RASE: A Real-Time Automatic Search Engine for Anomalous Seismic Electric Signals in Geoelectric Data

Abstract

The geoelectric data contain important anomalous information for short-term earthquake prediction. Timely and accurate identification of seismic electric anomalies is important for disaster prevention. However, identifying anomalies is challenging due to the huge volumes of data and noise disturbance. In this study, we develop a real-time automatic search engine (RASE) that incorporates an unsupervised convolutional denoising network (UCN) module and a supervised LSTM network (SLN) prediction module to automatically search for important anomalous signals in real-time. Experiments demonstrate that the RASE provides excellent detection accuracy and efficiency for synthetic and field data, which takes only dozens of seconds for a common PC to provide accurate detection results for data collected over a 24-hour period. The RASE has excellent flexibility and developability, as its internal modules can be adapted by more suitable technologies for better performance in various application scenarios. Comparison of multiple module combinations shows that the RASE configured with UCN and SLN has the highest detection accuracy. Our proposed search engine can reduce the human labor required for complex and repetitive detection work and fully realize the potential of geoelectric field observation in earthquake monitoring and disaster prevention.