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

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 prediction network (SLN) 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 personal computer (PC) to provide accurate detection results for data collected over a 24-h 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. The 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.