SEG2020年会139篇机器学习地球物理应用论文分享
2020年SEG年会于10月11-16日召开，今年不同的是由于新冠肺炎疫情会议在线进行。近日SEG开放了会议论文下载服务，笔者浏览了会议全部论文清单，筛选出有关机器学习（深度学习）技术在地球物理中应用方面的论文139篇，下载了全部139篇论文，打包后提供给大家下载。
论文下载地址为：https://pan.baidu.com/s/1smXalhndAxksChkF9CHP-w
提取码：mldl
附件：
SEG-2020年会机器学习地球物理应用论文清单
2020年SEG年会共收到1200多篇论文投稿，录用760篇。据不完全统计，内容涉及机器学习（深度学习）技术应用的论文共139篇，其中口头报告69篇，张贴论文70篇。
口头报告：69篇
segam2020-3399306.1
Machine-learning based data recovery and its benefit to seismic acquisition: deblending, data reconstruction and low-frequency extrapolation in a simultaneous fashion
Shotaro Nakayama* and Gerrit Blacquière, Delft University of Technology
segam2020-3415521.1
Synthesizing seismic diffractions using a generative adversarial network
Ricard Durall*1;2 , Valentin Tschannen1 , Franz-Josef Pfreundt1, Janis Keuper1;3, 1Fraunhofer ITWM, Germany, 2IWR, University of Heidelberg, Germany, 3IMLA, Offenburg University, Germany
segam2020-3417484.1
Deep learning for salt body detection applied to 3D Gulf of Mexico data
Benjamin Consolvo*, Fairfield Geotechnologies, Ehsan Zabihi Naeini, Earth Science Analytics, Paul Docherty, Fairfield Geotechnologies
segam2020-3419221.1
Improving TOC estimation for Wolfcamp shales using statistical shale rock physics modeling
Jaewook Lee* and David Lumley, University of Texas at Dallas; Un Young Lim, Chevron Energy Technology Company (formerly, Texas A&M University)
segam2020-3419762.1
Separating primaries and multiples using hyperbolic radon transform with deep learning
Harpreet Kaur, Nam Pham, and Sergey Fomel, The University of Texas at Austin
segam2020-3420195.1
Adaptive first arrival picking model with meta-learning
Pengyu Yuan, University of Houston; Wenyi Hu, Advanced Geophysical Technology, Inc; Xuqing Wu, Jiefu Chen, Hien Van Nguyen, University of Houston
segam2020-3420587.1
ML-adjoint: learn the adjoint source directly for full waveform inversion using machine learning
Bingbing Sun and Tariq Alkhalifah, King Abdullah University of Science and Technology
segam2020-3420598.1
CNN-boosted Full waveform inversion
Yulang Wu* and George A. McMechan, The University of Texas at Dallas
segam2020-3421468.1
Machine learned Green’s functions that approximately satisfy the wave equation
Tariq Alkhalifah, Chao Song, KAUST, Umair bin Waheed, KFUPM
segam2020-3422495.1
Automated well-to-seismic tie using deep neural networks
Philippe Nivlet, Robert Smith, Nasher AlBinHassan, Geophysics Technology, EXPEC Advanced Research Center, Saudi Aramco
segam2020-3422534.1
The Value of Information from Horizontal Distributed Acoustic Sensing Compared to Multicomponent Geophones via Machine Learning
Whitney J. Trainor-Guitton , SeaOwl Energy Services contracted to Total; Samir Jreij, Cimarex Energy Co.; Michael Morphew & Ivan Lim Chen Ning; Colorado School of Mines
segam2020-3422609.1
Fast lithofacies recognition technology based on Bayesian theory and its application
Bo Wang*, Tongxing Xia, Jiaguo Ma and Tao Tian, CNOOC Ltd. Tian jin Branch
segam2020-3423159.1
Anisotropic eikonal solution using physics-informed neural networks
Umair bin Waheed 1, Ehsan Haghighat2, and Tariq Alkhalifah3 1KFUPM, 2MIT, 3KAUST
segam2020-3423186.1
Estimation of time-lapse timeshifts using Machine Learning
Yuting Duan*, Siyuan Yuan, Paul Hatchell, Jeremy Vila, and Kanglin Wang Shell International Exploration and Production Inc.
