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The AAPG/Datapages Combined Publications Database
Showing 621 Results. Searched 200,293 documents.
Development of deep learning method for automatic seismic first break picking
Albert Farkhutdinov, Ruslan Malikov, Izat Shahsenov
International Meeting for Applied Geoscience and Energy (IMAGE)
... learning based techniques, such as support vector machines, convolutional image segmentation, and U-Net networks, have been studied for automatic FBP (Qu et...
2024
Using machine learning to interpret 3D airborne electromagnetic inversions
Eldad Haber, Jen Fohring, Mike McMillan, Justin Granek
Petroleum Exploration Society of Australia (PESA)
... types of regularization and constraints to the model, but another approach is to learn what underlying structures or boundaries these smooth...
2019
Efficient seismic image super-resolution
Adnan Hamida, Motaz Alfarraj, Abdullatif A. Al-Shuhail, Salam A. Zummo
International Meeting for Applied Geoscience and Energy (IMAGE)
... a GAN-based model with four convolutional layers for both the generator and discriminator. Fehler and Keliher (2011) SEAM Phase I synthetic dataset...
2022
Deep Convolutional Neural Networks for Seismic Salt-Body Delineation; #70360 (2018)
Haibin Di, Zhen Wang, Ghassan AlRegib
Search and Discovery.com
...Deep Convolutional Neural Networks for Seismic Salt-Body Delineation; #70360 (2018) Haibin Di, Zhen Wang, Ghassan AlRegib Deep Convolutional Neural...
2018
3D CNN for channel identification in seismic volume
Haishan Li, Wuyang Yang, Xiangyang Zhang, Xinjian Wei, Xin Xu
International Meeting for Applied Geoscience and Energy (IMAGE)
... volumes with complex structure using an end-to-end 3D convolutional neural network. To train the network, we automatically generate a training dataset...
2022
Implementation of frequency-dependent fault identification by convolutional neural networks with uncertainty analysis
Fan Jiang, Alejandro Jaramillo, Steve Angelovich, Phill Norlund, Julianna Toms
International Meeting for Applied Geoscience and Energy (IMAGE)
...Implementation of frequency-dependent fault identification by convolutional neural networks with uncertainty analysis Fan Jiang, Alejandro Jaramillo...
2022
High-resolution prestack seismic inversion of reservoir parameters using an arch network
Ting Chen, Yaojun Wang, Yuan Yuan, Gang Yu, Guangmin Hu
International Meeting for Applied Geoscience and Energy (IMAGE)
...) construct a convolutional neural network (CNN) that is trained to perform mapping of relevant seismic data cubes to respective velocity logs. Meanwhile...
2022
Predicting horizons for salt body models using machine learning from neighboring seismic surveys: A case study from the northern Gulf of Mexico
Andrew Reisdorf, Dan Ferdinand Fernandez, Hugo Enrique Munoz Cuenca, Ryan King, David Manzano, Gavin Menzel-Jones
International Meeting for Applied Geoscience and Energy (IMAGE)
... and manual effort are required to provide horizons that are input into the earth model building process. The quality of these horizons determines...
2022
Introducing stochasticity into CNN-based property estimation from angle-stack seismic
Haibin Di, Tao Zhao, Aria Abubakar
International Meeting for Applied Geoscience and Energy (IMAGE)
... and perturbing with Gaussian noises ℕ(0,1) per prior rock property model. convolutional layer for reconstructing the fullstack seismic, and (iii) one...
2024
Orogenic gold prospectivity mapping using machine learning
Mike McMillan, Jen Fohring, Eldad Haber, Justin Granek
Petroleum Exploration Society of Australia (PESA)
... developed a new algorithm for mineral prospectivity mapping using a VNet deep convolutional neural network and applied it to finding gold at the Committee...
2019
Machine-learning based arrival-picking in continuous DAS recordings Application to the Utah FORGE EGS project
Nepomuk Boitz, William Tegtow, Serge Shapiro
International Meeting for Applied Geoscience and Energy (IMAGE)
... a Convolutional Neural Network (CNN). In contrast to geophone-based picking methods, we suggest to train CNNs only on data from a specific dataset to best...
2024
GAN-based priors for Bayesian inference of subsurface geology at large scale
Kevin B. Daly, Tuan A. Tran, Brent D. Wheelock, Grant J. Seastream
International Meeting for Applied Geoscience and Energy (IMAGE)
... in the process stratigraphy simulations. One solution is to select a generative ML model with a fully convolutional architecture, where the latent coordinate...
2024
Unconventional Reservoir Microstructural Analysis Using SEM and Machine Learning
Amanda S. Knaup, Jeremy D. Jernigen, Mark E. Curtis, John W. Sholeen, John J. Borer IV, Carl H. Sondergeld, Chandra S. Rai
Unconventional Resources Technology Conference (URTEC)
... specifically Convolutional Neural Networks (CNN), are being used for pixel labeling and feature identification using the CNN U-Net. This network...
