...-type reflection coefficient inversion, aiming to enhance imaging fidelity and resolution. Numerical examples on both synthetic data and field data verify the proposed method significantly improves the image fidelity. reflection coefficient migration operator geology machine learning imaging...

... the model’s efficacy in accurately predicting CPT curve trends, albeit with some amplitude discrepancies. geology geological subdiscipline cpt curve artificial intelligence geological unit applied geoscience & energy 10 soil strength machine learning uhr data exploration geophysicist...

... with the observed full-stack seismic. geology property estimation geologist neural network reservoir characterization abubakar cnn-based property estimation stochasticity machine prediction artificial intelligence machine learning property model workflow lssm geological subdiscipline cycle...

... models while conditioning to well facies data. geology reservoir characterization geologist artificial intelligence sedimentary rock rock type deep learning machine learning geomodel generative adversarial network generative facies facies model applied geoscience & energy 10 ddpm...

... geologist mechanism focal mechanism solution neural network p-wave first-motion polarity machine learning polarity applied geoscience & energy 10 deep learning reservoir characterization mechanism solution chen plate tectonics artificial intelligence focal mechanism architecture west...

... superiority compared to the reported damped rank-reduction (DRR) and projection onto convex sets (POCS) methods. reconstruction deep learning reservoir characterization poc frequency component machine learning geophysics drr geologist neural network seismic data geology matrix applied...

... identification model was established by F-score and supervised-mode machine learning algorithms. The pore structure types were upscaling from core-scale to logging-scale. Finally, the spatial distributions of pore structure types in the whole region were characterized by combining with three-dimensional (3D...

.... Combining multi-frequency or multi-azimuth faults is another application for this technique. geologist artificial intelligence petroleum geology neural network geological subdiscipline interpretation machine learning fault prediction prediction applied geoscience & energy 10 reservoir...

... subdiscipline sand body reservoir spatial characterization structural geology seismic model rock type reservoir characterization decomposition target layer geologist variation petroleum geology machine learning thickness body spectrum decomposition information applied geoscience & energy 10...

... generated by the proposed network enhances stratigraphic continuity by interpolating missing values from the original data and restoring lost structures. geologist geology machine learning reservoir characterization loss function geological subdiscipline interpolation dataset architecture...

... on the manual optimization of processing parameters, which can take up to a week. To reduce the turn-around time for gather denoising, machine learning approaches are proposed to reduce the processing time from weeks to hours through capturing a best-practice workflow (Rynja, 2022). The resulting ML approach...

..., and achieved strong generalization ability through co-evolution. deep learning reservoir characterization applied geoscience & energy 10 neural network machine learning geology artificial intelligence noise label geologist ground-roll wave semi-supervised ground-roll wave segmentation semi...

.... Aperformance comparison with benchmark methods using a challenging field data example shows that the proposed method outperforms the traditional methods. geologist artificial intelligence reconstruction attention network deep learning machine learning chen exploration geophysicist local area...

... techniques. When applied to actual seismic data analysis, this method effectively indicates the distribution relationship between faults and karst caves, reduces manual interpretation costs, and provides guiding suggestions for oil and gas exploration. sedimentary rock deep learning machine learning...

... geologist rock type sedimentary rock neural network deep learning artificial intelligence classification mechanism geological subdiscipline pixel attention computer vision seismic facies classification machine learning se-resnet block proceedings channel attention image super-resolution...

... intelligence geologist noise geology machine learning geophysics exploration geophysicist surface wave vehicle-generated noise coherent information correlation symmetric autoencoder backscatter analysis applied geoscience & energy 10 american association subsurface information reservoir...

... neural network well logging geological subdiscipline baseline correction automatic well-log baseline correction log analysis economic geology machine learning correction abc-net interpretation raw sp log potential co 2 exploration geophysicist applied geoscience & energy 10 sp log...

... to leverage Machine Learning (ML) techniques to improve sampling in FWI model space. We propose improvements to the Implicit FWI (IFWI) method introduced by Zhang (2022), Zhang (2023) and Sun (2023) and show a successful application to vertical seismic profile (VSP) field data. As far as we are aware...

... learning geologist inversion artificial intelligence machine learning reservoir characterization applied geoscience & energy 10 american association deep learning approach log analysis calculation dispersion inversion wang workflow sensitivity dispersion curve efficiency parameter...

... machine learning arrival wave nonlinear time difference correction geometric structure ft-nltd correction exploration geophysicist applied geoscience & energy 10 feature template constraint field data linear feature template picking structure constraint Feature template based nonlinear...