segam2020-3423283.1
Salt interpretation with U-SaltNet
Hongbo Zhou*, Sheng Xu, Equinor TPD R&T ET EXG HOU; Gentiana Ionescu, Marin Laomana, Equinor EXP EE GPE SIPH; and Nathan Weber, Equinor NA R&A GOM
segam2020-3423931.1
SymAE: an autoencoder with embedded physical symmetries for passive time-lapse monitoring
Pawan Bharadwaj , Matt Li, Laurent Demanet, Massachusetts Institute of Technology
segam2020-3424584.1
Detecting the fundamental mode of energy for surface wave analysis, modelling and inversion using a deep convolutional network
Anisha Kaul*, Aria Abubakar, Amr Misbah, Schlumberger; Phillip J. Bilsby, WesternGeco Schlumberger
segam2020-3424773.1
Cross-equalization of time-lapse seismic data using recurrent neural networks
Abdullah Alali, Vladimir Kazei, Bingbing Sun, Robert Smith, Phlippe Nivlet, Andrey Bakulin, and Tariq Alkalifah, 1-King Abdullah University of Science and Technology, 2-Saudi Aramco
segam2020-3424849.1
Application of unsupervised machine learning techniques in sequence stratigraphy and seismic geomorphology: a case of study in the Cenozoic deep-water deposits in Northern Carnarvon Basin, Australia
Laura Ortiz-Sanguino*, Jerson Tellez and Heather Bedle, The University of Oklahoma
segam2020-3424945.1
Ground roll attenuation with conditional generative adversarial network
Xu Si, China University of Geosciences (Beijing)
segam2020-3424987.1
Enrich the interpretation of seismic image segmentation by estimating epistemic uncertainty
Tao Zhao* and Xiaoli Chen, Schlumberger
segam2020-3425261.1
Joint seismic and electromagnetic inversion for reservoir mapping using a deep learning aided feature-oriented approach
Yanhui Zhang and Mohamad Mazen Hittawe, King Abdullah University of Science and Technology; Klemens Katterbauer and Alberto F. Marsala, Saudi Aramco; Omar M. Knio, and Ibrahim Hoteit, King Abdullah University of Science and Technology
segam2020-3425384.1
Combination of classic geological/geophysical data analysis and machine learning: brownfield sweet spots case study of the middle Jurassic Formation in Western Kazakhstan
Natalia Osintseva1*, Dmitry Danko 1, Ivan Priezzhev 1, Kurmangazy Iskaziyev2, Valery Ryzhkov 1 1Gubkin University, 2JSC KazMunaiGas Exploration Production
segam2020-3425406.1
Wave Propagation with Physics Informed Neural Networks
Dimitri Voytan and Mrinal K. Sen, Jackson School of Geosciences, University of Texas at Austin
segam2020-3425737.1
Target-oriented time-lapse waveform inversion using a deep learning assisted regularization
Yuanyuan Li*, Tariq Alkhalifah and Qiang Guo, King Abdullah University of Science and Technology (KAUST)
segam2020-3425747.1
Semi-supervised seismic and well log integration for reservoir property estimation
Haibin Di*, Xiaoli Chen, Hiren Maniar, Aria Abubakar, Schlumberger
segam2020-3425792.1
Ground roll attenuation with an unsupervised deep learning approach
Rui Guo*, Hiren Maniar, Haibin Di, Nick Moldoveanu, Aria Abubakar, Schlumberger, Houston, USA
Maokun Li, Tsinghua University, Beijing, China
segam2020-3425878.1
De-aliasing using the U-Net Image Segmentation Algorithm
Madhav Vyas and Qingqing Liao, BP
segam2020-3426030.1
Well-log facies classification using a semi-supervised algorithm
Wei Xie* and Kyle T. Spikes, Department of Geological Sciences, Jackson School of Geosciences, The University of Texas at Austin
segam2020-3426173.1
Deep prior based seismic data interpolation via multi-res U-net
Fantong Kong1, Francesco Picetti2, Vincenzo Lipari2, Paolo Bestagini2 and Stefano Tubaro2
1China University of Petroleum 2Politecnico di Milano
segam2020-3426273.1
Automated Identification and Quantification of Rock Types from Drill Cuttings
Youssef Tamaazousti*, Matthias François‡ and Josselin Kherroubi*; *Schlumberger AI Lab, ‡Geoservices
segam2020-3426477.1
Channel simulation and deep learning for channel interpretation in 3D seismic images
Hang Gao1, Xinming Wu1 and Guofeng Liu2
1School of Earth and Space Science, University of Science and Technology of China,
2School of Geophysics and Information Technology, China University of Geosciences (Beijing).
