标题： 弹性全波形反演中多分量数据的低频外推深度学习 显示英文标题

标题： Deep learning for low frequency extrapolation of multicomponent data in elastic full waveform inversion

摘要： 全波形反演（FWI）的成功高度依赖于准确的初始模型。 这在弹性区域尤其如此：由于S波波长较短，弹性FWI中的周期跳变现象比声学FWI更为严重。 在本文中，我们通过提出合成多分量弹性地震记录的低频成分，并利用这些“人工”低频成分来启动弹性FWI的频率扫描，扩展了我们在外推FWI（EFWI）方面的工作。 我们的解决方案涉及深度学习：我们分别在两个训练数据集上训练相同的卷积神经网络（CNN），一个使用粒子速度的垂直分量，另一个使用水平分量，以外推弹性数据的低频成分。 该CNN的架构设计具有较大的感受野，通过大卷积核或扩张卷积实现。 在Marmousi2模型上的数值示例表明，从4Hz以上的带限数据外推得到的2-4Hz低频数据为P波和S波速度的弹性FWI提供了良好的初始模型。 此外，我们研究了所提出的神经网络在不同物理模型上的泛化能力。 对于弹性测试数据，通过弹性模拟收集训练数据的外推精度优于声学模拟，即泛化差距更小。 显示英文摘要

摘要： Full waveform inversion (FWI) strongly depends on an accurate starting model to succeed. This is particularly true in the elastic regime: The cycle-skipping phenomenon is more severe in elastic FWI compared to acoustic FWI, due to the short S-wave wavelength. In this paper, we extend our work on extrapolated FWI (EFWI) by proposing to synthesize the low frequencies of multi-component elastic seismic records, and use those "artificial" low frequencies to seed the frequency sweep of elastic FWI. Our solution involves deep learning: we separately train the same convolutional neural network (CNN) on two training datasets, one with vertical components and one with horizontal components of particle velocities, to extrapolate the low frequencies of elastic data. The architecture of this CNN is designed with a large receptive field, by either large convolutional kernels or dilated convolution. Numerical examples on the Marmousi2 model show that the 2-4Hz low frequency data extrapolated from band-limited data above 4Hz provide good starting models for elastic FWI of P-wave and S-wave velocities. Additionally, we study the generalization ability of the proposed neural network over different physical models. For elastic test data, collecting the training dataset by elastic simulation shows better extrapolation accuracy than acoustic simulation, i.e., a smaller generalization gap.