Abstract

Neural networks (NN) are widely used for solving various problems of geophysical data interpretation and processing. The application of the neural network approximation (NNA) method for solving inverse problems, including inverse multicriteria problems of geophysics that are reduced to a nonlinear operator equation of first kind (respectively, to a system of operator equations) is considered. The NNA method assumes the construction of an approximate inverse operator of the problem using neural network approximation designs (MLP networks) on the basis of a preliminary constructed set of reference solutions to direct and inverse problems. A review of the application of the NNA method for solving nonlinear inverse problems of geophysics is given. Techniques for estimating the practical ambiguity (error) of approximate solutions to inverse multicriteria problems are considered. Results of solving the inverse two-criteria 2D gravimetry problem in combination with magnetometry are presented.

中文翻译：

---

神经网络在地球物理非线性反问题中的应用

摘要

神经网络（NN）被广泛用于解决地球物理数据解释和处理的各种问题。考虑了将神经网络逼近（NNA）方法用于求解反问题的应用，这些问题包括地球物理的反多准则问题，这些问题被简化为第一类非线性算子方程（分别对应于算子方程组）。NNA方法假设在神经网络逼近设计（MLP网络）的基础上，构造了针对直接和反问题的参考解的初步构造，从而构造了问题的近似逆算子。综述了NNA方法在解决地球物理非线性反问题中的应用。考虑了用于估计逆多准则问题的近似解的实际歧义（误差）的技术。提出了结合磁强法解决反二维准则二维重力法的问题的结果。