segam2020-3426483.1
Multispectral aberrancy
Bin Lyu*, Jie Qi, The University of Oklahoma; Fangyu Li, The University of Georgia; and Kurt J. Marfurt, The University of Oklahoma
segam2020-3426676.1
Structure enhanced Least-squares migration by deep learning based structural preconditioning
Cheng Cheng*, Yang He, Bin Wang and Yi Huang (TGS)
segam2020-3426788.1
Clinoform interpretation for stratigraphic features utilizing Machine Learning Methodology
Mikael Kvalvaer, Clifford Kelley*, RagnaRock Geo, Abdul Rahman Rahbi, Johannes Stammeijer, Nadeem Balushi, Petroleum Development Oman
segam2020-3426826.1
Numerical analysis of a deep learning formulation of multi-parameter elastic full waveform inversion
Tianze Zhang , Kristopher A. Innanen , Jian Suny, Daniel O. Trad , University of Calgary, The Pennsylvania State University
segam2020-3426827.1
Improving the accuracy of convolutional neural networks in predicting magnetization directions
Felicia Nurindrawati* and Jiajia Sun. Department of Earth and Atmospheric Sciences, University of Houston
segam2020-3426887.1
Application of seismic attributes and machine learning for imaging submarine slide blocks on the North Slope, Alaska
Shuvajit Bhattacharya*, University of Alaska Anchorage
Miao Tian, University of Texas Permian Basin
Jon Rotzien, Basin Dynamics
Sumit Verma, University of Texas Permian Basin
segam2020-3426925.1
Deep learning joint inversion of seismic and electromagnetic data for salt reconstruction
Yen Sun*, Bertrand Denel, Norman Daril, Lory Evano, Paul Williamson, Mauricio Araya-Polo
Total E&P Research & Technology USA
segam2020-3426944.1
Seismic inversion for reservoir facies under geologically realistic prior uncertainty with 3D convolutional neural networks
Anshuman Pradhan* and Tapan Mukerji, Stanford University
segam2020-3427011.1
Automatic seismic fault surfaces construction using seismic discontinuity attribute
Bo Zhang*, and Yihuai Lou, Department of Geological Sciences, The University of Alabama
segam2020-3427022.1
Automate Seismic Velocity Model Building Through Machine Learning
Jiangchuan Huang*, Jun Cao, Guang Chen and Yu Zhang, ConocoPhillips
segam2020-3427030.1
A benchmark dataset for semi-automatic seismic interpretation based on a New Zealand's seismic survey
Matheus Oliveira, Maiana Avalone, Emilio Vital Brazil and Daniel Civitarese, IBM Research Brazil
segam2020-3427085.1
Machine learning algorithms for real-time prediction of the sonic logs based on drilling parameters and downhole accelerometers
Stanislav Glubokovskikh*, Curtin University; Andrey Bakulin, Robert Smith, Ilya Silvestrov, EXPEC Advanced Research Center, Geophysics Technology, Saudi Aramco
segam2020-3427086.1
Self-supervised learning for low frequency extension of seismic data
Meixia Wang*, Sheng Xu, and Hongbo Zhou, Equinor US Operations
segam2020-3427239.1
Uncertainty estimation using Bayesian convolutional neural network for automatic channel detection
Nam Pham⇤ and Sergey Fomel, The University of Texas at Austin
segam2020-3427254.1
Deep learning for seismic image registration
Arnab Dhara*, The University of Texas at Austin; Claudio Bagaini, Schlumberger
segam2020-3427266.1
Passive seismic signal denoising using convolutional neural network
Nam Pham⇤1, Dmitrii Merzlikin1,2, Sergey Fomel1, and Yangkang Chen3
1The University of Texas at Austin; 2Currently at Schlumberger; 3Zhejiang University
segam2020-3427300.1
Detecting earthquakes through telecom fiber using a convolutional neural network
Fantine Huot* and Biondo Biondi, Stanford University
segam2020-3427349.1
Physically Realistic Training Data Construction for Data-driven Full-waveform Inversion and Traveltime Tomography
