3d gravity cross-correlation imaging for large scale data analysis: application to the crustal structure of iran

We propose the 3d gravity crosscorrelation method to large scale data analyses as a fast analysis method to image the underground mass distribution. this method presents the crosscorrelation product of the observed gravity anomaly (or its vertical gradient) and the calculated field due to an elementary mass contrast source. the crosscorrelation product of the domain is used to highlight the zones of the highest probability of mass concentrations. first, some synthetic examples demonstrate the reliability and resolution of the method. the synthetic models discover different parameters of investigation space as space dimensions and densities. tests with synthetic bodies show that the resultant correlation coefficients of the approach can delineate causative bodies in the subsurface. finally, terrestrial gravity anomaly data of iran is used to study the crustal structure and the moho depth of iran. the result is in a good agreement compared with other research studies of the domain. this technique took about five minutes to calculate the 3d gravity crosscorrelation of the whole terrestrial gravity data set of iran (25,937 data) a computer. hence, it can easily be used repeatedly to monitor changes of gravity field.

3D Gravity Cross-Correlation Imaging for Large Scale Data Analysis: Application to the Crustal Structure of Iran

We propose the 3D gravity crosscorrelation method to large scale data analyses as a fast analysis method to image the underground mass distribution. This method presents the crosscorrelation product of the observed gravity anomaly (or its vertical gradient) and the calculated field due to an elementary mass contrast source. The crosscorrelation product of the domain is used to highlight the zones of the highest probability of mass concentrations. First, some synthetic examples demonstrate the reliability and resolution of the method. The synthetic models discover different parameters of investigation space as space dimensions and densities. Tests with synthetic bodies show that the resultant correlation coefficients of the approach can delineate causative bodies in the subsurface. Finally, terrestrial gravity anomaly data of Iran is used to study the crustal structure and the Moho depth of Iran. The result is in a good agreement compared with other research studies of the domain. This technique took about five minutes to calculate the 3D gravity crosscorrelation of the whole terrestrial gravity data set of Iran (25,937 data) a computer. Hence, it can easily be used repeatedly to monitor changes of gravity field.