sparse norm and cross-gradient inversions of gravity and magnetic data sets utilizing open-source resources in python (case study: hematite ore body in jalal abad area (iran))

The gravity and the magnetic data sets are utilized to model the hematite ore body. the cross-gradient joint inversion is used to invert the data sets simultaneously. to discretize the model space, the advanced meshing algorithm (octree mesh) has been applied. the sparse norm and cross-gradient inversion modules in python, accessible through simulation and parameter estimation in geophysics (simpeg, version 0.17.0) website, have been applied to the inversion process. the sparse norm inversions do not provide reasonable results, particularly for the gravity data set. the estimated density contrasts through the inversion process are very low and unrealistic and on the other hand, the north-south cross sections do not represent a real image from the subsurface sources. the magnetic modeling results obtained through sparse norm inversion also show unrealistic characters, particularly for the 3-dimensional figure of the subsurface anomaly.the cross-gradient inversion acts quite successfully for both gravity and magnetic models in spite of high noise level in gravity data and the weak signal of magnetic data. the results are in good agreement with geological evidences and also former geophysical survey in the survey area. the priority of cross-gradient inversion of gravity and magnetic data sets to separate inversion is quite clear, despite the weak magnetic signal.

sparse norm and cross-gradient inversions of gravity and magnetic data sets utilizing open-source resources in python (case study: hematite ore body in jalal abad area (iran))

the gravity and the magnetic data sets are utilized to model the hematite ore body. the cross-gradient joint inversion is used to invert the data sets simultaneously. to discretize the model space, the advanced meshing algorithm (octree mesh) has been applied. the sparse norm and cross-gradient inversion modules in python, accessible through simulation and parameter estimation in geophysics (simpeg, version 0.17.0) website, have been applied to the inversion process. the sparse norm inversions do not provide reasonable results, particularly for the gravity data set. the estimated density contrasts through the inversion process are very low and unrealistic and on the other hand, the north-south cross sections do not represent a real image from the subsurface sources. the magnetic modeling results obtained through sparse norm inversion also show unrealistic characters, particularly for the 3-dimensional figure of the subsurface anomaly.the cross-gradient inversion acts quite successfully for both gravity and magnetic models in spite of high noise level in gravity data and the weak signal of magnetic data. the results are in good agreement with geological evidences and also former geophysical survey in the survey area. the priority of cross-gradient inversion of gravity and magnetic data sets to separate inversion is quite clear, despite the weak magnetic signal.