Joint inversion of gravity and gravity gradient data using a binary formulation

2015 ◽  
Author(s):  
Joseph Capriotti ◽  
Yaoguo Li ◽  
Richard Krahenbuhl
Keyword(s):  
Joint Inversion ◽  
Gravity Gradient ◽  
Binary Formulation

scholarly journals Unveiling the 3D undercover structure of a Precambrian intrusive complex by integrating airborne magnetic and gravity gradient data into 3D quasi-geology model building

2020 ◽  
Vol 8 (4) ◽  
pp. SS15-SS29 ◽  
Author(s):  
Jiajia Sun ◽  
Aline Tavares Melo ◽  
Jae Deok Kim ◽  
Xiaolong Wei
Keyword(s):  
High Resolution ◽  
Model Building ◽  
Country Rock ◽  
Joint Inversion ◽  
Sedimentary Cover ◽  
Geophysical Methods ◽  
Added Value ◽  
Gravity Gradient ◽  
Airborne Magnetic

Mineral exploration under a thick sedimentary cover naturally relies on geophysical methods. We have used high-resolution airborne magnetic and gravity gradient data over northeast Iowa to characterize the geology of the concealed Precambrian rocks and evaluate the prospectivity of mineral deposits. Previous researchers have interpreted the magnetic and gravity gradient data in the form of a 2D geologic map of the Precambrian basement rocks, which provides important geophysical constraints on the geologic history and mineral potentials over the Decorah area located in the northeast of Iowa. However, their interpretations are based on 2D data maps and are limited to the two horizontal dimensions. To fully tap into the rich information contained in the high-resolution airborne geophysical data, and to further our understanding of the undercover geology, we have performed separate and joint inversions of magnetic and gravity gradient data to obtain 3D density contrast models and 3D susceptibility models, based on which we carried out geology differentiation. Based on separately inverted physical property values, we have identified 10 geologic units and their spatial distributions in 3D which are all summarized in a 3D quasi-geology model. The extension of 2D geologic interpretation to 3D allows for the discovery of four previously unidentified geologic units, a more detailed classification of the Yavapai country rock, and the identification of the highly anomalous core of the mafic intrusions. Joint inversion allows for the classification of a few geologic units further into several subclasses. We have demonstrated the added value of the construction of a 3D quasi-geology model based on 3D separate and joint inversions.

3D stochastic joint inversion of borehole gravity and gravity gradient data using cokriging

2014 ◽  
Author(s):  
Meixia Geng* ◽  
Danian Huang ◽  
Ping Yu ◽  
Qingjie Yang
Keyword(s):  
Joint Inversion ◽  
Gravity Gradient

Joint inversion of gravity and gravity gradient data: A systematic evaluation

Geophysics ◽  
2021 ◽  
pp. 1-93
Author(s):  
Joseph Capriotti ◽  
Yaoguo Li
Keyword(s):  
Noise Level ◽  
Joint Inversion ◽  
Low Noise ◽  
Systematic Evaluation ◽  
Gravity Gradient ◽  
Gravity Inversion ◽  
Data Sets ◽  
Noise Levels ◽  
Data Set ◽  
Test Range

Gravity and gravity gradiometry measurements are commonly used to map density variations in the subsurface. Gravity measurements can characterize gravitational anomalies at both long and short wavelengths effectively, but the cost of collecting a sufficiently spatially dense survey to characterize the short wavelengths can be prohibitive. Gravity gradient data can be quickly collected with short wavelength information at a low noise level, but have decreasing sensitivity to longer wavelengths. We describe a method to jointly invert gravity and gravity gradient data that takes advantage of the differing frequency contents and noise levels of the two methods to create an improved image of the subsurface. Previous work simply treated the inversion as a multiple component gravity inversion, however this can cause unintended errors in the recovered models because each data set is not guaranteed to be fit within its noise level. Our joint inversion methodology ensures that both the gravity and gravity gradient data sets are fit to within their individual noise levels by incorporating a relative weighting parameter, and we describe how to find that parameter. This method can also be used to create an improved broadband gravity anomaly map that has a reduced noise level at long wavelengths using a joint equivalent source reconstruction. We first build a synthetic model using a Minecraft world editor, that has different wavelength anomalies, and show the improvement with joint inversion. These results are also confirmed using a real world example at the R. J. Smith test range in Kauring, Australia.

