Deep and Convolutional Neural Networks for identifying vertically-propagating incoming seismic wave motion into a heterogeneous, damped soil column
Deep and Convolutional Neural Networks for identifying vertically-propagating incoming seismic wave motion into a heterogeneous, damped soil column
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DOI：
10.1016/j.soildyn.2022.107510
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发表时间：
2022-11
影响因子：
4
通讯作者：
Shashwat Maharjan;B. Guidio;A. Fathi;C. Jeong
中科院分区：
文献类型：
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作者：
Shashwat Maharjan;B. Guidio;A. Fathi;C. Jeong
岩土工程场地地震波运动的识别在土木基础设施关键部件的抗震分析和设计中起着不可或缺的作用。目前的做法依赖于在现场使用土壤介质的一维模型，以及一个称为反褶积的过程，该过程识别由于在地面进行的测量而引起的地震输入。当一个场地表现出相当大的异质性或地形变化时，使用底层土壤介质的多维模型变得非常重要。这些情况需要使用基于梯度的优化方法来反演多维入射地震波运动。然而，这样的方法是计算昂贵，耗时。我们探讨的有效性和鲁棒性的数据知情的框架的反源问题，由于其潜在的降低计算成本，相比基于梯度的方法。我们设计了深度和卷积神经网络架构，以根据在地面进行的测量来预测入射波的运动。我们证明了他们的有效性和鲁棒性盲测试的例子，测量数据被噪声污染，当在训练数据集中的入射信号可能会或可能不会像一个现实的地震信号。最后，所提出的人工神经网络被证明是有效的预测入射波运动时，地下材料的属性缺乏准确性，或不确定的，这是可能的情况下，在现实情况下。虽然这里只考虑一维问题，但我们处理多维问题的数据通知方法的推广似乎很简单。总的来说，我们的数据知情的方法似乎是强大的，快速的，并有希望确定传入的地震波运动。
Identification of the incoming seismic wave motion at a geotechnical site plays an integral role in the seismic analysis and design of the critical components of the civil infrastructure. Current practice relies on using one-dimensional models for the soil medium at the site, and a process called deconvolution, which identifies seismic input due to measurements made at the ground surface. Using multi-dimensional models for the underlying soil medium becomes important when a site exhibits considerable heterogeneity or change in topography. These situations necessitate using a gradient-based optimization method for inverting the multi-dimensional incoming seismic-wave motion. However, such methods are computationally expensive and time consuming.We explore the effectiveness and robustness of a data-informed framework for the inverse-source problem, due to its potential in reducing the computational cost, compared to a gradient-based approach. We design deep and convolutional neural network architectures to predict the incoming wave motion based on measurements made at the ground surface. We demonstrate their effectiveness and robustness on blind test examples, where measured data are contaminated with noise, and when the incident signals in the training data set may or may not resemble a realistic seismic signal. Lastly, the presented artificial neural networks are shown to be effective in predicting incoming wave motion when the subsurface material properties lack accuracy, or are uncertain, which is likely the case in realistic situations. While only one-dimensional problems are considered here, generalization of our data-informed approach to handle multi-dimensional problems appears to be straightforward. Overall, our data-informed approach seems to be robust, fast, and promising for identifying the incoming seismic wave motion.