The Study Of Three-dimensional Inversion Method Based On The Gravity Data For The Buried Depth And Physical Propetry Of Geological Bodies

The determination of the buried depth and physical parameters of geologicalbody is the main task of gravity anomaly interpretation, and it is the hot spot ofgravity inversion. In this paper, according to determine depth of geological body bygravity data, we select the normalized total gravity gradient method and the depthfrom extreme points (DEXP) method for study. In order to determine physicalparameters of geological body, we introduce the focusing inversion method. Then wedo further studies in order to improve the practicality of these methods and theaccuracy of the results.The normalized total gravity gradient method can effectively obtain the spatialdistribution of geological body without any priori information. In this paper wesystematically analyze the influence of the normalized total gravity gradient inversionresults with different continuation method and normalization ways. After model testand comparison, the normalized total gravity gradient inversion results that obtainedby combination of adaptive iterative regularization and geometric mean normalizationhave higher resolution and accuracy.DEXP-Depth inversion imaging method can quickly and efficiently estimate theburied depth of geological body. In this paper we use it to estimate depth of geologicalbody by gravity anomaly. It can suppress background noise efficiently, and we extendthe method to three-dimensional gravity inversion calculation.The physical property (density) inversion is based on gravity to obtain thedistribution range and attribute features of geological body. Focusing inversion is oneof the most practical and effective method nowadays. In this paper, depth weightingfactor is introduced into the gravity anomaly data focusing inversion. The skin effectof the inversion results is greatly reduced, and the effect of focusing inversion isimproved. We introduce depth and density weighting factor into the re-weightedregularization conjugate gradient inversion algorithm, and the experimental modeland real data interpretation show that the improved algorithm inversion result isreliable. It has more broad application prospects.