... of a seismic attribute that may be significant in seismic fault interpretation or a seismic fault attribute is treated as an image segmentation problem and using different deep learning (DL) architectures. For doing this, the researchers mainly concentrate on applying cutting-edge DL architectures in computing...

... Recently, first arrivals picking is treated as an image segmentation problem and deep learning (DL) algorithms have been successfully used to pick the first arrivals of seismic shot gathers. Researchers have demonstrated that cutting-edge DL architectures have the potential to improve...

... bhattacharya architecture interpretation applied geoscience international Variability in river-dominated deltaic systems: facies models and drivers based on the Late Cretaceous Panther Tongue Sandstone, Central Utah Clayton Jacks* and Diane Kamola, The University of Kansas Summary Conceptual facies...

... Are facies and stratigraphic architecture reliably predicted using machine learning (ML) techniques on sedimentary outcrop statistics? When are the predictions robust and under what conditions will they fail? At what threshold of training data do these predictions become accurate...

... national laboratory international artifact architecture High- delity GPR image super-resolution via deep-supervised machine learning Kai Gao , Carly M. Donahue, Bradley G. Henderson, Ryan T. Modrak, Los Alamos National Laboratory SUMMARY Ground-penetrating radar (GPR) is the tool of choice for fast...

... & gas artificial intelligence international american association interpretation energy 10 society architecture seismic inversion uncertainty quantification deep learning seismic model acoustic impedance applied geoscience poststack inversion inference algorithm information impedance...

... facies. A U-Net architecture is used to train a baseline model with an offshore New Zealand dataset to transfer learned features to a second model using a Netherlands North Sea dataset. The main hyperparameter tuning goal is focused on maximizing the number of frozen layers while minimizing the number...

... with transgressive mudstone-rich shale intervals. This study determines the architecture of the conglomeratic units using Drone Photogrammetry. These units in outcrop provide a unique window into the evolution of local basin margin depositional environments and their sediment dispersal patterns during the Early...

... artificial intelligence frequency extrapolation fine-tuning reservoir characterization exploration geophysicist neural network prediction architecture figure 1 picking applied geoscience dataset storseismic international application american association randy harsuko energy 10 Seismic...

... Reservoir models of deep-water channels rely upon low-resolution but spatially extensive seismic data, high vertical resolution but spatially sparse well log data and geomodeling methods. The results cannot predict architecture (Figure 1B) below seismic resolution or between well logs (Ringrose...

... of a petrographic dataset are presented. The StyleGAN2 architecture was selected to train a set of 10070 petrographic images which were divided into four categories: plutonic, volcanic, sedimentary, and metamorphic rocks. This model achieved a state-of-the-art FID (Fréchet Inception Distance) score of 12.49...

... We often apply a number of processing modules to a field dataset to perform various tasks, like denoising, first arrival picking, velocity estimation, and so on. Selecting the architecture, collecting labels, and tuning a deep-learning network for a specific seismic processing task have been...

...; Olariu et al. , 2020). The excellent exposure provides an ideal outcrop study location to map slope channel facies and architecture in detail, and put their characteristics into the context of basin margin evolution. reservoir characterization architecture clinoform exploration geophysicist...

... and qualitative analysis of the model predictions, while the former will develop the models. This separation of roles is most apparent in each team's tools, and effectively tracking the data and models lineage across applications can be daunting. We propose a high-level architecture for integrating mainstream...

... with a variety of processes that post-date initial deposition, which include mass transport deposits, overbank collapse, de-watering, and loading. Recognition of MTDs (i.e., debris flows, slumps, slides) has implications for understanding reservoir architecture, reservoir characterization, top selection, mapping...

... Seismic data is contaminated by random noise, which decreases the accuracy of subsequent seismic data processing. Therefore, random noise attenuation is an important step to improve the signal-to-noise ratio (SNR) of seismic data. We design a Deep Image Prior (DIP) network architecture based...

... learning inversion reservoir characterization coefficient artificial intelligence dipole dictionary dictionary coefficient exploration geophysicist biswa reflectivity dictionary learning architecture american association seismic inversion international equation decoder baylor university...

... of simultaneous source wavefields. neural network machine learning inversion deep learning reservoir characterization wavefield expanded abstract upstream oil & gas artificial intelligence international acquisition noise separation dncnn interference noise architecture hua-wei zhou...

... learning reservoir characterization machine learning architecture international american association poisson shear prediction society figure 2 energy 10 correlation coefficient hyperparameter A robust approach for shear log predictions using deep learning on big data sets from a carbonate...

... computation, and parallel architecture optimization, to accelerate the KDMIG method. Numerical tests demonstrate that the efficiency has been improved by up to 40x compared to the CPU version without any compromise of the imaging accuracy. reservoir characterization architecture international energy...