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The AAPG/Datapages Combined Publications Database
Showing 624 Results. Searched 200,756 documents.
Massive focal mechanism solutions from deep learning in west Texas
Yangkang Chen, Omar M. Saad, Alexandros Savvaidis, Fangxue Zhang, Yunfeng Chen, Dino Huang, Huijian Li, Farzaneh Aziz Zanjani
International Meeting for Applied Geoscience and Energy (IMAGE)
... to quantitatively pick the first-motion polarity using a pre-trained model from a rela- tively high-quality dataset. The fundamental principle of deep...
2024
Relative geologic time generation based on 3D-CNNs and domain adaptation
Xin He, Bangli Zou, Yifeng Fei, Gang Yu, Dajun Li
International Meeting for Applied Geoscience and Energy (IMAGE)
..., which can reduce the difference between the features of field and synthetic seismic data. Then, the trained model is fine-tuned with seismic horizons...
2024
Leveraging source-over-cable marine seismic field data for near offset reconstruction with deep learning
Owen Rohwer Huff, Jan Erik Lie, Andreas Kjelsrud Evensen, Aina Juell Bugge
International Meeting for Applied Geoscience and Energy (IMAGE)
... data collected with source-over-cable acquisition geometry as training data. First, a convolutional neural network (CNN) is trained to reconstruct...
2024
High-efficient reflection retrieval from massive ambient noise using a deep-learning workflow
Yinghe Wu, Shulin Pan, Dawei Liu, Yaojie Chen, Qinghui Cui
International Meeting for Applied Geoscience and Energy (IMAGE)
... workflow for quickly retrieving body wave events from massive ambient noise datasets. We feed relevant data to a convolutional autoencoder classifier...
2024
Rock Thin-section Analysis and Mineral Detection Utilizing Deep Learning Approach
Fatick Nath, Sarker Asish, Shaon Sutradhar, Zhiyang Li, Nazmul Shahadat, Happy R. Debi, S M Shamsul Hoque
Unconventional Resources Technology Conference (URTEC)
... of rock thin sections. In a similar objective, Nanjo et al. (2019) implemented convolutional neural network-based model to classify four types of rock...
2023
Deep Learning Models for Methane Emissions Identification and Quantification
Ismot Jahan, Mohamed Mehana, Bulbul Ahmmed, Javier E. Santos, Dan O’Malley, Hari Viswanathan
Unconventional Resources Technology Conference (URTEC)
... to prepare the data for the machine learning model. In this section, we will outline the preprocessing and Convolutional Neural Network (CNN) model...
2023
A Deep Learning-Based Surrogate Model for Rapid Assessment of Geomechanical Risks in Geologic CO2 Storage
Fangning Zheng, Birendra Jha, Behnam Jafarpour
Carbon Capture, Utilization and Storage (CCUS)
... storage. Using simulated data, we train a U-Net convolutional neural network to learn a mapping between well locations s and spatially distributed model...
2024
Seismic imaging uncertainty using deep learning predicted Greens functions
Han Liu, Anar Yusifov, Muhong Zhou, Linda Hodgson
International Meeting for Applied Geoscience and Energy (IMAGE)
... velocity model. We used U-net architecture (Ronneberger et al., 2015) with 5 layers, and each layer has 2 convolutional layers. Ensemble models were...
2022
Interactive channel interpretation using deep learning
Hao Zhang, Peimin Zhu, Zhiying Liao, Zewei Li, Dianyong Ruan
International Meeting for Applied Geoscience and Energy (IMAGE)
..., it is difficult to extract channels completely. With the development of machine learning technology, convolutional neural network (CNN) is widely...
2022
Interactive 3D fault prediction using a weighted 2D-CNN and multidirectional 3D-CNN
Jesse Lomask, Samuel Chambers
International Meeting for Applied Geoscience and Energy (IMAGE)
... using a weighted 2D-CNN and multi-directional 3D-CNN Jesse Lomask* and Samuel Chambers, S&P Global Summary We present an interactive 2D Convolutional...
2022
Machine learning and seismic attributes for prospect identification and risking: An example from offshore Australia
Mohammed Farfour, Douglas Foster
International Meeting for Applied Geoscience and Energy (IMAGE)
... and convert them to Gas chimney probability cube, and to Gamma Ray cube. Next, pre-trained Convolutional Neural Network (CNN) is trained using...
2022
Deep learning-based Vz-noise attenuation for OBS data
Jing Sun, Arash Jafargandomi, Julian Holden
International Meeting for Applied Geoscience and Energy (IMAGE)
... for the shear-noise model. We show that the proposed approach can effectively capture the substantial variability of shear noise and remove it from...
2023
Generalization Capability of Data-driven Deep Learning Models for Seismic Full-waveform Inversion: An Example Using the OpenFWI Dataset
Ayrat Abdullin, Umair Bin Waheed
International Meeting for Applied Geoscience and Energy (IMAGE)
... model, and ill-posedness of the inverse problem. There is a lack of Data-driven approaches have witnessed development for FWI, including multilayer...
