Welcome to the new Datapages Archives
Datapages has redesigned the Archives with new features. You can search from the home page or browse content from over 40 publishers and societies. Non-subscribers may now view abstracts on all items before purchasing full text. Please continue to send us your feedback at emailaddress.
AAPG Members: Your membership includes full access to the online archive of the AAPG Bulletin. Please login at Members Only. Access to full text from other collections requires a subscription or pay-per-view document purchase.
Welcome to the new Datapages Archives
Search Results > New Search > Revise Search
The AAPG/Datapages Combined Publications Database
Showing 621 Results. Searched 200,293 documents.
Seismic Facies Segmentation Using Deep Learning; #42286 (2018)
Daniel Chevitarese, Daniela Szwarcman, Reinaldo Mozart D. Silva, Emilio Vital Brazil
Search and Discovery.com
... selected a trained convolutional neural network (CNN) with the highest accuracy on the classification task. Then, we modified the final part...
2018
Using mixture density networks for uncertainty and prediction in seismic reservoir characterization
Cornelius Rosenbaum, Ryan Warnick, Anar Yusifov, Reetam Biswas, Atish Roy
International Meeting for Applied Geoscience and Energy (IMAGE)
...–111. Khan, S., H. Rahmani, S. A. A. Shah, and M. Bennamoun, 2018, A guide to convolutional neural networks for computer vision: Synthesis Lectures...
2022
Application of Machine Learning to Facies Classification of Carbonate Core Images
Sharinia Kanagandran
Southeast Asia Petroleum Exploration Society (SEAPEX)
... learning techniques. The study evaluated two commonly used machine learning algorithms, Random Forest (RF) and Convolutional Neural Networks (CNNs...
2019
ABSTRACT: Shaping the Wavelet, by Wang, Yuchun E.; Huo, Shoudong; #90141 (2012)
Search and Discovery.com
2012
Transfer Learning Applied to Seismic Images Classification
Search and Discovery.com
N/A
Seismic Facies Segmentation Using Deep Learning
Search and Discovery.com
N/A
Reservoir Modeling With Deep Learning
Search and Discovery.com
N/A
Application of Machine Learning and Deep Learning for Complex Fault Network Characterizationon the North Slope, Alaska
Search and Discovery.com
N/A
ABSTRACT: Quantitative Integration of 4D Seismic for Field Development; #90007 (2002)
Garnham, Gail Riekie, Malu Jensen, Liz Pointing
Search and Discovery.com
... Figure 1. Schematic display of Nelson channels Figure 2. Forward convolutional model of moved OWC Figure 3. 4D forward convolutional model (Moved OWC...
Unknown
Abstract: FaciesNet: Machine Learning Applications for Facies Classification in Well Logs;
Chayawan Jaikla, Pandu Devarakota, Neal Auchter, Mohamed Sidahmed, Irene Espejo
Search and Discovery.com
... information, facies stacking pattern, and geologic correlations, FaciesNet. Our proposed model incorporates decoding and encoding deep convolutional...
Unknown
Abstract: CO2 Distribution Prediction Using Machine Learning Based Proxy Model in Geological Carbon Sequestration;
Zhi Zhong, Alexander Sun
Search and Discovery.com
...Abstract: CO2 Distribution Prediction Using Machine Learning Based Proxy Model in Geological Carbon Sequestration; Zhi Zhong, Alexander Sun CO2...
Unknown
Abstract: Open-source Python Stack and Tools for Geoscientific Image Analysis and Interpretation -From research to deployment; #91204 (2023)
Mustafa Al Ibrahim
Search and Discovery.com
... learning as backend to automate the estimation partially or completely. Semantic segmentation modules use convolutional neural networks to extract rock...
2023
Abstract: Different Flavors of the Marchenko-Equation-Based Internal Multiple Elimination Methods: the Trade-off between Fidelity, Computational Cost and Ease of Use; #91204 (2023)
Marcin Dukalski, Chris Reinicke
Search and Discovery.com
... that the target primaries are dressed with a convolutional filter representing the total overburden transmissions. Therefore, convolution...
