Machine learning applied to anthropogenic seismic events detection in Lai Chau reservoir area, Vietnam

Automatic detection of seismic events is a useful tool for routine data processing. Effective detection saves time and effort in phase picking and events' location, especially in areas with moderate seismicity at regional and local scales. The Lai Chau area in northern Vietnam is a good example of such a region. An additional difficulty in detection is the anthropogenic origin of reservoir-triggered seismicity observed in this region, where seismicity is non-stationary and there was no prior seismic activity. Neural network event detection was prepared to aid event identifications and further processing of seismic data. An automatic detection system was utilized to reduce the effort of manual interpretation of seismic signals in the region of the Lai Chau dam in North Vietnam while maintaining the detection of weak events at the same level. For this reason, a Single Layer Recurrent Neural Network (SLRNN) was applied. Compared to deep learning algorithms, fewer examples were needed to train the SLRNN. This paper presents a modified version of SLRNN, which additionally uses polarization analysis and the multistage learning process. In the first stage, the training data consists of events detected manually and disturbances selected visually by the operator. In the next stages, the earlier trained detection is validated in the successive recording periods. False detections together with new seismic events are added to the training set and the detection is retrained. The multi-stage process significantly reduces false detections. The software allows SLRNN to be used for routine seismic data processing.