Shihang Feng, Youzuo Lin and Brendt Wohlberg, Los Alamos National Laboratory
segam2020-3427388.1
3D relative geologic time estimation with deep learning
Zhengfa Bi ;1, Zhicheng Geng2, Hang Gao1, Xinming Wu1 and Haishan Li3
1 School of Earth and Space Science, University of Science and Technology of China, 2 BEG, UT Austin, 3 PIPEDNWGI,Petrochina.
segam2020-3427504.1
Seismic horizon refinement with dynamic programming
Shangsheng Yan and Xinming Wu
School of Earth and Space Sciences, University of Science and Technology of China, Hefei, China.
segam2020-3427517.1
RNN-based dispersion inversion using train-induced signals
Lu Liu, Yujin Liu, Aramco Beijing Research Center, Aramco Asia; Yi Luo, EXPEC Advanced Research Center, Saudi Aramco
segam2020-3427522.1
Extrapolating low-frequency prestack land data with deep learning
Oleg Ovcharenko⇤, Vladimir Kazei⇤, Pavel Plotnitskiy⇤, Daniel Peter⇤, Ilya Silvestrov †, Andrey Bakulin †,
Tariq Alkhalifah ⇤
⇤ King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
† EXPEC Advanced Research Center, Saudi Aramco, Dhahran, Saudi Arabia
segam2020-3427708.1
Deep learning for characterizing paleokarst features in 3D seismic images
Xinming Wu⇤,1, Shangsheng Yan1, Jie Qi2, and Hongliu Zeng3
1School of Earth and Space Sciences, University of Science and Technology of China; 2University of Oklahoma; 3Bureau of Economic Geology, University of Texas at Austin.
segam2020-3427812.1
Automated first break picking with constrained pooling networks
David Cova*, Peigen Xie, Phuong-Thu Trinh, Total SA, Pau, France
segam2020-3427827.1
Automatic picking of multi-mode dispersion curves using CNN based machine learning
Li Ren* 1, Fuchun Gao2, Yulang Wu1, Paul Williamson2, Wenlong Wang3, George A. McMechan1
1. the University of Texas at Dallas; 2. Total EP Research and Technology; 3. Harbin Institute of Technology
segam2020-3428004.1
Seismic Inversion via Closed-Loop Fully Convolutional Residual Network and Transfer Learning
Lingling Wang, Institute of Geophysics and Geomatics, China University of Geosciences; Delin Meng, Bangyu Wu, and Naihao Liu, School of Mathematics and Statistics, Xi'an Jiaotong University
segam2020-3428107.1
Real-data earthquake localization using Convolutional Neural Networks trained with synthetic data
Nicolas Vinard*, Guy Drijkoningen, Eric Verschuur, TU Delft
segam2020-3428150.1
Parameterizing uncertainty by deep invertible networks, an application to reservoir characterization
Gabrio Rizzuti, Ali Siahkoohi, Philipp A. Witte, and Felix J. Herrmann
School of Computational Science and Engineering, Georgia Institute of Technology
segam2020-3428171.1
Comparing convolutional neural networking and image processing seismic fault detection methods
Jie Qi*, Bin Lyu, The University of Oklahoma, Xinming Wu, The University of Science and Technology of China, and Kurt Marfurt, The University of Oklahoma
segam2020-3428172.1
Elastic parameters estimation using multi-model based on machine learning
Kai Xu*, Zhentao Sun, Shixing Wang, Jinliang Tang, Ruyi Zhang, Sinopec Geophysical Research Institute
segam2020-3428250.1
Applications of machine learning techniques on angle stacks to enhance carbonate reservoir characterization
Clayton Silver*, Dr. Heather Bedle, University of Oklahoma
segam2020-3428251.1
Remote sensing data fusion and machine learning techniques for mineral exploration
Priscilla Addison, Stephen Alwon*, Alex Janevski, Kristopher Purens, and Clyde Wheeler, Descartes Labs
segam2020-3428275.1
Machine Learning model interpretability using SHAP values: application to a seismic facies classification task
David Lubo-Robles1, Deepak Devegowda1, Vikram Jayaram2, Heather Bedle1, Kurt J. Marfurt1, and Matthew J.
