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3D organic matter modeling: a novel tool in forward stratigraphic modeling [PDF]

Journal of Stratigraphy and Sedimentology Researches, 2023
The source rock characteristics (thickness, quantity and quality) can be varied both in the deposition of time and space. Currently, the distribution map of organic matter is estimated by simplified extrapolate methods using observed data on well ...
Mostafa Javid, Ziba Zamani
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Validation data of parallel 3D surface-borehole electromagnetic forward modeling

Data in Brief, 2020
Forward modeling of geophysical electromagnetic fields over large three-dimensional volumes is a heavy computational task that demands effective accelerating strategies. As a solution to this computational challenge, a hybrid parallel computing algorithm
Chong Liu, LiZhen Cheng, Bahman Abbassi
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Regional-scale full 3D hybrid waveform forward modeling

Earth and Planetary Physics
Accurate three-dimensional (3D) velocity models are essential for fitting high-frequency seismic waveform records. This process usually requires regional-scale 3D numerical simulations that are computationally expensive, especially with sparse seismic ...
JingNan Sun, Yi Wang, Li Zhao, RiSheng Chu   +3 more
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1D Stochastic Inversion of Airborne Time-Domain Electromagnetic Data with Realistic Prior and Accounting for the Forward Modeling Error

Remote Sensing, 2021
Airborne electromagnetic surveys may consist of hundreds of thousands of soundings. In most cases, this makes 3D inversions unfeasible even when the subsurface is characterized by a high level of heterogeneity.
Peng Bai, Giulio Vignoli, Thomas Mejer Hansen   +2 more
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Massively parallel modeling and inversion of electrical resistivity tomography data using PFLOTRAN [PDF]

Geoscientific Model Development, 2023
Electrical resistivity tomography (ERT) is a broadly accepted geophysical method for subsurface investigations. Interpretation of field ERT data usually requires the application of computationally intensive forward modeling and inversion algorithms.
P. Jaysaval, G. E. Hammond, T. C. Johnson   +2 more
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Preface: 3D GeoInfo 2021 [PDF]

ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2021
3D GeoInfo 2021, the 16th GeoInfo conference, is an annual ISPRS workshop offering a forum for leading international decision makers and prominent voices in the field of 3D Geoinformation across the academic, commercial, and public sectors.
L. Truong-Hong, F. Jia, E. Che, S. Emamgholian, D. Laefer, A. V. Vo   +5 more
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Grounded source transient electromagnetic 3D forward modeling with the spectral-element method and its application in hydraulic fracturing monitoring

Frontiers in Earth Science, 2023
A long wire with large current source transient electromagnetic (TEM) monitoring, with a large detection depth, low cost, safety, and environmental protection, has unique advantages in the testing and identification of unconventional reservoir fluid and ...
Xin Huang, Liangjun Yan, Xingyu Wang, Xingbing Xie, Lei Zhou, Xiaoyue Cao   +5 more
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A Geometric Multigrid Method for 3D Magnetotelluric Forward Modeling Using Finite-Element Method

Remote Sensing, 2023
The traditional three-dimensional (3D) magnetotelluric (MT) forward modeling using Krylov subspace algorithms has the problem of low modeling efficiency.
Xianyang Huang, Changchun Yin, Luyuan Wang, Yunhe Liu, Bo Zhang, Xiuyan Ren, Yang Su, Jun Li, Hui Chen   +8 more
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Three-dimensional frequency-domain full waveform inversion based on the nearly-analytic discrete method

Earth and Planetary Physics, 2021
The nearly analytic discrete (NAD) method is a kind of finite difference method with advantages of high accuracy and stability. Previous studies have investigated the NAD method for simulating wave propagation in the time-domain.
DeYao Zhang, WenYong Pan, DingHui Yang, LingYun Qiu, XingPeng Dong, WeiJuan Meng   +5 more
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3D Airborne EM Forward Modeling Based on Time-Domain Spectral Element Method

Remote Sensing, 2021
Airborne electromagnetic (AEM) method uses aircraft as a carrier to tow EM instruments for geophysical survey. Because of its huge amount of data, the traditional AEM data inversions take one-dimensional (1D) models.
Changchun Yin, Zonghui Gao, Yang Su, Yunhe Liu, Xin Huang, Xiuyan Ren, Bin Xiong   +6 more
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