Groundwater Level Prediction and Earthquake Precursor Anomaly Analysis Based on TCN-LSTM-Attention Network

Abnormal changes in groundwater level are key indicators of seismic precursors. Before an earthquake, the groundwater level often shows varying degrees of abnormality. These anomalies typically manifest as a sudden rise or fall in groundwater levels and will last for a period of time. On Jul. 12, 2020, at 6:38 AM, a 5.1 magnitude earthquake occurred in Guye District, Tangshan City, Hebei Province, China (39.78 degrees N, 118.44 degrees E). This study uses this earthquake as a case study to analyze the groundwater level data from two observation wells, the Zhaogezhuang well and Yutian Ji 03 well. To accurately identify seismic precursor anomalies, the groundwater level data were divided into seismically active (SA) and seismically inactive (non-SA) periods, forming the basis for dataset segmentation. This paper proposes a TCN-LSTM-Attention model that combines the advantages of effective feature extraction with TCN and capturing complex temporal dependencies with LSTM. Experiments show that the designed model has strong abilities in predicting groundwater levels and identifying earthquake precursor anomalies. To enhance the accuracy of anomaly detection, this study employed an Exponentially Weighted Moving Average (EWMA) control chart to precisely pinpoint the onset of anomalies. Through validation with earthquakes in Jianshui County, Yunnan Province, China, the model effectively identified groundwater level anomalies under different geological conditions, confirming its generalization and practicality. Finally, this article conducted cross validation on the designed model, which has improved its reliability in practical applications. This study has certain scientific innovation and practical value in earthquake precursor analysis, providing new technical support and analysis methods for the development of earthquake warning technology and disaster prevention and reduction work.