Magnetotelluric deep neural network inversion method based on space mapping technology

Abstract

The invention discloses a magnetotelluric deep neural network inversion method based on a space mapping technology. The magnetotelluric deep neural network inversion method mainly comprises the following steps: 1) determining a detection area; 2) establishing a geoelectric model sample set A2; 3) establishing a magnetotelluric forward modeling response data set A3; 4) performing normalization processing; 5) establishing a deep learning neural network model; 6) obtaining a trained deep learning neural network model;7) obtaining a layered geoelectric section electromagnetic prediction data set;8) establishing a layered geoelectric section electromagnetic verification data set; 9) judging whether the fitting degree error of the layered geoelectric section electromagnetic prediction data setand the layered geoelectric section electromagnetic verification data set meets a convergence condition or not, if so, ending inversion, and outputting the layered geoelectric section electromagneticverification data set. The method can be widely applied to the field of magnetotelluric inversion imaging, and has good practical value and application prospect for quickly and accurately predicting the underground electrical structure.

Description

technical field [0001] The invention relates to the field of geophysical magnetotelluric neural network inversion, in particular to a magnetotelluric deep neural network inversion method based on space mapping technology. Background technique [0002] Magnetotelluric (MT) is a branch method of electromagnetic sounding by changing the frequency of the electromagnetic field. Usually, the field source is a vertically incident magnetic field, and the propagation of the underground electromagnetic field satisfies Maxwell's equations. The propagation problem is the modeling problem of magnetotelluric imaging. Magnetotelluric imaging uses the inversion method to obtain the geoelectric model of the underground structure, and infers the underground structure, that is, electromagnetic imaging. Inversion is an extremely critical step in the processing and interpretation of MT data. At present, MT has entered the stage of two-dimensional or even three-dimensional inversion from the ini...

AI Technical Summary

Problems solved by technology

[0005] Although the existing research on using artificial intelligence algorithms to predict the geoelectric structure has achieved some results, it can only satisfy the prediction of the layered geoelectric model with a small number of layers, and the resistivity range is limited, so it cannot be applied to more complex geoelectric structures. Geoelectric structure

The main reasons for this problem are as follows: For the layered geoelectric model, although the parameter inversion imaging established by the artificial intelligence algorithm can obtain layer thickness and resistivity information at the same time, the learning cost increases sharply as the number of layers increases. The amount of calculation in the model also increases sharply; when the number of formation layers is small, the sharp change of the resistivity value does not conform to the actual geological significance

Not only that, the inaccuracy of the electrical parameters of a certain layer has a great influence on the overall geoelectric structure

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