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融合处理速度和加速度记录的地震检测模型及其在新丰江水库的应用
蒋策, 吕作勇, 房立华
PDF(2511 KB)
PDF(2511 KB)
融合处理速度和加速度记录的地震检测模型及其在新丰江水库的应用
Earthquake Detection Model Trained on Velocity and Acceleration Records and Its Application in Xinfengjiang Reservoir
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随着国家地震烈度速报与预警工程的建设，加速度记录在地震科学中将得到越来越多的应用. 但目前的地震检测模型多使用速度记录训练，对加速度记录的检测效果较差.利用广东地震台网数据，训练得到了可检测速度记录的PhaseNet_GD模型和检测加速度记录的PhaseNet_ITS模型. 在此基础上，结合GaMMA震相关联和HYPOSAT地震定位方法，发展了一套新的地震数据智能处理流程，并处理了2023年新丰江水库M L4.8地震序列，检测出的事件数量是人工目录的3.8倍，匹配率为93.2%，误检测率为0.38%.这一系统可快速产出完备性高、高精度的地震目录，为水库地震监测和区域地震台网的数据实时处理提供技术支撑.
With the construction of the "National Seismic Intensity Rapid Reporting and Early Warning" project, acceleration records data will be increasingly applied in earthquake science research. However, most current earthquake detection models use velocity records for training, which results in poor detection performance for acceleration records. This study utilized seismic records from the Guangdong Earthquake Network to train the PhaseNet_GD model for detecting velocity records and the PhaseNet_ITS model for detecting acceleration records.Based on this, a new intelligent earthquake data processing system was developed by combining the GaMMA, phase association method, and the HYPOSAT, earthquake location method. This system was used to process the 2023 M L 4.8 earthquake sequence in XinfengjiangReservoir, Heyuan, and detected events 3.8 times more than the manual catalog, with a matching rate of 93.2% and a false detection rate of 0.38%. This system can provide technical support for reservoir seismic monitoring and real-time data processing of regional earthquake networks.
区域台网 / 深度学习 / 地震检测 / PhaseNet / 新丰江水库 / 水库地震
regional seismic network / deep learning / seismic detection / PhaseNet / Xinfengjiang Reservoir / reservoir induced earthquake
P315.61
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本研究使用的数据由广东省地震局提供. 感谢中国地质大学（武汉）王墩教授和中国海洋大学邹志辉教授对本文修改过程中提出的意见和建议.
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