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.

中文翻译：

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使用长短期记忆网络的地震前兆数据异常检测

地震前兆数据已被用作地震预报的重要基础。在这项研究中，具有长短期记忆（LSTM）单元的递归神经网络（RNN）体系结构用于开发正常数据的预测模型。此外，来自预测模型的预测误差用于指示正​​常或异常行为。使用LSTM网络的另一个优势是，地震前兆数据可以直接馈入网络，而无需其他方法要求的任何复杂的预处理。此外，由于这些网络仅使用正常数据进行训练，因此不需要有关异常数据的先前信息。使用三组真实数据进行了实验，以比较该方法和手动识别方法的异常检测结果。