3-D full-waveform inversion (FWI) is an advanced seismic imaging technique that has been widely adopted by the oil and gas industry to obtain high-fidelity models of P-wave velocity that lead to improvements in migrated images of the reservoir. Most industrial applications of 3-D FWI model the acoustic wavefield, often account for the kinematic effect of anisotropy, and focus on matching the low-frequency component of the early arriving refractions that are most sensitive to P-wave velocity structure. Here, we have adopted the same approach in an application of 3-D acoustic, anisotropic FWI to an ocean-bottom-seismometer (OBS) field data set acquired across the Endeavour oceanic spreading centre in the northeastern Pacific. Starting models for P-wave velocity and anisotropy were obtained from traveltime tomography; during FWI, velocity is updated whereas anisotropy is kept fixed. We demonstrate that, for the Endeavour field data set, 3-D FWI is able to recover fine-scale velocity structure with a resolution that is 2-4 times better than conventional traveltime tomography. Quality assurance procedures have been employed to monitor each step of the workflow; these are time consuming but critical to the development of a successful inversion strategy. Finally, a suite of checkerboard tests has been performed which shows that the full potential resolution of FWI can be obtained if we acquire a 3-D survey with a slightly denser shot and receiver spacing than is usual for an academic experiment. We anticipate that this exciting development will encourage future seismic investigations of earth science targets that would benefit from the superior resolution offered by 3-D FWI.
机构:
Hanyang Univ, RISE ML Reservoir Imaging Seism & EM Technol, Machine Learning Lab, Seoul 04763, South Korea
KIGAM Korea Inst Geosci & Mineral Resources, Daejeon 34132, South KoreaHanyang Univ, RISE ML Reservoir Imaging Seism & EM Technol, Machine Learning Lab, Seoul 04763, South Korea
Kim, Sooyoon
Park, Jiho
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Hanyang Univ, RISE ML Reservoir Imaging Seism & EM Technol, Machine Learning Lab, Seoul 04763, South Korea
KIGAM Korea Inst Geosci & Mineral Resources, Daejeon 34132, South KoreaHanyang Univ, RISE ML Reservoir Imaging Seism & EM Technol, Machine Learning Lab, Seoul 04763, South Korea
Park, Jiho
Seol, Soon Jee
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Hanyang Univ, RISE ML Reservoir Imaging Seism & EM Technol, Machine Learning Lab, Seoul 04763, South KoreaHanyang Univ, RISE ML Reservoir Imaging Seism & EM Technol, Machine Learning Lab, Seoul 04763, South Korea
机构:
Colorado Sch Mines, Dept Geophys, Golden, CO 80401 USA
Chevron Tech Ctr, Div Chevron USA Inc, Houston, TX USAColorado Sch Mines, Dept Geophys, Golden, CO 80401 USA
机构:
Univ Paris Cite, Sorbonne Univ, CNRS, Lab Jacques Louis Lions LJLL,Inria, F-75005 Paris, FranceUniv Paris Cite, Sorbonne Univ, CNRS, Lab Jacques Louis Lions LJLL,Inria, F-75005 Paris, France
Tournier, Pierre-Henri
Jolivet, Pierre
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CNRS, IRIT, F-31071 Toulouse 7, FranceUniv Paris Cite, Sorbonne Univ, CNRS, Lab Jacques Louis Lions LJLL,Inria, F-75005 Paris, France
Jolivet, Pierre
Dolean, Victorita
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Univ Strathclyde, Glasgow G1 1XQ, Lanark, ScotlandUniv Paris Cite, Sorbonne Univ, CNRS, Lab Jacques Louis Lions LJLL,Inria, F-75005 Paris, France
Dolean, Victorita
Aghamiry, Hossein S.
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Univ Cote Azur, LJAD, CNRS, Nice, FranceUniv Paris Cite, Sorbonne Univ, CNRS, Lab Jacques Louis Lions LJLL,Inria, F-75005 Paris, France
Aghamiry, Hossein S.
Operto, Stephane
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Univ Cote Azur, LJAD, CNRS, Nice, FranceUniv Paris Cite, Sorbonne Univ, CNRS, Lab Jacques Louis Lions LJLL,Inria, F-75005 Paris, France
Operto, Stephane
Riffo, Sebastian
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Univ Cote Azur, LJAD, CNRS, Nice, FranceUniv Paris Cite, Sorbonne Univ, CNRS, Lab Jacques Louis Lions LJLL,Inria, F-75005 Paris, France