2023
Deep carbonate reservoir characterization with unsupervised machine-learning approaches
Xuanying Zhu, Luanxiao Zhao, Xiangyuan Zhao, Yuchun You, Minghui Xu, Tengfei Wang
International Meeting for Applied Geoscience and Energy (IMAGE)
... (PCA), T-distributed Stochastic Neighbor Embedding (t-SNE), Uniform Manifold Approximation and Projection (UMAP), and Convolutional Autoencoder (CAE...
2023
Bayesian variational auto-encoder for seismic wavelet extraction
Ammar Ghanim, Ricard Durall, Norman Ettrich
International Meeting for Applied Geoscience and Energy (IMAGE)
... approaches is the convolutional model. In the well-tie process, the wavelet is convolved with the reflectivity series along the well path to produce...
2023
Automated machine learning first-break picking in the Sichuan Basin A case study
Jianfa Wu, Xuewen Shi, Qiyong Gou, Ersi Xu, Dongjun Zhang, Dingxue Wang, Phil Bilsby, Qing Zhou, Rong Li
International Meeting for Applied Geoscience and Energy (IMAGE)
... to the various machine learning model architectures employed and demonstrate the uplift in both the resulting reservoir imaging and the reduction...
2024
Convolution neural networks fault interpretation in the Brazilian presalt
Hugo Garcia, Edimar Perico, Ana Moliterno, Alexandre Kolisnyk, Michael Lowsby
International Meeting for Applied Geoscience and Energy (IMAGE)
..., particularly deep learning convolutional neural networks have been used successfully in fault interpretation in seismic data around the world with different...
2024
Automating the thresholding of multi-stage iterative source separation with priors using machine learning
Nam Pham, Rajiv Kumar, Sunil Manikani, Yousif Izzeldin Kamil Amin, Phillip Bilsby, Massimiliano Vassallo, Tao Zhao
International Meeting for Applied Geoscience and Energy (IMAGE)
... algorithm. A machine learning model is trained with a fraction of data and is then applied to the entire survey. Experimental results on a field dataset...
2024
Abstract: Push the Limits of Seismic Resolution Using Surface Consistent Gabor Deconvolution; #90171 (2013)
Xinxiang Li and Darren P. Schmidt
Search and Discovery.com
... and the time-variant earth wavelet in a nonstationary convolutional trace model, which can be approximately factorized in the Gabor domain...
2013
Abstract: Short-time Wavelet Estimation in the Homomorphic Domain; #90174 (2014)
Roberto H. Herrera and Mirko van der Baan
Search and Discovery.com
... phases in both the wavelet and the reflectivity. Theory The seismic signal is described by the convolutional model (Ulrych, 1971): s(t) = w(t) ⋆ r...
2014
Simulating seismic data using generative adversarial networks
Bradley C. Wallet, Eyad Aljishi, Hussain Alfayez
International Meeting for Applied Geoscience and Energy (IMAGE)
... International Conference on Machine Learning, 70, 214–223. Chellapilla, K., S. Puri, and P. Simard, 2006, High performance convolutional neural...
2022
S/N RATIO AND BANDWIDTH CONSIDERATIONS WHEN UTILIZING SEISMIC DATA IN EXPLORING FOR SUBTLE TRAPS - EXAMPLES FROM THE KNOX PLAY
Edward R. Tegland, Exploration Development, Inc., S. Pikes Peak Dr., Parker, CO Patrick H. Bygott, Exploration Development, Inc., S. Pikes Peak Dr., Parker, CO
Ohio Geological Society
.... Model data created from an Ohio Knox synthetic Seismogram will be used to illustrate what this means to the person...
1999
3D velocity model building based upon hybrid neural network
Herurisa Rusmanugroho, Junxiao Li, M. Daniel Davis Muhammed, Jian Sun
International Meeting for Applied Geoscience and Energy (IMAGE)
... as an image are passed through some convolutional layers to estimate P-velocity model. This network is expected to learn from the features obtained from...
2022
Validating machine learning-based seismic property prediction through self-supervised seismic reconstruction
Tao Zhao, Haibin Di, Aria Abubakar
International Meeting for Applied Geoscience and Energy (IMAGE)
... a convolutional autoencoder as the SSL model to reconstruct the original seismic data. The training samples are 2D seismic patches randomly extracted from...
2022
Implementation of Seismic Data Quality Characterisation Using Supervised Deep Learning
Joshua Thorp, Krista Davies, Julien Bluteau, Peter Hoiles
Australian Petroleum Production & Exploration Association (APPEA) Journal
... is to use a deep learning convolutional classification network that converts an image of the seismic stack into a prediction confidence value (%) for each...
2020