2019
Convolutional Neural Networks Forecasting for Unconventional Drilling Units in US Land
Francisco J. Parga Garcia, Jie Fang, Niven Shumaker
Unconventional Resources Technology Conference (URTEC)
...Convolutional Neural Networks Forecasting for Unconventional Drilling Units in US Land Francisco J. Parga Garcia, Jie Fang, Niven Shumaker URTeC...
2024
Estimating CO2 saturation maps from seismic data using deep convolutional neural networks
Zi Xian Leong, Tieyuan Zhu, Alexander Y. Sun
International Meeting for Applied Geoscience and Energy (IMAGE)
... deep convolutional neural networks interpolated velocity and density conform with the seismic structure. We select a 2D slice (Fig. 1) from the 3D model...
2022
What samples must seismic interpreters label for efficient machine learning?
Ryan Benkert, Mohit Prabhushankar, Ghassan AlRegib
International Meeting for Applied Geoscience and Energy (IMAGE)
... resources AlRegib et al. (2018). At the core of successful machine learning algorithms, stands the mathematical model representation of data points...
2023
A hybrid machine learning model for improving regression of mineral composition estimation using well logging data
Xiaojun Liu, Kezhen Hu, Stephen E. Grasby, Benjamin Lee
International Meeting for Applied Geoscience and Energy (IMAGE)
... classification problems. This ConvXGB architecture consists of a network with several stacked convolutional layers and XGBoost as the last layer of the model...
2024
Intelligent Prediction of Shale Oil Fracturing Curves Based on A Sequence-to-Sequence Model
Leyi Zheng, Tianbo Liang, Yunjin Wang, Fujian Zhou, Junlin Wu, Bin Wang, Jiaming Zhang, Maoqin Yang, Gong Chen, Xingyuan Liang
Unconventional Resources Technology Conference (URTEC)
... of critical events during fracturing. A novel sequence-tosequence prediction model (TCN-LSTM) is proposed that integrates a temporal convolutional...
2025
HIGH-PRECISION ALGORITHM FOR GRAIN SEGMENTATION OF THIN SECTIONS BY MULTI-ANGLE OPTICAL-MICROSCOPIC IMAGES
Timur Murtazin, Zufar Kayumov, Vladimir Morozov, Radik Akhmetov, Anton Kolchugin, Dmitrii Tumakov, Danis Nurgaliev, Vladislav Sudakov
Journal of Sedimentary Research (SEPM)
.... (2020) for semantic segmentation of the porosity of petrographic thin sections. The U-Net model is a fully connected convolutional neural network...
2023
Seismic data reconstruction using denoising convolutional neural network combined with regularization by denoising
Nanying Lan, Kaiheng Sang, Fanchang Zhang
International Meeting for Applied Geoscience and Energy (IMAGE)
...Seismic data reconstruction using denoising convolutional neural network combined with regularization by denoising Nanying Lan, Kaiheng Sang...
2022
Aiding self-supervised coherent noise suppression by the introduction of signal segmentation using blind-spot networks
Sixiu Liu, Claire Birnie, Tariq Alkhalifah, Andrey Bakulin
International Meeting for Applied Geoscience and Energy (IMAGE)
... al., 2019; Wang and Chen, 2019; Birnie et al., 2021a). A number of NN-based denoising procedures utilise Convolutional Neural Networks (CNNs) to learn...
2022
Generative modeling for inverse problems
Rami Nammour
International Meeting for Applied Geoscience and Energy (IMAGE)
... by model errors (triangle, Q1) and data errors (box, Q2) for fully connected, convolutional and variational autoencoder models. Note that the success rate...
2022
Insights using machine learning in predicting faults and horizons: A case study onshore Texas
Dan Ferdinand Fernandez, Mustafa Karer, Richard Hearn, Ryan King, Sunil Manikani, Gavin Menzel-Jones
International Meeting for Applied Geoscience and Energy (IMAGE)
... Texas dataset. By employing ML technology through convolutional neural networks (CNNs) trained on real data we predict multiple layers of faults from...
2022
Multi-realization seismic data processing with deep variational preconditioners
Matteo Ravasi
International Meeting for Applied Geoscience and Energy (IMAGE)
... problems of the form, d = Gx + ε, where the model x and the observed data d are connected via a linear operator G. Moreover, ε represents a combination...
2023
Bridging the gap: Deep learning on seismic field data with synthetic training for building Gulf of Mexico velocity models
Stuart Farris, Robert Clapp
International Meeting for Applied Geoscience and Energy (IMAGE)
... Clapp, Stanford University SUMMARY This study employs Convolutional Neural Networks (CNNs) to predict low-wavenumber seismic velocity models to serve...
2023