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RSS FeedsRemote Sensing, Vol. 14, Pages 4651: Joint Inversion of 3D Gravity and Magnetic Data under Undulating Terrain Based on Combined Hexahedral Grid (Remote Sensing)

 
 

17 september 2022 12:09:12

 
Remote Sensing, Vol. 14, Pages 4651: Joint Inversion of 3D Gravity and Magnetic Data under Undulating Terrain Based on Combined Hexahedral Grid (Remote Sensing)
 


As an effective underground imaging method, the joint inversion of the gravity and magnetic data has an important application in the comprehensive interpretation of mineral exploration, and unstructured modeling is the key to accurately solving its topographic problem. However, the traditional tetrahedral grid can only impose the gradient-based constraints approximately, owing to its poor arrangement regularity. To address the difficulty of applying a cross-gradient constraint in an unstructured grid, we propose a joint inversion based on a combined hexahedral grid, which regularly divides the shallow part into curved hexahedrons and the deep part into regular hexahedrons. Instead of a cross-gradient in the spatial sense, we construct a geometric sense “cross-gradient” for a structural constraint to reduce the influence of approximation. In addition, we further correct the traditional sensitivity-based weighting function according to element volume, to make it suitable for an unstructured grid. Model tests indicate that the new grid can impose the cross-gradient constraint more strongly, and the proposed correction can effectively solve the false anomaly caused by the element volume difference. Finally, we apply our method to the measured data from a mining area in Huzhong, Heilongjiang Province, China, and successfully invert out the specific location of a known skarn deposit, which further proves its practicability.


 
100 viewsCategory: Geology, Physics
 
Remote Sensing, Vol. 14, Pages 4650: Geospatial Technology-Based Analysis of Air Quality in India during the COVID-19 Pandemic (Remote Sensing)
Remote Sensing, Vol. 14, Pages 4653: Responses of Satellite Chlorophyll-a to the Extreme Sea Surface Temperatures over the Arabian and Omani Gulf (Remote Sensing)
 
 
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