Deep-learning(DL)algorithms are increasingly used for routine seismic data processing tasks,including seismic event detection and phase arrival picking.Despite many examples of the remarkable performance of existing(i...Deep-learning(DL)algorithms are increasingly used for routine seismic data processing tasks,including seismic event detection and phase arrival picking.Despite many examples of the remarkable performance of existing(i.e.,pre-trained)deep-learning detector/picker models,there are still some cases where the direct applications of such models do not generalize well.In such cases,substantial effort is required to improve the performance by either developing a new model or fine-tuning an existing one.To address this challenge,we present Blockly Earthquake Transformer(BET),a deep-learning platform for efficient customization of deep-learning phase pickers.BET implements Earthquake Transformer as its baseline model,and offers transfer learning and fine-tuning extensions.BET provides an interactive dashboard to customize a model based on a particular dataset.Once the parameters are specified,BET executes the corresponding phase-picking task without direct user interaction with the base code.Within the transfer-learning module,BET extends the application of a deep-learning P and S phase picker to more specific phases(e.g.,Pn,Pg,Sn and Sg phases).In the fine-tuning module,the model performance is enhanced by customizing the model architecture.This no-code platform is designed to quickly deploy reusable workflows,build customized models,visualize training processes,and produce publishable figures in a lightweight,interactive,and open-source Python toolbox.展开更多
基金funded by a Discovery Grant(RGPIN-2018-03752)from the Natural Science and Engineering Research Council of Canada(PA)This is NRCan publication number 20220610.
文摘Deep-learning(DL)algorithms are increasingly used for routine seismic data processing tasks,including seismic event detection and phase arrival picking.Despite many examples of the remarkable performance of existing(i.e.,pre-trained)deep-learning detector/picker models,there are still some cases where the direct applications of such models do not generalize well.In such cases,substantial effort is required to improve the performance by either developing a new model or fine-tuning an existing one.To address this challenge,we present Blockly Earthquake Transformer(BET),a deep-learning platform for efficient customization of deep-learning phase pickers.BET implements Earthquake Transformer as its baseline model,and offers transfer learning and fine-tuning extensions.BET provides an interactive dashboard to customize a model based on a particular dataset.Once the parameters are specified,BET executes the corresponding phase-picking task without direct user interaction with the base code.Within the transfer-learning module,BET extends the application of a deep-learning P and S phase picker to more specific phases(e.g.,Pn,Pg,Sn and Sg phases).In the fine-tuning module,the model performance is enhanced by customizing the model architecture.This no-code platform is designed to quickly deploy reusable workflows,build customized models,visualize training processes,and produce publishable figures in a lightweight,interactive,and open-source Python toolbox.
