Application of machine learning (ML) or deep learning (DL) to geophysical data inversion is a growing topic of interest. Opportunities are in the areas of enhanced efficiency, resolution, and uniqueness for the inversion of geophysical ill-posed problems. Direct application of standard data-driven ML approaches to inversion have quickly shown limitations in the practical implementation. Some of the problems reside in the scarcity of appropriate labeled data caused by the lack of prior knowledge on the earth model being explored. Physics-informed network training is reducing the solution to physically bounded models. ML-inversion, however, needs to compete against the battery of highly evolved physics-based (Phy) inversion techniques that represent the most efficient and best result-oriented approaches. We have developed robust and efficient workflows and algorithms for combining ML and physics-based inversions in a unified approach by providing reciprocal interference. The workflow consists of linking the ML and Phy schemes through penalty functions applied to the common model term. A process of network retraining using partial inversion results further complements the procedure. The network progressively learns the Phy requirements and steers its predictions toward the data misfit reduction. The Phy inversion process also evolves by adding pseudo randomness and nonlinearity to the deterministic approach through the pseudo-stochastic model space sampling and nonlinear hyperparameter determination provided by DL. The procedure tends to converge after several iterations to common agreeable models introducing a stochastic flavor. The Phy-DL inversion (PhyDLI) scheme is demonstrated on synthetic and field transient electromagnetic data.

机器学习 (ML) 或深度学习 (DL) 在地球物理数据反演中的应用是一个越来越受关注的话题。机会在于地球物理不适定问题反演的效率、分辨率和独特性的提高。标准数据驱动的 ML 反演方法的直接应用在实际实现中很快显示出局限性。一些问题在于由于缺乏对正在探索的地球模型的先验知识而导致缺乏适当的标记数据。物理信息网络训练正在减少物理有界模型的解决方案。然而，ML 反演需要与一系列高度进化的基于物理 (Phy) 的反演技术竞争，这些技术代表了最有效和最好的以结果为导向的方法。我们开发了强大而高效的工作流程和算法，通过提供相互干扰，以统一的方法将 ML 和基于物理的反演结合起来。工作流程包括通过应用于通用模型项的惩罚函数将 ML 和 Phy 方案联系起来。使用部分反演结果的网络再训练过程进一步补充了该过程。网络逐步学习 Phy 要求，并将其预测转向减少数据失配。Phy 反演过程还通过 DL 提供的伪随机模型空间采样和非线性超参数确定，将伪随机性和非线性添加到确定性方法中。该过程在多次迭代后趋于收敛到引入随机风味的常见模型。