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Automatic Waveform Classification and Arrival Picking Based on Convolutional Neural Network

机译:基于卷积神经网络的自动波形分类及到达拣选

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Automatic waveform classification and arrival picking methods are widely studied to reduce or replace the manual works. Machine learning based methods, especially neural networks, and clustering based methods have shown great potentials in previous studies. However, most of the existing methods are sensitive to noise. The convolution neural networks (CNNs), developed from the traditional neural networks, have been successfully applied in many different fields, but are rarely studied in seismic waveform classification. In this paper, we propose a novel antinoise CNN architecture for waveform classification and also propose to combine k‐means clustering (KC) with CNN classification to pick arrivals (CNN‐KC). Seismic data are sampled to 1‐D vectors using a specific time window. Using the trained CNN classifier, these 1‐D vectors are classified into two categories: waveform and nonwaveform. With the constraint of the first waveform, CNN‐KC can pick the arrival more accurately. We also apply the proposed methods to the synthetic microseismic data with different noise levels and the actual field microseismic data to test their robustness. CNNs perform much better than the traditional multilayer perceptron on the waveform classification of the noisy microseismic data. Based on the analysis of the CNN internal architecture, we also conclude that the main reason that CNN is insensitive to noise is the convolution and pooling layers which behave like smooth operation in some ways. The final results show that the CNN and CNN‐KC are effective and robust methods for waveform classification and arrival picking.
机译:自动波形分类和到达采摘方法被广泛研究以减少或更换手动工作。基于机器学习的方法,尤其是神经网络,基于聚类的方法在以前的研究中表现出很大的潜力。但是,大多数现有方法对噪声敏感。从传统的神经网络开发的卷积神经网络(CNNS)已成功应用于许多不同的领域,但很少在地震波形分类中研究。在本文中,我们提出了一种用于波形分类的新型AntioIch CNN架构,并建议将K-Means聚类(KC)与CNN分类组合以挑选抵达(CNN-KC)。使用特定时间窗口对地震数据进行采样至1-D vectors。使用训练有素的CNN分类器,这些1-D向量分为两类:波形和非WaveForm。随着第一个波形的约束,CNN-KC可以更准确地拾取到达。我们还将所提出的方法应用于具有不同噪声水平的合成微震数据和实际场微震数据以测试其稳健性。 CNNS比传统的多层的Perceptron更好地表现出嘈杂微震数据的波形分类。基于对CNN内部架构的分析,我们还得出结论,CNN对噪声不敏感的主要原因是卷积和汇集层,其以某种方式表现得如此平稳。最终结果表明,CNN和CNN-KC是波形分类和到达拣选的有效和鲁棒方法。

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