With the recent improvement of deep learning (DL) techniques and computer hardware capabilities, neural networks are widely used to monitor massive sensor data and detect earthquakes in them. This makes designing fast, accurate, and generalized DL models necessary for an active field of research for automatic seismic phase picking. A seismic phase picking network called MFFnet is proposed to fuse power spectral density (PSD), expert knowledge, spectrograms, recurrence plots (RPs), and Gramian angle fields. The network uses fast Fourier convolution (FFC) on 2-D representations to extract more interpretable features. Considering the high proportion of noisy signals in field applications, MFFnet uses focal loss (FL) as the loss function to improve network accuracy. Experimental results show that MFFnet achieves precision, recall, and accuracy with 0.96, 0.98, and 0.98, respectively, in seismic phase detection tasks. Shapley value is used to evaluate the relationship between features and network predictions. Compared with other DL networks, the feature extraction approach used in this letter is more explanatory and provides greater confidence in the results.

中文翻译：

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MFFnet：基于多特征融合的地震取相网络

近年来，随着深度学习（DL）技术和计算机硬件能力的提高，神经网络被广泛用于监测海量传感器数据并检测其中的地震。这使得设计快速、准确和通用的深度学习模型成为自动地震相位拾取活跃研究领域所必需的。提出了一种称为 MFFnet 的地震相位拾取网络来融合功率谱密度（PSD）、专家知识、频谱图、递归图（RP）和格拉米角场。该网络在二维表示上使用快速傅里叶卷积（FFC）来提取更多可解释的特征。考虑到现场应用中噪声信号比例较高，MFFnet采用焦点损失（FL）作为损失函数来提高网络精度。实验结果表明，MFFnet 在地震相位检测任务中的精确度、召回率和准确度分别为 0.96、0.98 和 0.98。 Shapley 值用于评估特征和网络预测之间的关系。与其他深度学习网络相比，这封信中使用的特征提取方法更具解释性，并且对结果提供了更大的可信度。