Anomaly detection in borehole strain data as earthquake precursors is of great significance for earthquake prediction. This paper takes the borehole strain time series provided by the Jiangsu Earthquake Agency as experimental data. Firstly, a preprocessing method combining trend separation and residual denoising is used to effectively extract the long-term trends and critical fluctuation information from the data, thereby reducing noise interference. Subsequently, an LSTM-Attention model is designed and its performance is compared with three other models: LSTM, CNN, and AutoEncoder. The comparison results indicate that the LSTM-Attention model outperforms the other models in terms of MSE, MAE, and RMSE. Finally, this model is used for anomaly detection. Experimental validation demonstrates that the LSTM-Attention model can accurately capture anomalies in the data for the task of anomaly detection in borehole strain precursors of earthquakes.
Key words
earthquake precursor anomaly /
borehole strain data /
time series /
LSTM /
attention
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