2023
Abstract: Utilizing Seismic Attributes for Machine Assisted Fault Detection and Extraction; #91204 (2023)
Muhammad Khan, Yasir Bashir, Saleh Dossary, Syed Ali
Search and Discovery.com
... labelled data as transfer learning to update the foundation Convolutional Neural Network (CNN) model that was initially trained on synthetic data...
2023
Abstract: Seismic Fault Detection by Denoising Diffusion Probabilistic Model; #91204 (2023)
Bingbing Sun, Ali Abdulmohsen, Nasher AlBinHassan
Search and Discovery.com
...Abstract: Seismic Fault Detection by Denoising Diffusion Probabilistic Model; #91204 (2023) Bingbing Sun, Ali Abdulmohsen, Nasher AlBinHassan Seismic...
2023
-- no title --
user1
Search and Discovery.com
... by utilizing Convolutional Neural Networks(CNNs) and Wavelet-based approaches. This ensures a clear interpretation of subsurface characteristics for a better...
Unknown
Convolutional neural networks as an aid to biostratigraphy and micropaleontology: a test on late Paleozoic microfossils
Rafael Pires De Lima, Katie F. Welch, James E. Barrick, Kurt J. Marfurt, Roger Burkhalter, Murphy Cassel, Gerilyn S. Soreghan
PALAIOS
... and the input data to train the convolutional kernel weights. Cross Entropy Loss.—A measure of the difference between the model’s predictions are from...
2020
Accelerated deep learning-based estimation of wavefront dips and curvatures and their application to 3D prestack data enhancement
Kirill Gadylshin, Ilya Silvestrov, Andrey Bakulin
International Meeting for Applied Geoscience and Energy (IMAGE)
... Attributes Deep Neural Network. It is based on automatic local wavefront attributes estimation using a specially trained convolutional deep neural network...
2022
Marchenko focusing using convolutional neural networks
Mert S. R. Kiraz, Roel Snieder
International Meeting for Applied Geoscience and Energy (IMAGE)
...Marchenko focusing using convolutional neural networks Mert S. R. Kiraz, Roel Snieder Marchenko focusing using convolutional neural networks Mert...
2022
Introduction to Special Issue: Geoscience Data Analytics and Machine Learning
Michael J. Pyrcz
AAPG Bulletin
... complicated, multivariate, spatiotemporal subsurface systems, and in predictive mode, to make predictions for cases not used to train the model...
2022
A hybrid deep learning network for tight and shale reservoir characterization using pressure and rate transient data
Hamzeh Alimohammadi, Hamid Rahmanifard, and Shengnan Nancy Chen
AAPG Bulletin
... at a batch size of 20. Figure 5. Optimum number of batch size (A) and dropout rate (B) for hybrid convolutional neural networks–long short-term memory model...
2022
Abstract: Impedance Inversion of Blackfoot 3D Seismic Dataset; #90171 (2013)
A. Swisi and Igor B. Morozov
Search and Discovery.com
... by using the methods below. 2) Model-based inversion is also called blocky inversion. This method is based on the convolutional seismic model: S =W * R + n...
2013
Convolution Neural Networks If They can Identify an Oncoming Car, can They Identify Lithofacies in Core?; #42312 (2018)
Rafael Pires de Lima, Fnu Suriamin, Kurt Marfurt, Matthew Pranter, Gerilyn Soreghan
Search and Discovery.com
... drive our cars but also taste our beer. Specifically, recent advances in the architecture of deep-learning convolutional neural networks (CNN) have...
2018
3D GPR data mel-frequency cepstral coefficients features for effective CNN classification of urban utilities
Jide Nosakare Ogunbo, Sang Hun Baek, Sang-Wook Kim
International Meeting for Applied Geoscience and Energy (IMAGE)
... misclassifications because of the nonuniqueness inherent in the restrictive geometrical extent. Therefore, the Convolutional Neural Network classification of 3D GPR...
2024
Geophysics and neural networks: learning from computer vision
Mark Grujic, Liam Webb, Tom Carmichael
Petroleum Exploration Society of Australia (PESA)
... the ResNet-50 convolutional neural network to the GGMplus regional gravity model of Australia. This results in the quantitative characterisation of geophysical...
2019