标题： 深度学习增强的弹性全波形反演初始模型预测：应用于海洋拖缆数据 显示英文标题

标题： Deep learning enhanced initial model prediction in elastic FWI: application to marine streamer data

摘要： 低频数据对于约束地震全波形反演（FWI）中的低波数模型分量至关重要。 然而，由于采集限制和环境噪声，这些数据通常不可用。 深度学习（DL）可以学习从弹性FWI的高频模型更新映射到低波数模型更新，从而产生一个初始模型估计，就好像它来自低频数据一样。 我们在合成数据集上训练一个基于FusionNET的卷积神经网络（CNN），以从一组由FWI在缺失低频数据的数据上产生的模型更新中生成一个初始低波数模型。 我们使用一个合成基准来验证这种DL融合方法，该基准的数据是在与训练数据集无关的模型中生成的。 最后，将我们训练好的网络应用于基于野外数据估计初始低波数模型，我们发现从这种“DL融合”模型更新开始的弹性FWI在真实世界海洋拖缆数据上表现出更好的收敛性。 显示英文摘要

摘要： Low-frequency data are essential to constrain the low-wavenumber model components in seismic full-waveform inversion (FWI). However, due to acquisition limitations and ambient noise it is often unavailable. Deep learning (DL) can learn to map from high frequency model updates of elastic FWI to a low-wavenumber model update, producing an initial model estimation as if it was available from low-frequency data. We train a FusionNET-based convolutional neural network (CNN) on a synthetic dataset to produce an initial low-wavenumber model from a set of model updates produced by FWI on the data with missing low frequencies. We validate this DL-fused approach using a synthetic benchmark with data generated in an unrelated model to the training dataset. Finally, applying our trained network to estimate an initial low-wavenumber model based on field data, we see that elastic FWI starting from such a 'DL-fused' model update shows improved convergence on real-world marine streamer data.