Microseismic data have a low signal-to-noise ratio (S/N). Existing waveform classification and arrival-picking methods are not effective enough for noisy microseismic data with low S/N. We have adopted a novel antinoise classifier for waveform classification and arrival picking by combining the continuous wavelet transform (CWT) and the convolutional neural network (CNN). The proposed CWT-CNN classifier is applied to synthetic and field microseismic data sets. Results show that CWT-CNN classifier has much better performance than the basic deep feedforward neural network (DNN), especially for microseismic data with low S/N. The CWT-CNN classifier has a shallow network architecture and small learning data set, and it can be trained quickly for different data sets. We have determined why CWT-CNN has better performance for noisy microseismic data. CWT can decompose the microseismic data into time-frequency spectra, where effective signals and interfering noise are easier to distinguish. With the help of CWT, CNN can focus on the specific frequency components to extract useful features and build a more effective classifier.

中文翻译：

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卷积神经网络用于微地震波形分类和到达选择

微地震数据的信噪比（S / N）低。现有的波形分类和到达拾取方法对于具有低信噪比的嘈杂微地震数据还不够有效。通过结合连续小波变换（CWT）和卷积神经网络（CNN），我们采用了一种新颖的抗噪声分类器进行波形分类和到达选择。提出的CWT-CNN分类器应用于合成和现场微地震数据集。结果表明，CWT-CNN分类器比基本的深度前馈神经网络（DNN）具有更好的性能，特别是对于低S / N的微地震数据而言。CWT-CNN分类器具有浅层网络体系结构和小的学习数据集，并且可以针对不同的数据集快速进行训练。我们已经确定了为什么CWT-CNN对于嘈杂的微地震数据具有更好的性能。CWT可以将微地震数据分解为时频频谱，从而更容易区分有效信号和干扰噪声。借助CWT，CNN可以专注于特定的频率分量，以提取有用的特征并建立更有效的分类器。