Joint inversion method based on magnetotelluric and direct current resistivity data

Abstract

The invention discloses a joint inversion method based on magnetotelluric and DC resistivity data, and the method comprises the steps: determining an inversion region according to observation data, carrying out the subdivision to obtain an initial grid, and setting an initial value of an inversion parameter vector; performing Gaussian-Newton inversion iteration based on the current value of the inversion parameter vector to calculate a model change amount and a linear search step length, and then calculating an iterative update value of the inversion parameter vector; judging whether an inversion termination condition is met or not, and if not, carrying out next iteration; if so, judging whether a progressive grid inversion termination condition is reached or not, and if so, ending the inversion; and if not, carrying out refined subdivision on the grid cell, updating the inversion parameter vector value, and returning to carry out Gauss-Newton inversion iteration until inversion is finished. According to the invention, a joint inversion technology of inversion grid adaptive adjustment is researched and developed, the problem of multiplicity of solutions of joint inversion interpretation of a magnetotelluric method and direct current resistivity data is effectively reduced, and the joint interpretation accuracy is improved.

Description

technical field [0001] The invention relates to the field of applied geophysics, in particular to a joint inversion method based on magnetotelluric and DC resistivity data. Background technique [0002] In the field of applied geophysical exploration, magnetotelluric method (MT) and direct current resistance method (DC) are widely used in mineral resource exploration, and the application effect is remarkable. However, the single magnetotelluric method or DC resistance method can hardly avoid the problem of multi-solution inversion, which affects the accuracy of data interpretation, and thus the joint inversion interpretation technology is derived. Joint inversion can effectively reduce the ambiguity of inversion and improve the accuracy and reliability of data interpretation. In addition, most of the traditional joint inversion methods are carried out under a fixed forward and inversion grid, which brings extreme instability to data interpretation. This is because a high-p...

AI Technical Summary

Problems solved by technology

In addition, most of the traditional joint inversion methods are carried out under a fixed forward and inversion grid, which brings extreme instability to data interpretation

This is because a high-precision forward modeling algorithm is a prerequisite for inversion. Obtaining high-precision forward modeling results will inevitably increase the grid density, which will lead to a sharp increase in the number of grid cells for inversion, thus aggravating the inversion. Secondly, if the density of the inversion grid is red

Images