Anomaly detection of earthquake precursor data using long short-term memory networks

基于LSTM-RNN的地震前兆数据异常检测新方法（英文）

Earthquake precursor data have been used as an important basis for earthquake prediction.In this study,a recurrent neural network(RNN)architecture with long short-term memory(LSTM)units is utilized to develop a predictive model for normal data.Furthermore,the prediction errors from the predictive models are used to indicate normal or abnormal behavior.An additional advantage of using the LSTM networks is that the earthquake precursor data can be directly fed into the network without any elaborate preprocessing as required by other approaches.Furthermore,no prior information on abnormal data is needed by these networks as they are trained only using normal data.Experiments using three groups of real data were conducted to compare the anomaly detection results of the proposed method with those of manual recognition.The comparison results indicated that the proposed LSTM network achieves promising results and is viable for detecting anomalies in earthquake precursor data.

研究地震前兆数据的异常变化是地震短临预测的基础,本文提出一种地震前兆数据的异常智能检测新方法,利用长短期记忆单元的递归神经网络(LSTMRNN)构建数据趋势变化预测模型,通过模型预测的误差来提取数据的异常变化。该方法不需要对原始数据进行预处理,也不需要对异常数据判断的经验积累,适用于各类不同长度的地震前兆数据异常检测。通过使用三类真实的前兆观测数据的进行方法检验,将机器检测结果与人工识别结果进行对比分析,试验结果表明,基于LSTM-RNN的异常检测方法能够准确识别各类异常,可以代替人工用于地震前兆数据的异常检测。