Automatic Identification of Mantle Seismic Phases Using a Convolutional Neural Network
Automatic Identification of Mantle Seismic Phases Using a Convolutional Neural Network
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DOI：
10.1029/2020gl091658
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发表时间：
2021-09-28
影响因子：
5.2
通讯作者：
Schmerr, N.
中科院分区：
文献类型：
--
作者：
Garcia, J. A.;Waszek, L.;Schmerr, N.
典型的地震波形数据集包括数十万到数百万条记录。编译是通过耗时的相位到达时间的手工挑选或信号处理算法(如互相关)来执行的。与手工采摘相比，后者通常表现不佳。然而，挑选方法的不同造成了对地球结构的模型和解释的不同。在这里，我们利用卷积神经网络(CNN)的模式识别能力。使用大量精选的数据集，我们训练了一个CNN模型来识别地震剪切相SS。这将加速、自动化并进行一致的数据汇编，这是一项通常通过目测完成并受科学家选择影响的任务。利用CNN模型识别地幔不连续产生的SS前兆。它识别叠加和单独地震记录中的前兆，产生新的地幔过渡带测量结果，其质量可与手工挑选的数据相媲美。这种高质量观测的快速获取对未来地震层析成像研究的自动化具有重要意义。
Typical seismic waveform data sets comprise hundreds of thousands to millions of records. Compilation is performed by time-consuming handpicking of phase arrival times, or signal processing algorithms such as cross-correlation. The latter generally underperform compared to handpicking. However, differences in picking methods creates variations in models and interpretation of Earth's structure. Here, we exploit the pattern recognition capabilities of Convolutional Neural Networks (CNN). Using a large handpicked data set, we train a CNN model to identify the seismic shear phase SS. This accelerates, automates, and makes consistent data compilation, a task usually completed by visual inspection and influenced by scientists' choices. The CNN model is employed to identify precursors to SS generated by mantle discontinuities. It identifies precursors in stacked and individual seismograms, producing new measurements of the mantle transition zone with quality comparable to handpicked data. This rapid acquisition of high-quality observations has implications for automation of future seismic tomography studies.