长短期记忆网络(LSTM)是一种用于深度学习领域的人工回归神经网络(RNN)结构。与标准的前馈神经网络不同,LSTM具有反馈连接。它不仅可以处理单个数据点(如图像),还可以处理整个数据序列(如语音或视频)。例如,LSTM适用于未分段、连接的手写识别、语音识别、网络流量或IDSs(入侵检测系统)中的异常检测等任务。

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In this paper, an end-to-end based LSTM scheme is proposed to address the problem of volcano event localization without any a priori model relating phase picking with localization estimation. It is worth emphasizing that automatic phase picking in volcano signals is highly inaccurate because of the short distances between the event epicenters and the seismograph stations. LSTM was chosen due to its capability to capture the dynamics of time varying signals, and to remove or add information within the memory cell state and model long-term dependencies. A brief insight into LSTM is also discussed here. The results presented in this paper show that the LSTM based architecture provided a success rate, i.e., an error smaller than 1.0Km, equal to 48.5%, which in turn is dramatically superior to the one delivered by automatic phase picking. Moreover, the proposed end-to-end LSTM based method gave a success rate 18% higher than CNN.

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