Abstract:A CCFE model is used to detect regional seismic signals(epicenter distance <150 km) in a single waveform signal and identify P wave and S wave phase. Continuous waveform data recorded at Foziling station in Huoshan area, Anhui province from June to August 2017 are used for seismic detection and phase identification. A total of 164 earthquakes were detected, about 2.16 times more than the earthquake catalog. The missing earthquakes significantly improved the integrity of the seismic catalog in the ML-1.7 to ML0.0 range. The difference between the time of P wave and S wave phases detected in the cataloged earthquake and the cataloged results are about 0.03 seconds. Compared with the more common deep learning models CRED, EQT and GPD, CCFE model has a higher success rate of identifying seismic events and phase locations for earthquakes with smaller magnitude and some special cases, such as two seismic waveforms that are close to each other, especially when a larger waveform is preceded by a much smaller one.
SHAO Yongqian,PENG Zhao,WANG Chengrui et al. Automatic Construction of Microseismic Catalog in Huoshan Area of Anhui Based on CCFE[J]. jgg, 2024, 44(4): 436-440.