The method of functional representations in the solution of inverse problems of gravimetry

Kobrunov, A.I.

doi:10.1134/s1069351315030076

Cited by 6 publications

(2 citation statements)

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“…Priezzhev’s method leverages rapid computation in the wavenumber domain, combining a priori information with gravity field data 19 , 20 . Kobrunov’s iterative inversion approach, based on functional representation, expeditiously produces sub-surface density and structural models 21 . Using depth scaling factors, Cui and Guo recently enhanced the wavenumber domain density imaging technique to produce high-resolution density models 22 .…”

Section: Introductionmentioning

confidence: 99%

3D density imaging using gravity and gravity gradient in the wavenumber domain and its application in the Decorah

He,

Fang,

Guo

et al. 2024

Sci Rep

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Density imaging is a method that uses the inversion of the gravity and gravity gradient spectra in the wavenumber domain to create accurate 3D reconstructions of subsurface density distributions. This approach offers computational efficiency and rapid calculations. This research used preliminary inversions to examine the spectral characteristics of gravity and gravity gradient anomalies, as well as the resulting models, were scrutinized through preliminary inversions. 3D density imaging of gravity and gravity gradient was performed in the wavenumber domain using depth weighting on both noise-added and theoretical data, producing a density model that was consistent with the theoretical one. The technique was then used in the Decorah region of the United States, where 3D density imaging was performed and an examination of the properties of gravity and gravity gradient anomalies was conducted. The results showed where high-density Decorah complexes, low-density siliceous intrusive rocks, and high-density intrusive rock masses, were the distributed within the surrounding rock. Each of these provided comprehensive insights into the intrusive pathways to the rock mass. Thus, the appropriateness and effectiveness of the density imaging method were confirmed, supporting a deeper understanding of the structural division and geological evolution in the region.

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Section: Introductionmentioning

confidence: 99%

3D density imaging using gravity and gravity gradient in the wavenumber domain and its application in the Decorah

He,

Fang,

Guo

et al. 2024

Sci Rep

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“…Kobrunov and Varfolomeev [24] and Kobrunov [25] presented the wavenumber-domain approach for imaging the density distribution from the gravity anomalies and provided the iterative framework of the approach. In their approach, the spectra of gravity anomalies are deconvolved by a 2-D generalized filter describing the spectrum of imaging operator, and then the resultant spectra are transformed into the space domain to derive the density distribution.…”

Section: Introductionmentioning

confidence: 99%

A Wavenumber-Domain Iterative Approach for Rapid 3-D Imaging of Gravity Anomalies and Gradients

Cui

Guo

2019

IEEE Access

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We present a wavenumber-domain iterative approach for rapid 3-D imaging of gravity anomalies and gradients data, which is based on the 3-D mesh model with a flat observational surface. The approach deconvolves the spectra of gravity anomalies or gradients by a 2-D deconvolution filter describing the spectrum of the imaging operator and then transforms the resultant spectra into the space domain to derive the density distribution. This 2-D deconvolution filtering is operated layer by layer from top to bottom in the subsurface and finally, all the results are merged to generate the 3-D density distribution. We improve previous 2-D deconvolution filters by involving a depth-scaling factor and utilize a priori constraint and the iteration algorithm for imaging, enable the presented approach to produce a density model with a considerable resolution and accuracy. The wavenumber-domain algorithm makes the imaging faster than the conventional space-domain inversions. Tests on the synthetic data and the real data from a metallic deposit area in Northwest China verified the feasibility and high efficiency of the presented approach.INDEX TERMS 3-D imaging, gravity anomaly, gradients, iterative, wavenumber domain.

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Parameter identification by deep learning of a material model for granular media

Tanyu,

Michel,

Rademacher

et al. 2024

Int J Geomath

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Classical physical modeling with associated numerical simulation (model-based), and prognostic methods based on the analysis of large amounts of data (data-driven) are the two most common methods used for the mapping of complex physical processes. In recent years, the efficient combination of these approaches has become increasingly important. Continuum mechanics in the core consists of conservation equations that-in addition to the always-necessary specification of the process conditions-can be supplemented by phenomenological material models. The latter are an idealized image of the specific material behavior that can be determined experimentally, empirically, and based on a wealth of expert knowledge. The more complex the material, the more difficult the calibration is. This situation forms the starting point for this work’s hybrid data-driven and model-based approach for mapping a complex physical process in continuum mechanics. Specifically, we use data generated from a classical physical model by the MESHFREE software (MESHFREE Team in Fraunhofer ITWM & SCAI: MESHFREE. https://www.meshfree.eu, 2023) to train a Principal Component Analysis-based neural network (PCA-NN) for the task of parameter identification of the material model parameters. The obtained results highlight the potential of deep-learning-based hybrid models for determining parameters, which are the key to characterizing materials occurring naturally such as sand, soil, mud, or snow. The motivation for our research is the simulation of the interaction of vehicles with sand. However, the applicability of the presented methodology is not limited to this industrial use case. In geosciences, when predicting the runout zones of landslides or avalanches and evaluating corresponding protective measures, the parameterization of the respective material model is essential.

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The method of functional representations in the solution of inverse problems of gravimetry

Cited by 6 publications

References 0 publications

3D density imaging using gravity and gravity gradient in the wavenumber domain and its application in the Decorah

3D density imaging using gravity and gravity gradient in the wavenumber domain and its application in the Decorah

A Wavenumber-Domain Iterative Approach for Rapid 3-D Imaging of Gravity Anomalies and Gradients

Parameter identification by deep learning of a material model for granular media

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