Deep Neural Networks for Creating Reliable PmP Database With a Case Study in Southern California
Deep Neural Networks for Creating Reliable PmP Database With a Case Study in Southern California
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DOI：
10.1029/2021jb023830
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发表时间：
2021-12
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影响因子：
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通讯作者：
Wen Ding;Tianjue Li;Xu Yang;Kui Ren;P. Tong
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作者：
Wen Ding;Tianjue Li;Xu Yang;Kui Ren;P. Tong
人工智能和机器学习的最新进展使得从指数级增长的地震数据中自动识别地震相位成为可能。尽管在自动识别第一个P波和S波到达方面取得了一些令人兴奋的成功，但自动识别后期地震相位（如莫霍反射PmP波）仍然是一个重大挑战，无法与经验丰富的分析师的表现相匹配。机器识别PmP波的主要困难在于可识别的PmP波很少，这使得从大量地震数据库中识别PmP波的问题本质上是不平衡的。在这项工作中，通过利用南加州的高质量PmP数据集（10,192个手动选择），我们开发了PmPNet，这是一种基于深度神经网络的算法，可以有效地自动识别PmP波；通过这样做，我们加快了识别PmP波的过程。PmPNet在机器学习社区应用了类似的技术来解决PmP数据集的不平衡问题。PmPNet的架构是一个带有附加预测块的残差神经网络（ResNet）自编码器，其中编码器、解码器和预测器都配备了ResNet连接。通过对现场数据的系统研究表明，PmPNet可以同时实现高精度和高查全率的PmP波自动识别。将预训练的PmPNet应用于南加州1990年1月至1999年12月的地震数据库，我们获得的PmP数据比原始PmP数据集多近两倍，为其他研究提供了有价值的数据，如绘制莫霍面结构和成像南加州下地壳结构。
Recent progresses in artificial intelligence and machine learning make it possible to automatically identify seismic phases from exponentially growing seismic data. Despite some exciting successes in automatic picking of the first P‐ and S‐wave arrivals, auto‐identification of later seismic phases such as the Moho‐reflected PmP waves remains a significant challenge in matching the performance of experienced analysts. The main difficulty of machine‐identifying PmP waves is that the identifiable PmP waves are rare, making the problem of identifying the PmP waves from a massive seismic database inherently unbalanced. In this work, by utilizing a high‐quality PmP data set (10,192 manual picks) in southern California, we develop PmPNet, a deep‐neural‐network‐based algorithm to automatically identify PmP waves efficiently; by doing so, we accelerate the process of identifying the PmP waves. PmPNet applies similar techniques in the machine learning community to address the unbalancement of PmP datasets. The architecture of PmPNet is a residual neural network (ResNet)‐autoencoder with additional predictor block, where encoder, decoder, and predictor are equipped with ResNet connection. We conduct systematic research with field data, concluding that PmPNet can efficiently achieve high precision and high recall simultaneously to automatically identify PmP waves from a massive seismic database. Applying the pre‐trained PmPNet to the seismic database from January 1990 to December 1999 in southern California, we obtain nearly twice more PmP picks than the original PmP data set, providing valuable data for other studies such as mapping the topography of the Moho discontinuity and imaging the lower crust structures of southern California.