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Keywords: neural network
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Proceedings Papers

Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, August 27–September 1, 2023
Paper Number: SEG-2023-3914280
...), medium vehicles (SUVs and cars), and small vehicles (motorcycles and electric bicycles). We compare the performance of Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN) for identifying vehicles of different sizes. The accuracy of all three neural...
Proceedings Papers

Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, August 27–September 1, 2023
Paper Number: SEG-2023-3915640
.... upstream oil & gas artificial intelligence geology reservoir characterization algorithm noise deep learning machine learning applied geoscience gaussian noise figure-i exploration geophysicist energy 10 geophysics intermediate geologist neural network interpolation american...
Proceedings Papers

Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, August 27–September 1, 2023
Paper Number: SEG-2023-3896358
... dataset artificial intelligence upstream oil & gas neural network geological subdiscipline international exploration geophysicist inversion full-waveform inversion applied geoscience geologist united states government machine learning fwi american association energy 10 elasticnet...
Proceedings Papers

Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, August 27–September 1, 2023
Paper Number: SEG-2023-3898594
... and Jafarpour, 2012; Zisselman et al., 2018; Mdrafi and Gurbuz, 2021; Douarre et al., 2021; Mou and Zhang, 2023). The solution jointly optimizes a dimensionality reduction operator and a 3D inversion encoder-decoder implemented by a deep convolutional neural network (DCNN). Dimensionality reduction is achieved...
Proceedings Papers

Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, August 27–September 1, 2023
Paper Number: SEG-2023-3904707
... and high-resolution results. geology inversion geologist deep learning reservoir characterization geological subdiscipline upstream oil & gas neural network machine learning exploration geophysicist international applied geoscience noise geophysics avo inversion synthetic dataset...
Proceedings Papers

Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, August 27–September 1, 2023
Paper Number: SEG-2023-3905013
..., ChatGPT is a powerful tool that can be used to quickly and effectively analyze text and extract meaningful information. geologist neural network china government asia government chatbot machine learning applied geoscience artificial intelligence geological subdiscipline keyword chatgpt...
Proceedings Papers

Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, August 27–September 1, 2023
Paper Number: SEG-2023-3905116
... intelligence american association energy 10 society architecture neural network reservoir characterization applied geoscience salvador island san salvador island realization Spatial statistical analysis and geomodelling of banana holes using point patterns and generative adversarial networks Rayan...
Proceedings Papers

Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, August 27–September 1, 2023
Paper Number: SEG-2023-3905650
... This study employs Convolutional Neural Networks (CNNs) to predict low-wavenumber seismic velocity models to serve as starting models for Full Waveform Inversion (FWI), utilizing the Gulf of Mexico’s Tiber field data, characterized by its expansive allochthonous salt body. The CNNs were trained...
Proceedings Papers

Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, August 27–September 1, 2023
Paper Number: SEG-2023-3906861
... estimate is necessary. This inquiry harnesses sophisticated AI models including Artificial neural network (ANN) to develop a robust empirical correlation for critical oil flow using the most efficacious parameters for managing coning. The study presents a 3D reservoir simulation model that has been tested...
Proceedings Papers

Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, August 27–September 1, 2023
Paper Number: SEG-2023-3907037
.... upstream oil & gas artificial intelligence geology reservoir characterization geologist deep learning machine learning invmixer seismic inversion accuracy trace mlp american association segmentation inversion international deep neural network architecture neural network unet applied...
Proceedings Papers

Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, August 27–September 1, 2023
Paper Number: SEG-2023-3907407
... an optimization-based workflow implemented as neural network training was developed to construct an RGT cube that can satisfy the trace-to-trace correlations which is captured by flow field and seismic amplitude. This workflow does not rely on interpreter label input. A public dataset has been tested...
Proceedings Papers

Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, August 27–September 1, 2023
Paper Number: SEG-2023-3907527
.... geologist neural network machine learning geology artificial intelligence interpolation deep learning reservoir characterization wavelet accuracy international applied geoscience american association upstream oil & gas inversion geological subdiscipline exploration geophysicist...
Proceedings Papers

Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, August 27–September 1, 2023
Paper Number: SEG-2023-3908217
... noise levels and insert prior information. Here, we investigate the properties of a Machine Learning-based UQ tool, the Invertible neural network (INN). This appears perfectly suited for UQ but in practice is limited by its memory requirements. We therefore propose a strategy to enable INN to perform...
Proceedings Papers

Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, August 27–September 1, 2023
Paper Number: SEG-2023-3908373
... image volume. Furthermore, the probabilistic layers of the network provides a way to achieve model uncertainty analysis of the output. geologist neural network translation deep learning machine learning cgan expanded abstract upstream oil & gas artificial intelligence international...
Proceedings Papers

Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, August 27–September 1, 2023
Paper Number: SEG-2023-3908592
... of the migrated image). However, instead of using non-stationary compact filters as commonly done in the literature to approximate the Hessian, we propose to use a deep neural network to directly learn the mapping between the FWI gradient (output) and its Hessian blurred counterpart (input). By doing so...
Proceedings Papers

Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, August 27–September 1, 2023
Paper Number: SEG-2023-3911297
... We propose a joint data- and physics-model-driven fullwaveform inversion (FWI) method based on semisupervised learning framework, which uses well-logging data, pseudo labels produced from conventional FWI and common mid-point (CMP) gathers to train neural network. Neural network builds mapping...
Proceedings Papers

Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, August 27–September 1, 2023
Paper Number: SEG-2023-3911335
... alignment, facilitating more precise interpretation and analysis in seismic processing workflows. upstream oil & gas europe government geologist deep learning geology norway government exploration geophysicist neural network reservoir characterization american association energy 10...
Proceedings Papers

Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, August 27–September 1, 2023
Paper Number: SEG-2023-3911339
... obtained with a Convolutional Neural Network (CNN) on a simple synthetic test and a more complex synthetic scenario. In both cases, we achieved an accurate modelling of the source which is supported also by a low data misfit. upstream oil & gas gravity machine learning geologist neural...
Proceedings Papers

Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, August 27–September 1, 2023
Paper Number: SEG-2023-3911535
... computed in the Fourier domain. We show the advantages of using global FNOs over conventional convolutional neural networks (CNN), to achieve a better non-linear mapping between the recorded data and the subsurface velocity. We show that FNOs can be used to automate velocity model building from field data...
Proceedings Papers

Paper presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, August 27–September 1, 2023
Paper Number: SEG-2023-3911647
... This paper discusses the automatic interpretation of seismic data by using a CNN (Convolutional Neural Network) to predict RGT (Relative Geologic Time). RGT is used as a high-resolution geometric framework and is defined on a regular 3D grid, with the same dimensions as the seismic data...

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