基于CCFE自动构建安徽霍山地区微震目录

Automatic Construction of Microseismic Catalog in Huoshan Area of Anhui Based on CCFE

使用单台波形信号中检测区域地震信号(震中距<150 km)并识别P波和S波震相的CCFE模型,利用安徽霍山地区佛子岭台2017-06~08记录到的连续波形数据进行地震检测和震相识别。共检测出164次地震,约为地震目录的2.16倍,使地震目录中ML-1.7~0.0范围内的地震完整性得到较明显的改善。检测到编目地震的P波和S波震相到时与编目结果相差0.03 s左右。同目前比较常见的深度学习模型CRED、EQT和GPD相比,CCFE模型对于震级较小及某些特殊情形,如2个距离较近,尤其是较大地震前震级相对小很多的地震波形,具备较高的地震检测和震相识别成功率。

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 M L-1.7 to M L0.0 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.