Full text loading...
-
Joint Inversion of Geophysical Data Based on Local Pearson Correlation Constraints
- Publisher: European Association of Geoscientists & Engineers
- Source: Conference Proceedings, 79th EAGE Conference and Exhibition 2017, Jun 2017, Volume 2017, p.1 - 5
Abstract
Since the distribution of different causative geophysical attributes are relevant, we develop a new joint inversion algorithm based on local correlation constraints. A window function is introduced to the Pearson correlation coefficient to measure linear correlations of each windowed subarea, in which the physical attributes are assumed to be linearly correlated. With the objective functional incorporating the correlation constraints decreasing in the iterative optimization process, the similarity of geophysical models with different attributes enhances gradually. We test our joint inversion algorithm on three-dimensional synthetic MT and gravity data. The results show that the new algorithm can effectively exploit different geophysical data for resolving the underground structures. Compared to single MT or gravity inversions, the joint inversion results deliver the accurate attribute values and the positions of underground structures.