A neural network approximation algorithm for solving inverse geoelectrics problems in the class of grid (block) models of media is presented. The algorithm is based on using neural networks for constructing an approximate inverse operator and enables formalized construction of solutions of inverse geoelectrics problem with a total number of sought-for medium parameters of ~ n · 10³. The correctness of the problem of constructing neural network inverse operators is considered. A posteriori estimates of the degree of ambiguity of solutions of the resulting inverse problem are calculated. The operation of the algorithm is illustrated by examples of 2D and 3D inversions of synthetic and field geoelectric data obtained by the MTS method.

中文翻译：

---

基于神经网络逼近的地电反问题求解算法

提出了一种神经网络近似算法，用于求解介质网格（块）模型中的逆地电问题。该算法基于使用神经网络构造近似逆算子的方法，并能够正式构造反地球电学问题解的形式，所寻找的介质参数总数约为〜n ·10 ³。考虑构造神经网络逆算子问题的正确性。计算所得反问题解的歧义程度的后验估计。通过MTS方法获得的合成和现场地电数据的2D和3D反演示例说明了该算法的操作。