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USTC-Pickers: a Unified Set of seismic phase pickers Transfer . . . - GitHub A quantitative evaluation of deep learning based seismic pickers Example of in-depth bencharking study of deep learning-based picking routines using the SeisBench framework The USTC-Pickers with exactly the same architecture as the original PhaseNet Multiple data augmentation techniques are used Latest
USTC-Pickers: a Unified Set of seismic phase pickers Transfer learned . . . To mitigate this problem, we build a unified set of customized seismic phase pickers for different levels of use in China We first train a base picker with the recently released DiTing dataset using the same U-Net architecture as PhaseNet
[2501. 03621] Benchmarking seismic phase associators: Insights from . . . This study presents a detailed benchmark analysis of five seismic phase associators, including classical and machine learning-based approaches: PhaseLink, REAL, GaMMA, GENIE, and PyOcto We use synthetic datasets mimicking real seismicity characteristics in crustal and subduction zone scenarios
USTC-Pickers: a Unified Set of seismic phase pickers Transfer learned . . . To mitigate this problem, we build a unified set of customized seismic phase pickers for different levels of use in China We first train a base picker with the recently released DiTing dataset using the same U-Net architecture as PhaseNet
GitHub - cangyeone csnbench: 中国地区震相拾取模型对比 This tutorial aims to link the raw dataset and final applicable models by introducing the data organization, model training and validating in a step by step style
Benchmark on the accuracy and efficiency of several neural network . . . We train and compare seven deep-learning-based seismic phase pickers for the first time using a uniform dataset from the China Seismic Network The accuracy and efficiency on both CPU and GPU devices are evaluated, providing a reference for end-users to choose a suitable phase picker
USTC-Pickers:a Unified Set of seismic phase pickers . . . Benchmark on the accuracy and efficiency of several neural network based phase pickers using datasets from China Seismic Network Seismic phase pickers based on deep neural networks have been extensively used recently, demonstrating their advantages on both performance and efficiency
Benchmarking seismic phase associators: - arXiv. org This study presents a detailed benchmark analysis of five seismic phase associators, including classical and machine learning-based approaches: PhaseLink, REAL, GaMMA, GENIE, and PyOcto We use synthetic datasets mimicking real seismicity characteristics in crustal and subduction zone scenarios