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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Unsupervised Clustering of Seismic Signals Using Deep Convolutional Autoencoders
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Unsupervised Clustering of Seismic Signals Using Deep Convolutional Autoencoders

机译：使用深度卷积自动编码器的地震信号无监督聚类

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摘要

In this letter, we use deep neural networks for unsupervised clustering of seismic data. We perform the clustering in a feature space that is simultaneously optimized with the clustering assignment, resulting in learned feature representations that are effective for a specific clustering task. To demonstrate the application of this method in seismic signal processing, we design two different neural networks consisting primarily of full convolutional and pooling layers and apply them to: 1) discriminate waveforms recorded at different hypocentral distances and 2) discriminate waveforms with different first-motion polarities. Our method results in precisions that are comparable to those recently achieved by supervised methods, but without the need for labeled data, manual feature engineering, and large training sets. The applications we present here can be used in standard single-site earthquake early warning systems to reduce the false alerts on an individual station level. However, the presented technique is general and suitable for a variety of applications including quality control of the labeling and classification results of other supervised methods.
机译：在这封信中，我们将深度神经网络用于地震数据的无监督聚类。我们在特征空间中执行聚类，该特征空间同时通过聚类分配进行了优化，从而获得了对特定聚类任务有效的学习型特征表示。为了证明该方法在地震信号处理中的应用，我们设计了两个主要由全卷积和池化层组成的不同神经网络，并将它们应用于：1）区分以不同的中心距记录的波形，以及2）区分具有不同初动的波形极性。我们的方法产生的精度可与最近通过监督方法获得的精度相媲美，但不需要标记数据，手动特征工程和大型训练集。我们在此提供的应用程序可用于标准的单站点地震预警系统中，以减少单个站点级别的虚假警报。然而，提出的技术是通用的并且适合于多种应用，包括标签的质量控制和其他监督方法的分类结果。

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