Euler deconvolution of gravity tensor gradient data

Zhang, Changyou; Mushayandebvu, M.F.; Reid, Alan; Fairhead, J.D.; Odegard, Mark E.

doi:10.1190/1.1444745

Cited by 171 publications

(89 citation statements)

References 20 publications

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“…Various analysis techniques using gravity gradient tensors have been suggested and discussed (e.g., Zhang et al 2000;Beiki 2010;Martinez et al 2013;Cevallos 2014;Li 2015). These are considered to be so-called inversion techniques.…”

Section: Introductionmentioning

confidence: 99%

Eigenvector of gravity gradient tensor for estimating fault dips considering fault type

Kusumoto

2017

Prog. in Earth and Planet. Sci.

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The dips of boundaries in faults and caldera walls play an important role in understanding their formation mechanisms. The fault dip is a particularly important parameter in numerical simulations for hazard map creation as the fault dip affects estimations of the area of disaster occurrence. In this study, I introduce a technique for estimating the fault dip using the eigenvector of the observed or calculated gravity gradient tensor on a profile and investigating its properties through numerical simulations. From numerical simulations, it was found that the maximum eigenvector of the tensor points to the high-density causative body, and the dip of the maximum eigenvector closely follows the dip of the normal fault. It was also found that the minimum eigenvector of the tensor points to the low-density causative body and that the dip of the minimum eigenvector closely follows the dip of the reverse fault. It was shown that the eigenvector of the gravity gradient tensor for estimating fault dips is determined by fault type. As an application of this technique, I estimated the dip of the Kurehayama Fault located in Toyama, Japan, and obtained a result that corresponded to conventional fault dip estimations by geology and geomorphology. Because the gravity gradient tensor is required for this analysis, I present a technique that estimates the gravity gradient tensor from the gravity anomaly on a profile.

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Section: Introductionmentioning

confidence: 99%

Eigenvector of gravity gradient tensor for estimating fault dips considering fault type

Kusumoto

2017

Prog. in Earth and Planet. Sci.

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“…However, differentiation of the analytic signal amplitude may amplify high frequencies. Zhang et al (2000) employed the Euler's homogeneity equation for interpretation of GGT data and showed that the standard Euler deconvolution can be extended to where B x , B y and B z are the regional values of components of gravity vector g x , g y and g z to be estimated. Their method uses the full GGT and the components of the gravity vector to provide additional constraints on the Euler solutions.…”

Section: Euler Deconvolution Techniquementioning

confidence: 99%

New techniques for estimation of source parameters: Applications to airborne gravity and pseudo-gravity gradient tensors

Beiki

2011

GEOPHYSICS

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“…• Tensor Euler (Zhang, 2000), a 3D tensor Euler solution of the gridded data to solve for target source location.…”

Section: Data Processing and Inversionmentioning

confidence: 99%

Development and Evaluation of an Airborne Superconducting Quantum Interference Device-Based Magnetic Gradiometer Tensor System for Detection, Characterization and Mapping of Unexploded Ordnance

Gamey¹

2008

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Euler deconvolution of gravity tensor gradient data

Cited by 171 publications

References 20 publications

Eigenvector of gravity gradient tensor for estimating fault dips considering fault type

Eigenvector of gravity gradient tensor for estimating fault dips considering fault type

New techniques for estimation of source parameters: Applications to airborne gravity and pseudo-gravity gradient tensors

Development and Evaluation of an Airborne Superconducting Quantum Interference Device-Based Magnetic Gradiometer Tensor System for Detection, Characterization and Mapping of Unexploded Ordnance

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