Joint inversion of the lithospheric density struture in the North China Craton based on GOCE satellite gravity gradient data and surface gravity data

2020 ◽  
Author(s):  
Yu Tian ◽  
Yong Wang
Keyword(s):  
North China ◽  
Joint Inversion ◽  
Gravity Data ◽  
Initial Density ◽  
Gravity Gradient ◽  
Density Structure ◽  
Satellite Gravity ◽  
Density Data ◽  
The North ◽  
Goce Satellite

<p>The North China Craton (NCC) is one of the oldest cratons in the world. Currently, the destruction mechanism and geodynamics of the NCC still remain controversial. All of the proposed views regarding the issues involve studying the internal density structure of the NCC lithosphere. Gravity field data are one of the most important data in regard to investigating the lithospheric density structure, the gravity gradient data and the gravity data possess their own advantages. Given the inconsistency of the on orbit GOCE satellite gravity gradient and surface gravity observation plane height, also effects of the initial density model upon of the inversion results, the joint inversion of gravity gradient and gravity are divided into two integrated processes. By using the preconditioned conjugate gradient (PCG) inversion algorithm, the density data are calculated using the preprocessed remaining gravity anomaly data. The newly obtained high resolution density data are then used as the initial density model, which can be served as the constraints for the subsequent gravity gradient inversion. Downward continuation, terrain correction, interface undulation correction and long wavelength correction are performed for the four gravity gradient tensor data(<strong>T</strong><sub>xx</sub>，<strong>T</strong><sub>xz</sub>，<strong>T</strong><sub>yy</sub>，<strong>T</strong><sub>zz</sub>)of the Gravity field and steady-state Ocean Circulation Explorer (GOCE) satellite,  after which the remaining gravity gradient anomaly data(<strong>T</strong>'<sub>xx</sub>，<strong>T</strong>'<sub>xz</sub>，<strong>T</strong>'<sub>yy</sub>，<strong>T</strong>'<sub>zz</sub>) are used as the new observation quantity. Finally, the ultimate lithospheric density distribution within the depth range of 0–180 km in the NCC is obtained using the same PCG algorithm.</p>

3D joint inversion of gravity-gradient and borehole gravity data

2017 ◽  
Vol 48 (2) ◽  
pp. 151-165 ◽  
Author(s):  
Meixia Geng ◽  
Qingjie Yang ◽  
Danian Huang
Keyword(s):  
Joint Inversion ◽  
Gravity Data ◽  
Gravity Gradient

scholarly journals Joint inversion of the lithospheric density struture in the North China Craton based on GOCE satellite gravity gradient data and surface gravity data

2020 ◽  
Author(s):  
Yu Tian ◽  
Yong Wang
Keyword(s):  
North China ◽  
Joint Inversion ◽  
Gravity Data ◽  
Initial Density ◽  
Gravity Gradient ◽  
Density Structure ◽  
Satellite Gravity ◽  
Density Data ◽  
The North ◽  
Goce Satellite

Abstract. The North China Craton (NCC) is one of the oldest cratons in the world. Currently, the destruction mechanism and geodynamics of the NCC still remain controversial. All of the proposed views regarding the issues involve studying the internal density structure of the NCC lithosphere. Gravity field data are one of the most important data in regard to investigating the lithospheric density structure, the gravity gradient data and the gravity data possess their own advantages. Given the inconsistency of the on orbit GOCE satellite gravity gradient and surface gravity observation plane height, also effects of the initial density model upon of the inversion results, the joint inversion of gravity gradient and gravity are divided into two integrated processes. By using the preconditioned conjugate gradient (PCG) inversion algorithm, the density data are calculated using the preprocessed remaining gravity anomaly data. The newly obtained high resolution density data are then used as the initial density model, which can be served as the constraints for the subsequent gravity gradient inversion. Several essential corrections are performed for the four gravity gradient tensor (Txx，Txz，Tyy，Tzz) of the GOCE satellite, after which the remaining gravity gradient anomaly (T'xx，

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