用深度压缩学习缩小地震数据量，加速全波形反演。
Accelerated Full Waveform Inversion by Deep Compressed Learning
- 用二值化感知层的神经网络学习精简地震采集布局。
- 仅用10%数据进行2D反演，效果优于随机采样。
- 适合大规模3D反演场景，显著降低计算成本。
我们提出并验证了一种降低全波形反演（FWI）输入维度的方法，以缓解计算开销。现代地震采集系统生成的数据量可达千兆浮点级存储，使得工业级复杂地层反演或多场景探索变得难以实现。所提方法利用带有二值化感知层的深度神经网络，通过压缩学习从大量地下模型中提炼出简洁但关键的地震采集布局。给定大规模地震数据集时，训练好的网络选择较小的数据子集，再通过表示学习中的自编码器计算数据的潜在表示，并使用K-means聚类进一步筛选最相关数据用于FWI。该方法可视为分层数据选择。在仅使用10%数据的情况下，2D FWI结果始终优于随机采样，为大规模3D反演加速提供了可能。
原文摘要 · Abstract (English)
We propose and test a method to reduce the dimensionality of Full Waveform Inversion (FWI) inputs as computational cost mitigation approach. Given modern seismic acquisition systems, the data (as input for FWI) required for an industrial-strength case is in the teraflop level of storage, therefore solving complex subsurface cases or exploring multiple scenarios with FWI become prohibitive. The proposed method utilizes a deep neural network with a binarized sensing layer that learns by compressed learning a succinct but consequential seismic acquisition layout from a large corpus of subsurface models. Thus, given a large seismic data set to invert, the trained network selects a smaller subset of the data, then by using representation learning, an autoencoder computes latent representations of the data, followed by K-means clustering of the latent representations to further select the most relevant data for FWI. Effectively, this approach can be seen as a hierarchical selection. The proposed approach consistently outperforms random data sampling, even when utilizing only 10% of the data for 2D FWI, these results pave the way to accelerating FWI in large scale 3D inversion.
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