Mitigation of spatial nonstationarity with vision transformers
Spatial nonstationarity, the location variance of features' statistical distributions, is ubiquitous
in many natural settings. For example, in geological reservoirs rock matrix porosity varies …
in many natural settings. For example, in geological reservoirs rock matrix porosity varies …
Efficient subsurface modeling with sequential patch generative adversarial neural networks
Subsurface modeling is important for subsurface resource development, energy storage,
and CO2 sequestration. Many geostatistical and machine learning methods are developed …
and CO2 sequestration. Many geostatistical and machine learning methods are developed …
Data Conditioning for Subsurface Models with Single-Image Generative Adversarial Network (SinGAN)
The characterization of subsurface models relies on the accuracy of subsurface models
which request integrating a large number of information across different sources through …
which request integrating a large number of information across different sources through …
Introduction to Special Issue: Geoscience Data Analytics and Machine Learning
MJ Pyrcz - AAPG Bulletin, 2022 - archives.datapages.com
A digital revolution is underway in all sectors of our economy (Gurumurthy and Schatsky,
2019), posing unique challenges and opportunities for science and engineering research …
2019), posing unique challenges and opportunities for science and engineering research …
Exemplar-Guided Sedimentary Facies Modeling for Bridging Pattern Controllability Gap
C Wu, F Hu, D Sun, L Zhang, L Wang, H Zhang - Petrophysics, 2023 - onepetro.org
Inferring subsurface structure from sparse log data is crucial for geology. Recently, deep-
learning-based methods, which provide sufficient prior knowledge from training sets, have …
learning-based methods, which provide sufficient prior knowledge from training sets, have …
Deep learning for spatial nonstationarity: evaluation, mitigation, and generation
L Liu - 2024 - repositories.lib.utexas.edu
Spatial nonstationarity, the location variance of features' statistical distributions, is ubiquitous
in many natural settings. While the advent of deep learning technologies has enabled new …
in many natural settings. While the advent of deep learning technologies has enabled new …
Subsurface Image Morphing Operator Using Deep Learning Techniques
Velocity uncertainty is one of the major challenges for subsurface imaging in oil & gas
exploration. A surrogate migration engine based on image morphing operation can …
exploration. A surrogate migration engine based on image morphing operation can …
Reservoir Facies Modeling Based on Generative Adversarial Network
S Lin, S Yin, Y Zhang, J Liu… - … Conference on New …, 2024 - ieeexplore.ieee.org
Three-dimensional geological modeling of reservoirs is of great significance for developing
oil and gas resources, groundwater resources, and carbon dioxide geological storage …
oil and gas resources, groundwater resources, and carbon dioxide geological storage …
이산화탄소지중저장을위한기계학습기반4-D 탄성파자료통합및배사구조채널대수층특성화
김현민， 김남화， 신현돈， 조홍근 - 한국자원공학회지, 2024 - dbpia.co.kr
본 연구에서는 채널대수층의 이산화탄소 지중저장에서 4-D 탄성파자료를 통합해 불확실성을
정량화하고 신뢰도를 향상하기 위해 기계학습의 하나인 Pix2Pix 기반의 4-D 탄성파자료 …
정량화하고 신뢰도를 향상하기 위해 기계학습의 하나인 Pix2Pix 기반의 4-D 탄성파자료 …