRapid and reliable seismic monitoring requires accurate waveform inference, together with robustness to noise, incomplete sensing, and unstable predictions.
Askar Abdykadyrov +5 more
doaj +2 more sources
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
Earth and Space Science, 2020 We propose a new method that considers the envelope, phase attributes, and texture analysis of the subbottom profile to automatically obtain continuous and accurate horizon picking.
Jianhu Zhao +3 more
doaj +2 more sources
Automatic Phase Picking From Microseismic Recordings Using Feature Extraction and Neural Network
IEEE Access, 2020 High-accuracy microseismic phase picking is fundamental to microseismic signal processing. Phase picking methods based on deep learning show great potential dealing with low signal to noise ratio (SNR) data but need enormous training data.
Tianqi Jiang, Jing Zheng
doaj +3 more sources
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
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
doaj +6 more sources