Pranter1, 1The University of Oklahoma; 2Pioneer Natural Resources Company
segam2020-3428303.1
Integrated interpretation of multi-geophysical inversed results using guided fuzzy c-means clustering
Jun Guo, Peng Yu*, Chongjin Zhao, Luolei Zhang, State Key Laboratory of Marine Geology, Tongji University
segam2020-3428324.1
Velocity model building by deep learning: from general synthetics to field data application
Vladimir Kazei, Oleg Ovcharenko, Tariq Alkhalifah, KAUST
segam2020-3428369.1
Machine learning for the classification of unexploded ordnance (UXO) from electromagnetic data
Lindsey J. Heagy1, Douglas W. Oldenburg2, Fernando P´erez1 & Laurens Beran3
1Department of Statistics, University of California Berkeley, 2Geophysical Inversion Facility, University of British Columbia, 3Black Tusk Geophysics
segam2020-3428431.1
Shortcutting inversion-based near-surface characterization workflows using deep learning
Bas Peters , Computational Geosciences Inc.
张贴报告：70篇
segam2020-3410057.1
Free surface elastic wave injection and wavefield reconstruction with applications to elastic RTM
Bingkai Han1,2*, Xiao-Bi Xie2
1. Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan, China
2. Institute of Geophysics and Planetary Physics, University of California, Santa Cruz, USA
segam2020-3413112.1
Source deghosting of coarsely sampled common-receiver data using machine learning
J.W. Vrolijk and G. Blacquiere*, Delft University of Technology
segam2020-3413175.1
Automation of Well Integrity Operations: Machine Learning Framework for Diagnosis of Ultrasonic Waveforms
Josselin Kherroubi* (Schlumberger), Florian Laborde (Telecom Paris), Mikhail Lemarenko (Schlumberger)
segam2020-3414896.1
Microseismic event or noise: Automatic classification with convolutional neural networks
Benjamin Consolvo*, Michael Thornton, MicroSeismic, Inc
segam2020-3415191.1
A supervised descent learning technique for inversion of directional electromagnetic loggingwhile-drilling data
Yanyan Hu1*, Rui Guo2, Yuchen Jin1, Xuqing Wu1, Maokun Li2, Aria Abubakar3 and Jiefu Chen1
1. University of Houston, 2. Tsinghua University, 3. Schlumberger
segam2020-3417560.1
Uncertainty quantification in imaging and automatic horizon tracking—a Bayesian deep-prior based approach
Ali Siahkoohi, Gabrio Rizzuti, and Felix J. Herrmann
School of Computational Science and Engineering,Georgia Institute of Technology
segam2020-3417568.1
Weak deep priors for seismic imaging
Ali Siahkoohi, Gabrio Rizzuti, and Felix J. Herrmann
School of Computational Science and Engineering,Georgia Institute of Technology
segam2020-3417918.1
Using neural networks to detect microseismicity and pick P wave arrival times in Oklahoma
Bingxu Luo and Hejun Zhu, Department of Geosciences, The University of Texas at Dallas
segam2020-3419749.1
Time-lapse seismic data inversion for estimating reservoir parameters using deep learning
Harpreet Kaur*, Alexander Sun, Zhi Zhong, and Sergey Fomel, The University of Texas at Austin.
