Density Prediction from Full Waveform Inversion with Gravity Gradient Constraints
Adequate density information is essential for geophysical reservoir prediction and lithological interpretation. Achieving optimal density results through full waveform inversion (FWI) poses a consistent challenge. The low wavenumber component essential for density, easily derived from gravity data,...
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Veröffentlicht in: | IEEE geoscience and remote sensing letters 2024-01, Vol.21, p.1-1 |
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description | Adequate density information is essential for geophysical reservoir prediction and lithological interpretation. Achieving optimal density results through full waveform inversion (FWI) poses a consistent challenge. The low wavenumber component essential for density, easily derived from gravity data, is often unavailable in seismic inversion. Integrating gravity information into the seismic field can provide long wavelength density information and reduce the feasible solution space. Hence, we propose a joint gravity-seismic inversion method and develop a joint inversion objective function. We start by inverting the low wavenumber component for density using gravity data, followed by FWI with gravity gradient constraints for the high wavenumber information. Numerical examples show that our method can improve numerical accuracy and recover an accurate density model, providing petrophysical guidance for resource exploration. |
doi_str_mv | 10.1109/LGRS.2024.3397889 |
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Achieving optimal density results through full waveform inversion (FWI) poses a consistent challenge. The low wavenumber component essential for density, easily derived from gravity data, is often unavailable in seismic inversion. Integrating gravity information into the seismic field can provide long wavelength density information and reduce the feasible solution space. Hence, we propose a joint gravity-seismic inversion method and develop a joint inversion objective function. We start by inverting the low wavenumber component for density using gravity data, followed by FWI with gravity gradient constraints for the high wavenumber information. 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subjects | Components Data models Density full waveform inversion Gravity Gravity data gravity gradient joint inversion Kernel Linear programming Lithology Mathematical models Objective function Resource exploration Seismic surveys Sensitivity Solution space Tensors Waveforms Wavelength Wavelengths |
title | Density Prediction from Full Waveform Inversion with Gravity Gradient Constraints |
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