Deep convolutional autoencoders as generic feature extractors in seismological applications

Qingkai Kong, Andrea Chiang, Ana C. Aguiar, M. Giselle Fernández-Godino, Stephen C. Myers, Donald D. Lucas
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引用次数: 8

Abstract

The idea of using a deep autoencoder to encode seismic waveform features and then use them in different seismological applications is appealing. In this paper, we designed tests to evaluate this idea of using autoencoders as feature extractors for different seismological applications, such as event discrimination (i.e., earthquake vs. noise waveforms, earthquake vs. explosion waveforms), and phase picking. These tests involve training an autoencoder, either undercomplete or overcomplete, on a large amount of earthquake waveforms, and then using the trained encoder as a feature extractor with subsequent application layers (either a fully connected layer, or a convolutional layer plus a fully connected layer) to make the decision. By comparing the performance of these newly designed models against the baseline models trained from scratch, we conclude that the autoencoder feature extractor approach may only outperform the baseline under certain conditions, such as when the target problems require features that are similar to the autoencoder encoded features, when a relatively small amount of training data is available, and when certain model structures and training strategies are utilized. The model structure that works best in all these tests is an overcomplete autoencoder with a convolutional layer and a fully connected layer to make the estimation.

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深度卷积自编码器在地震学应用中的通用特征提取
使用深度自编码器对地震波形特征进行编码,然后将其用于不同的地震学应用的想法很有吸引力。在本文中,我们设计了测试来评估使用自编码器作为不同地震学应用的特征提取器的想法,例如事件识别(即地震与噪声波形,地震与爆炸波形)和相位选择。这些测试包括在大量地震波形上训练一个自动编码器,可以是欠完全的,也可以是过完全的,然后使用训练好的编码器作为具有后续应用层(一个完全连接层,或者一个卷积层加上一个完全连接层)的特征提取器来做出决定。通过将这些新设计模型的性能与从头开始训练的基线模型进行比较,我们得出结论,自动编码器特征提取方法可能仅在某些条件下优于基线,例如当目标问题需要与自动编码器编码特征相似的特征时,当可用的训练数据相对较少时,以及当使用某些模型结构和训练策略时。在所有这些测试中,效果最好的模型结构是一个带有卷积层和一个完全连接层的过完备自编码器来进行估计。
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