segam2020-3420478.1
Intelligent analysis of pore structure for oil reservoir based on Conditional GAN
Yili Ren*, He Liu, Lu Luo, Jia Liang and Yan Gao, Research Institute of Petroleum Exploration & Development
segam2020-3421111.1
Machine Learning for Geophysical Characterization of Brittleness: Tuscaloosa Marine Shale Case Study
Mark Mlella , Ming Ma , Rui Zhang and Mehdi Mokhtariy
School of Geosciences, University of Louisiana at Lafayette, Lafayette, LA, 70503
Department of Petroleum Engineering, University of Louisiana at Lafayette, Lafayette, LA, 70503
segam2020-3421310.1
Characterization of the Caddo Sequence, Boonsville Field, Texas, combining Similarity Analysis and Neural Networks
Carla D. Acosta* (Engineering Geophysics Coordination, Simón Bolívar University, Venezuela), Milagrosa Aldana and Ana Cabrera, (Earth Science Department, Simón Bolívar University, Venezuela)
segam2020-3422819.1
An Attempt to Decode Reverse Time Migration through Machine Learning
Cheng Zhan (Microsoft), Chang-chun Lee(Rice University),Licheng Zhang(University of Houston),Yong Chang(TGS)
segam2020-3423396.1
Physics-guided self-supervised learning for low frequency data prediction in FWI
Wenyi Hu*, Advanced Geophysical Technology Inc., Yuchen Jin, Xuqing Wu, and Jiefu Chen, University of Houston
segam2020-3423766.1
Geophysical data and gradient translation using deep neural networks
Jiashun Yao, Lluís Guasch, Mike Warner (Imperial College London), Tim Lin (S-Cube London) and Elizabeth Percak-Dennett (Amazon Web Services, Houston)
segam2020-3423839.1
Digital Rock Physics for Elastic Characterization of Organic-Rich Source Rocks
Mita Sengupta* and Shannon L. Eichmann, Aramco Services Company
segam2020-3424142.1
Use of computational topology to quantify changes in pore space due to chemical dissolution of core matrix: a numerical study
V. Lisitsa, Institute of Mathematics SB RAS, Novosibirsk, Russia
T. Khachkova, Institute of Petroleum Geology and Geophysics SB RAS, Novosibirsk, Russia
Ya. Bazaikin, Institute of Mathematics SB RAS, Novosibirsk, Russia
segam2020-3424928.1
Automation of depth matching using a structured well-log database: Prototype well example in the North Sea.
Veronica Torres*, Kenneth Duffaut, Alexey Stovas, and Frank O. Westad, Norwegian University of Science and Technology; Yngve Bolstad Johansen, AkerBP
segam2020-3424983.1
Augment time-domain FWI with iterative deep learning
Tao Zhao*, Aria Abubakar, Xin Cheng, and Lei Fu, Schlumberger
segam2020-3425046.1
Application of a convolutional neural network to classification of swell noise attenuation
Bagher Farmani, Morten W. Pedersen*, PGS, Oslo, Norway
segam2020-3425457.1
Predict passive seismic events with a convolutional neural network
Hanchen Wang1, Tariq Alkhalifah1 & Qi Hao2,
1. King Abdullah University of Science and Technology 2. King Fahd University of Petroleum and Minerals
segam2020-3425692.1
Should we have labels for deep learning ground roll attenuation?
