BiInNet: Bilateral Inversion Network for Real-Time Velocity Analysis
BiInNet: Bilateral Inversion Network for Real-Time Velocity Analysis
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BiInNet-BiInNet 用于实时速度分析的双边反演网络
DOI：
10.1109/tgrs.2021.3117940
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发表时间：
2021-10
影响因子：
8.2
通讯作者：
Xuebao Guo
中科院分区：
文献类型：
--
作者：
Wei Cao;Ying Shi;Xuebao Guo
以前的大多数研究都集中在使用复杂的深度神经网络来学习大量合成数据的各种特征。在数据对数量有限的更现实的情况下，复杂网络不仅具有更高的计算复杂度，增加了训练时间，降低了推理速度，而且往往过拟合少量的训练数据，从而具有较弱的泛化能力。为了解决上述问题，我们提出了一个轻量级的架构，实时速度反演在现实情况下，双边反演网络（BiInNet）。BiInNet使用轻量级的ResNet18、ShuffleNetV2和修改后的MobileNetV2作为主干，兼顾了反演精度和推理速度。为了减少共炮点道集中的冗余信息，集中处理与速度强相关的图形属性特征，将速度分析、相似度和层速度模型的中间结果作为数据对。数值实验表明，BiInNet可以实时推断层速度模型，当使用ResNet18作为骨干时，每秒帧数（FPS）高达76.90。此外，当采用ShuffleNetV2作为主干时，BiInNet在更真实的褶皱模型，断层模型，盐模型和噪声数据集（NFOMD）上实现了最佳的反演精度，这说明BiInNet可以应用于不同地质结构的速度反演任务，并且对噪声具有鲁棒性。采用迁移学习对预训练模型进行微调，BiInNet有效地适用于速度反演模型和现场数据，进一步证明了该方法的可靠性，并在现场数据对不足的情况下提供了一种实用的速度反演方案。
Most previous studies focus on using complex deep neural networks to learn diverse features of massive synthetic data. In more realistic situations with a limited number of data pairs, complex networks not only have higher computational complexity, increasing training time and reducing inference speed, but also tend to over-fit a small amount of training data, thus having weak generalization capability. To address the aforementioned problem, we propose a lightweight architecture for real-time velocity inversion in realistic situations, the bilateral inversion network (BiInNet). BiInNet uses lightweight ResNet18, ShuffleNetV2, and modified MobileNetV2 as backbones, taking into account the inversion accuracy and inference speed. To reduce the redundant information in common shot gathers and focus on graphical property features which are strongly correlated with velocity, the intermediate results of velocity analysis, semblances, and interval velocity models are prepared as data pairs. Numerical experiments show that BiInNet can infer interval velocity models in real-time, with frames per second (FPS) up to 76.90 when ResNet18 is used as the backbone. Moreover, BiInNet achieves the best inversion accuracy on more realistic fold models, fault models, salt models, and noisy dataset (NFOMD) when adopting ShuffleNetV2 as the backbone, which illustrates that BiInNet can be applied to velocity inversion tasks of different geological structures and is robust to noise. Adopting transfer learning to fine-tune pretrained model, BiInNet is effectively applicable to velocity reversal models and field data, which further demonstrates the reliability of the proposed method and provides a practical velocity inversion scheme when the field data pairs are insufficient.
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DOI：
10.1190/segam2020-3426516.1
发表时间：
2020-09
期刊：
Seg Technical Program Expanded Abstracts
影响因子：
--
作者：
Du Yang;Yufei Cai;Guangmin Hu;Xingmiao Yao;Wen Zou
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影响因子：
8.2
作者：
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Jiang Peng
DOI：
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发表时间：
2016-02
期刊：
ArXiv
影响因子：
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作者：
F. Iandola;Matthew W. Moskewicz;Khalid Ashraf;Song Han;W. Dally;K. Keutzer
通讯作者：
F. Iandola;Matthew W. Moskewicz;Khalid Ashraf;Song Han;W. Dally;K. Keutzer
DOI：
10.4018/978-1-7998-1192-3.ch008
发表时间：
2020
期刊：
Advances in Systems Analysis, Software Engineering, and High Performance Computing
影响因子：
--
作者：
Menaga D.;R. S.
通讯作者：
Menaga D.;R. S.
DOI：
10.4018/978-1-5225-9096-5.ch007
发表时间：
2021-07
期刊：
Smart Computational Intelligence in Biomedical and Health Informatics
影响因子：
--
作者：
A. Sinha;S. Gupta;Anurag Tiwari;Amrita Chaturvedi
通讯作者：
A. Sinha;S. Gupta;Anurag Tiwari;Amrita Chaturvedi