[论文解读] A CNN-BiLSTM Model with Attention Mechanism for Earthquake Prediction

Parisa Kavianpour, Mohammadreza Kavianpour+2 more|arXiv (Cornell University)|Dec 26, 2021

Earthquake Detection and Analysis被引用 25

一句话总结

本文提出一种结合注意力机制的 CNN-BiLSTM 模型，利用 ZOH 预处理和九个区域划分，对中国大陆按月预测地震数量和最大震级，并与若干基线方法进行比较。

ABSTRACT

Earthquakes, as natural phenomena, have continuously caused damage and loss of human life historically. Earthquake prediction is an essential aspect of any society's plans and can increase public preparedness and reduce damage to a great extent. Nevertheless, due to the stochastic character of earthquakes and the challenge of achieving an efficient and dependable model for earthquake prediction, efforts have been insufficient thus far, and new methods are required to solve this problem. Aware of these issues, this paper proposes a novel prediction method based on attention mechanism (AM), convolution neural network (CNN), and bi-directional long short-term memory (BiLSTM) models, which can predict the number and maximum magnitude of earthquakes in each area of mainland China-based on the earthquake catalog of the region. This model takes advantage of LSTM and CNN with an attention mechanism to better focus on effective earthquake characteristics and produce more accurate predictions. Firstly, the zero-order hold technique is applied as pre-processing on earthquake data, making the model's input data more proper. Secondly, to effectively use spatial information and reduce dimensions of input data, the CNN is used to capture the spatial dependencies between earthquake data. Thirdly, the Bi-LSTM layer is employed to capture the temporal dependencies. Fourthly, the AM layer is introduced to highlight its important features to achieve better prediction performance. The results show that the proposed method has better performance and generalize ability than other prediction methods.

研究动机与目标

• 动机：需要进行准确、及时的地震预测，以降低损失和人员伤亡。
• Develop a hybrid CNN-BiLSTM-AM 架构，以捕捉区域地震数据中的时空依赖关系。
• 加入注意力机制以强调对预测有影响的特征。
• 使用 zero-order hold 对数据进行预处理，以提高模型的鲁棒性和泛化能力。
• Evaluate the model on monthly earthquake data for Mainland China divided into nine sub-regions and compare with baseline methods.

提出的方法

• 使用一维卷积神经网络提取月度地震数据的空间特征。
• 应用 BiLSTM 层来学习提取特征中的时间依赖性。
• 加入注意力机制以对影响预测的特征进行加权。
• 使用 zero-order hold 对数据进行预处理，以缓解零值效应。
• 使用全连接层生成月度计数和震级的最终预测。
• 使用 Adam 优化器、MSE 损失、150 轮、32 的批量大小进行训练，数据通过 Min-Max 归一化。

实验结果

研究问题

• RQ1 CNN-BiLSTM-AM 模型是否能够同时预测月度地震数量和最大月震级？
• RQ2 注意力机制是否通过突出重要特征来提高预测准确性？
• RQ3 zero-order hold 预处理对模型性能与泛化能力有什么影响？
• RQ4 将中国大陆划分为 nine regions 如何影响预测能力和对未来事件的定位？
• RQ5 所提出的方法与浅层 ML 及其他 DL 模型在地震统计预测方面的比较？

主要发现

• 所提出的 CNN-BiLSTM-AM 方法比所比较的方法具有更好的性能和泛化能力。
• 模型结构通过 CNN 和 BiLSTM 组件有效提取时空特征，并通过注意力强调重要特征。
• ZOH 预处理提高了训练鲁棒性和预测准确性。
• nine regional divisions 用于评估下一个地震事件的区域预测范围。
• 对比模型包括 SVM、DT、RF、MLP、CNN、LSTM 和 CNN-BiLSTM，所提出的方法在这些基线方法上进行评估。