We consider the optimization of a neural network previously developed by the authors for the joint inversion of 3D gravitational and magnetic fields in the context of mineral exploration. The distinctive feature of this neural network is that it solves ill-posed (ill-conditioned) inverse problems. The neural network implements a special two-level algorithm. The lower level of the algorithm uses two neural networks with equivalent architectures. The first of them computes the gravitational field sources in a given domain from measurements of this field on a remote surface. The second neural network processes magnetic field measured on the same surface to find magnetic sources in the same domain. The found source distributions are used at the upper level of the algorithm to calculate their structural residual, which determines the degree of difference (closeness) of their geometries. As a result, minimizing this residual, when training a neural network at the upper level, implements a computational algorithm that yields geometrically close source distributions of different fields. The article examines in detail the possibilities of optimizing some elements of the neural networks and the algorithms used (datasets, training process, specific form of loss functions, etc.) Test calculations for model problem demonstrate high quality of joint inversion by our optimized neural networks approach. Calculations were also carried out for the joint processing of real-feald data from gravity and magnetic exploration in Jussara region, Goias State, Brazil. The article also considers the issue of determining in joint field inversion not only the geometric distribution of sources, but also their physical intensities.

翻译：本文研究了作者先前开发的用于矿产勘探中三维重磁场联合反演神经网络的优化问题。该神经网络的显著特征在于其能够求解不适定（病态）反问题。该神经网络采用特殊的两级算法结构：算法底层使用两个架构等效的神经网络，其中第一个网络通过远场表面重力测量数据计算给定域内的重力场源分布，第二个网络则处理同一表面测量的磁场数据以确定相同域内的磁源分布。在算法上层，通过计算所得源分布的结构残差来量化其几何形态的差异（接近）程度。通过在上层神经网络训练过程中最小化该残差，最终实现能够生成几何形态相近的不同场源分布的计算算法。本文详细探讨了神经网络各组件及相关算法（数据集构建、训练流程、损失函数具体形式等）的优化可能性。模型问题的测试计算表明，经优化的神经网络联合反演方法具有高质量的反演性能。研究同时针对巴西戈亚斯州Jussara地区的实际重磁勘探数据进行了联合处理计算。此外，本文还探讨了在联合场反演中不仅确定场源几何分布，同时反演其物理强度的问题。