Dawei Liu 1,2, Wenchao Chen1, Mauricio D. Sacchi2, Hongxu Wang3
1Xi’an Jiaotong University 2University of Alberta 3Daqing Oilfield Company Ltd.
segam2020-3425785.1
Wasserstein cycle-consistent generative adversarial network for improved seismic impedance inversion: Example on 3D SEAM model
Ao Cai*, Haibin Di, Zhun Li, Hiren Maniar, Aria Abubakar, Schlumberger, Houston, TX
segam2020-3425831.1
Full waveform inversion using machine learning optimization techniques
Ricardo de Bragança*, Janaki Vamaraju and Mrinal K. Sen, UTIG - University of Texas at Austin
segam2020-3425889.1
Machine learning assisted seismic inversion
Prasenjit Roy* and Xinfa Zhu, Energy Technology Company, Chevron; Weihong Fei, North America Upstream, Chevron
segam2020-3425921.1
Elastic full wave-form inversion with recurrent neural networks
Wenlong Wang , George A. McMechan† and Jianwei Ma
Harbin Institute of Technology, † The University of Texas at Dallas
segam2020-3425975.1
Seismic Inversion by Multi-dimensional Newtonian Machine Learning
Yuqing Chen1, Erdinc Saygin11 and Gerard T. Schuster2
1Deep Earth Imaging Future Science Platform, CSIRO, Kensington, Australia
2 King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
segam2020-3426049.1
A regularization strategy for Q-compensated reverse time migration using excitation imaging condition
Mingkun Zhang1, Hui Zhou1, Hanming Chen1, Chuntao Jiang1, Shuqi jiang1, Lide Wang1
1.State Key Laboratory of Petroleum Resources and Prospecting, CNPC Key Lab of Geophysical Exploration, China University of Petroleum, 102249 Changping, Beijing, China
segam2020-3426105.1
Recognition of salt zones in 3D seismic images using Machine Learning
Xu Ji*, Nasher BenHasan, Yi Luo, EXPEC Advanced Research Center, Saudi Aramco; Ewenet Gashawbeza, Saleh M. Saleh, Red Sea Exploration Department, Saudi Aramco
segam2020-3426135.1
Seismic-reservoir characterization based on random forest and fuzzy logic algorithms
Weiheng Geng*1,2, Xiaohong Chen1,2, Jianhua Wang2,3, Jingye Li1,2, Jian Zhang1,2, Wei Tang1,2, Shuying Wei1,2
1. the State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum-Beijing
2. National Engineering Laboratory for Offshore Oil Exploration, Beijing
3. CNOOC Research Institute Co., Ltd, Beijing..
segam2020-3426151.1
Predicting mineralogy using a Deep Neural Network and Fancy PCA
Dokyeong Kim*, Junhwan Choi, Dowan Kim, and Joongmoo Byun, RISE.ML Lab., Hanyang University
segam2020-3426298.1
Joint 2D inversion of AMT and seismic travel time data with deep learning constraint
Rui Guo, Maokun Li , Fan Yang, Tsinghua University, Beijing, China
Heming Yao, Lijun Jiang, Michael Ng , The University of Hong Kong, Hong Kong, China
Aria Abubakar, Schlumberger, Houston, USA
segam2020-3426412.1
Deep learning for simultaneous seismic image super-resolution and denoising
Jintao Li1, Xinming Wu1 and Zhanxuan Hu2
1School of Earth and Space Sciences, University of Science and Technology of China, 2Northwestern Polytechnical University.
segam2020-3426516.1
Seismic fault detection based on 3D Unet++ model
Dun Yang, Yufei Cai, Guangmin Hu, Xingmiao Yao*, University of Electronic Science and Technology of China; Wen Zou, Research & Development Center, Bureau of Geophysical Prospecting(BGP), CNPC
segam2020-3426539.1
Multi-task learning based P/S wave separation and reverse time migration for VSP
Yanwen Wei⇤, Tsinghua University and National University of Singapore; Yunyue Elita Li, Jizhong Yang and Jingjing Zong, National University of Singapore; Jinwei Fang, China University of Petroleum (Beijing); Haohuan Fu, Tsinghua University
segam2020-3427097.1
Rock physics modeling using machine learning
Lian Jiang* and John P. Castagna, Department of Earth and Atmospheric Sciences, University of Houston; Brian Russell, CGG; Pablo Guillen, Hewlett Packard Enterprise Data Science Institute, University of Houston
segam2020-3427108.1
Enhancing spatial continuity of seismic facies via fuzzy c-means with cross-entropy constraints
Hanpeng Cai *, Yifeng Fei, Jiandong Liang, School of Resources and Environment, University of Electronic Science and Technology of China (UESTC).
