3-D data-space joint inversion of gravity and magnetic data using a correlation-analysis constraint
| Autor: | Xiang Liu, Songbai Xuan, Shuanggen Jin, Sheng Liu |
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| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Annals of Geophysics. 65 |
| ISSN: | 2037-416X 1593-5213 |
| Popis: | Non-uniqueness, low computational efficiency and large memory requirements are main issues for geophysical data inversion. In this paper, we propose an efficient algorithm for 3D correlationanalysis joint inversion of gravity and magnetic data with high accuracy and low computation effort. Firstly, since the number of the observed field data is smaller than the number of inverted parameters, the calculations of the correlation-analysis for joint inversion of gravity and magnetic data in model space (MS) are transformed into the equivalent calculations in data space (DS), which can reduce the dimensions of the calculation domain, improve the computation efficiency and reduce the non-uniqueness. Then, an improved conjugate gradient (ICG) method is employed for the optimization algorithm, which can facilitate the use of stable functions with sparse factors and improve the accuracy of the inversion. The inversion performed by the combined DS-ICG method for synthetic data tests shows the calculation effort can be effectively reduced, and the issues with non-uniqueness are improved. Finally, the test by real field data can delineate the distribution of underground geological bodies, which illustrates the strong stability and good applicability of our extended method. |
| Databáze: | OpenAIRE |
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