Physics-driven deep-learning for marine CSEM data inversion

IF 2.2 3区 地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY
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Abstract

Marine controlled-source electromagnetic (MCSEM) inversion plays a crucial role in hydrocarbon exploration and pre-drill reservoir evaluation. Deep learning techniques have been widely used in geophysical inversions. Although they work on theoretical data well, their performance on survey data needs to be improved. Since no constraint of physical laws is applied in the training phase, the trained neural network often exhibits large errors when extended to new datasets with different distributions from the train set. To solve this problem, we add a differentiable marine EM forward operator at the end of the neural network that maps the network-predicted results back to the response data. We incorporate a data error term to the loss function and the gradient of data error with respect to model parameters in the gradient back-propagation process so that we can successfully introduce the physical law constraints into the network training process. Experiments on synthetic data validate the effectiveness of our Physics-driven Deep Neural Network (PhyDNN) inversions. It performs significantly better than the conventional DNN as it can recover the model accurately while maintaining data fitting. Tests on theoretical data with different noise levels further demonstrate the superiority of our PhyDNN, which can achieve stable inversions under high noise levels. Moreover, we use the t-distributed stochastic neighbor embedding (t-SNE) algorithm to analyze the similarity between the train sets and real data. The results show that the real data falls within the data distribution of the train sets, ensuring the credibility of the inversion results. Finally, we use PhyDNN to invert an EM survey dataset acquired over a deep-sea sedimentary basin. The inversion results match well Occam's inversions, indicating that our physics-driven network has enhanced the data adaptability and overcome the limitation of conventional DNN in handling new data.

用于海洋 CSEM 数据反演的物理驱动深度学习
海洋可控源电磁(MCSEM)反演在油气勘探和钻探前储层评估中发挥着至关重要的作用。深度学习技术已广泛应用于地球物理反演。虽然这些技术在理论数据上运行良好,但在勘测数据上的性能却有待提高。由于在训练阶段没有应用物理规律的约束,当扩展到与训练集分布不同的新数据集时,训练好的神经网络往往会表现出很大的误差。为了解决这个问题,我们在神经网络的末端添加了一个可变海洋电磁前向算子,将网络预测的结果映射回响应数据。我们在损失函数中加入了数据误差项,并在梯度反向传播过程中加入了数据误差相对于模型参数的梯度,从而成功地在网络训练过程中引入了物理定律约束。合成数据实验验证了物理驱动深度神经网络(PhyDNN)反演的有效性。它的性能明显优于传统的 DNN,因为它能在保持数据拟合的同时准确恢复模型。对不同噪声水平的理论数据进行的测试进一步证明了 PhyDNN 的优越性,它可以在高噪声水平下实现稳定的反演。此外,我们还使用 t 分布随机邻域嵌入(t-SNE)算法分析了训练集与真实数据之间的相似性。结果表明,真实数据属于训练集的数据分布范围,确保了反演结果的可信度。最后,我们使用 PhyDNN 对深海沉积盆地的电磁勘测数据集进行反演。反演结果与奥卡姆反演结果吻合,表明我们的物理驱动网络增强了数据适应性,克服了传统 DNN 在处理新数据时的局限性。
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来源期刊
Journal of Applied Geophysics
Journal of Applied Geophysics 地学-地球科学综合
CiteScore
3.60
自引率
10.00%
发文量
274
审稿时长
4 months
期刊介绍: The Journal of Applied Geophysics with its key objective of responding to pertinent and timely needs, places particular emphasis on methodological developments and innovative applications of geophysical techniques for addressing environmental, engineering, and hydrological problems. Related topical research in exploration geophysics and in soil and rock physics is also covered by the Journal of Applied Geophysics.
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