The change in groundwater level is considered an important potential signal for earthquake precursors, and studying its relationship with seismic activity is of great significance for earthquake prediction. An anomaly detection method based on TCN-GRU model is proposed to identify the abnormal changes in groundwater level, and combined with EWMA control chart to accurately locate the time of anomaly occurrence. The experimental results show that the TCN-GRU model is most sensitive to abnormal fluctuations, has significant robustness and real-time detection ability, and can adapt to complex changes under different well conditions. The study reveals the close relationship between abnormal groundwater level and seismic activity, providing scientific basis for early identification of earthquake precursor signals and having important application value for earthquake prediction and disaster reduction.
Key words
TCN-GRU /
deep learning /
earthquake precursors /
anomaly detection /
EWMA control chart
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