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
Deep learning enhanced initial model prediction in elastic FWI: application to marine streamer data
AI总结：
针对低频数据缺失问题，提出用深度学习（FusionNET-CNN）从弹性FWI高频更新预测低波数初始模型，经合成与现场数据验证，可改善海洋拖缆数据反演收敛性。
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，低频数据往往难以获得。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，仿佛它是从低频数据中获得的。我们在合成数据集上训练了一个基于FusionNET的卷积神经网络（CNN），以从缺失低频数据上的FWI产生的模型更新集合中生成初始低波数模型。我们使用一个与训练数据集无关的模型生成的合成基准数据验证了这种DL融合方法。最后，将我们训练好的网络应用于基于现场数据的初始低波数模型估计，我们发现从这种“DL融合”模型更新开始的弹性FWI在真实海洋拖缆数据上显示出改进的收敛性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
