Efficient seismic phase picking is fundamental to seismic signal processing. Phase picking methods based on neural networks show great potential in accurately picking signals with a low signal-to-noise ratio but… Click to show full abstract
Efficient seismic phase picking is fundamental to seismic signal processing. Phase picking methods based on neural networks show great potential in accurately picking signals with a low signal-to-noise ratio but require large training datasets. We present a transductive transfer-learning-based support vector machine (TTL-SVM) algorithm for seismic phase picking when the seismic dataset possesses insufficient training samples. An objective function of TTL-SVM, which is incorporated with a pretraining classification process in the source domain that possesses an adequate training dataset and quality labeling, is proposed for phase picking in the target domain with no quality labeling. Seismic compressional (
               
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