Seismic inversion plays a very useful role in the detailed stratigraphic interpretation of migrated seismic volumes by enabling the estimation of reservoir properties over the complete volume. Traditional and machine learning-based seismic inversion workflows are limited to inverting each seismic trace independently of other traces to estimate impedance profiles, leading to lateral discontinuities in the presence of noise and large geologic variations in the seismic data. In addition, machine learning-based approaches suffer the problem of overfitting if there is only a small number of wells on which the model is trained. We have developed a two-pronged strategy to overcome these problems. We present a temporal convolutional network that models seismic traces temporally. We further inject the spatial context for each trace into its estimations of the impedance profile. To counter the problem of limited labeled data, we also present a joint learning scheme whereby multiple data sets are simultaneously used for training, sharing beneficial information among each of the sets. This results in improvement in the generalization performance on all data sets. We have developed a case study of acoustic impedance inversion using the open-source SEAM and Marmousi 2 data sets. Our evaluations show that our proposed approach is able to estimate impedance in the presence of noisy seismic data and a limited number of well logs with greater robustness and spatial consistency. We compare and contrast our approach to other learning-based seismic inversion methodologies in the literature. On SEAM, we are able to obtain an average mean squared error of 0.0476, the lowest among all other methodologies.

中文翻译：

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基于空间上下文的多数据集地震反演的联合学习，以提高通用性和鲁棒性

地震反演通过估计整个体积的储层特性，在偏移地震体积的详细地层解释中起着非常有用的作用。传统的和基于机器学习的地震反演工作流程仅限于将每个地震道独立于其他道进行反演以估计阻抗剖面，从而导致在地震数据中存在噪声和大地质变化的情况下横向不连续。此外，如果模型训练的井数很少，则基于机器学习的方法会遇到过度拟合的问题。我们制定了一个双管齐下的策略来克服这些问题。我们提出了一个时间卷积网络，它可以在时间上模拟地震道。我们进一步将每个迹线的空间上下文注入其对阻抗曲线的估计中。为了解决标记数据有限的问题，我们还提出了一种联合学习方案，即同时使用多个数据集进行训练，在每个集之间共享有益的信息。这导致所有数据集的泛化性能得到改善。我们开发了一个使用开源 SEAM 和 Marmousi 2 数据集进行声阻抗反演的案例研究。我们的评估表明，我们提出的方法能够在存在噪声地震数据和有限数量的测井时估计阻抗，具有更大的稳健性和空间一致性。我们将我们的方法与文献中其他基于学习的地震反演方法进行比较和对比。在 SEAM 上，