Gravity Field Density Inversion Method Based on Quasi Radial Basis Function Neural Network

Abstract

The invention provides a gravity field density inversion method based on a quasi-radial basis function neural network. The method comprises a step of establishing a gravity observation system, a stepof establishing a gridded model, a step of establishing a gravity forward kernel function matrix, a step of establishing a radial basis function neural network, a step of training the neural network,and a step of outputting an inversion result. According to the method, a model space is compressed by using a radial basis function, and the dimensionality reduction of inversion parameters is achieved under the premise of ensuring complex model representation ability. A pseudo-neural network structure is proposed, the training of a sample tag is not needed, the difficulty of establishing a training data set is avoided, and a gravity field density inversion algorithm is achieved based on the pseudo-neural network structure. The vertical resolution and reliability of an inversion result are improved, the method has strong anti-noise ability, and the application field of a gravity inversion method is extended.

Description

technical field [0001] The invention belongs to the field of geophysical inversion, and specifically refers to a gravity inversion method, in particular to a method for inverting an underground target density model based on a quasi-radial basis function neural network using gravity field data. Background technique [0002] Gravity prospecting is widely used in regional geological surveys, basin framework studies, and deep and large fault reconnaissance because of its fast data collection and low price. It restricts its application in high-precision exploration fields, such as oil and gas exploration. The root cause of the above problems lies in the ill-posed nature of the inverse gravity problem. First, the density of gravity data collection is low and the amount of data is small, while high-resolution inversion requires high-density subdivision of the underground half-space, making the number of unknown parameters in the inversion far greater than the number of data, resul...

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Problems solved by technology

[0006] The object of the present invention proposes a gravity field density inversion method based on a pseudo-radial basis function neural network, which can solve the problems that the gravity inversion of the existing inversion method is ill-posed and cannot improve the vertical resolution of the gravity inversion

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