Deep Learning Models Augment Analyst Decisions for Event Discrimination
Deep Learning Models Augment Analyst Decisions for Event Discrimination
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DOI：
10.1029/2018gl081119
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发表时间：
2019-04
影响因子：
5.2
通讯作者：
L. Linville;K. Pankow;T. Draelos
中科院分区：
文献类型：
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作者：
L. Linville;K. Pankow;T. Draelos
长期地震监测网络能够充分利用机器学习的进步，因为策划的事件目录提供了丰富的标记训练数据。我们探索使用卷积和递归神经网络来完成本地距离的爆炸和构造源的歧视。使用犹他州大学地震仪站生成的5年事件目录，我们训练模型，使用来自三分量和单通道传感器的90 s事件频谱图生成自动事件标签。两种网络架构都能够复制98%以上的分析师标签。最常见的是，模型错误是标签错误的结果（70%的情况下）。考虑到错误标记的事件（约占目录的1%），两个模型的模型准确度都提高到99%以上。浅层构造地震的分类准确率保持在98%以上，说明受地震深度控制的光谱特征在地震事件判别中并不起主导作用。
Long‐term seismic monitoring networks are well positioned to leverage advances in machine learning because of the abundance of labeled training data that curated event catalogs provide. We explore the use of convolutional and recurrent neural networks to accomplish discrimination of explosive and tectonic sources for local distances. Using a 5‐year event catalog generated by the University of Utah Seismograph Stations, we train models to produce automated event labels using 90‐s event spectrograms from three‐component and single‐channel sensors. Both network architectures are able to replicate analyst labels above 98%. Most commonly, model error is the result of label error (70% of cases). Accounting for mislabeled events (~1% of the catalog) model accuracy for both models increases to above 99%. Classification accuracy remains above 98% for shallow tectonic events, indicating that spectral characteristics controlled by event depth do not play a dominant role in event discrimination.