Jun Wang, Zhipeng Li, Research Institute of Exploration & Production Shengli Oilfield Branch Co., SINOPEC
segam2020-3427129.1
The Poisson effect influence on the stress dependent fluid migration properties of a fracture
Bo-Ye Fu *, 1, 2, Arthur Cheng1 and Yunyue Elita Li1
1 Department of Civil end Environmental Engineering, National University of Singapore
2 Institute of Geology and Geophysics, Chinese Academy of Sciences
segam2020-3427177.1
Least-squares reverse-time migration with a machine-learning-based denoising preconditioner
Xuejian Liu*, Yuqing Chen and Lianjie Huang, Los Alamos National Laboratory, Los Alamos, NM 87545
segam2020-3427292.1
Complete Sequence Stratigraphy from Seismic Optical Flow without Human Labeling
Zhun Li* and Aria Abubakar, Schlumberger
segam2020-3427334.1
The overestimated elastic moduli from digital rock images: computational reasons
Jiabin Liang*, Stanislav Glubokovskikh, Boris Gurevich, Maxim Lebedev and Stephanie Vialle, Curtin University
segam2020-3427351.1
Wavefield reconstruction inversion via machine learned functions
Chao Song and Tariq Alkhalifah, King Abdullah University of Science and Technology.
segam2020-3427448.1
A workflow of separating and imaging diffraction wave by using deep learning network: an application of GPR data
Ming Ma and Rui Zhang, University of Louisiana at Lafayette, School of Geosciences; Jonathan Ajo-Franklin, Rice University
segam2020-3427494.1
Kernel Prediction Network for Common Image Gather Stacking
Ziang Li1*, Xinming Wu1, Luming Liang2, and Xiaofeng Jia1, 1. School of Earth and Space Sciences, University of Science and Technology of China, Hefei, Anhui, 230026, China; 2. Applied Sciences Group, Microsoft
segam2020-3427510.1
Data augmentation using CycleGAN for overcoming the imbalance problem in petrophysical facies classification
Dowan Kim*, and Joongmoo Byun, RISE.ML, Hanyang University.
segam2020-3427515.1
Digital rock image inpanting using GANs
Yong Zheng Ong, Nan You, Yunyue Elita Li, National University of Singapore；Haizhao Yang, Purdue University
segam2020-3427534.1
A new method of thin interlayer net thickness prediction based on SVM algorithm and its application
Jiangbo Huang*, Dongjia Hou, Gaiwei Wang, Jian Ren, Baolin Yue
CNOOC (China National Offshore Oil Corporation) Ltd Tianjin Branch, Tianjin, China
segam2020-3427540.1
Machine learning with Artificial Neural Networks for shear log predictions in the Volve field Norwegian North Sea
Aun Al Ghaithi* and Manika Prasad, Colorado School of Mines
segam2020-3427542.1
Sensitivity analysis for successful microseismic moment tensor inversion using machine learning
Jihun Choi*, Joongmoo Byun and Soon Jee Seol, RISE.ML, Hanyang University
segam2020-3427602.1
Strategies in picking training data for 3D convolutional neural networks in stratigraphic interpretation
Oddgeir Gramstad*, Michael Nickel, Bartosz Goledowski, Schlumberger Stavanger Research; and Marie Etchebes, Schlumberger Doll Research
segam2020-3427668.1
Estimation method of group velocity dispersion attribute and its application based on synchrosqueezing wavelet transform: A case study from BZ Oilfield, Bohai Bay
Shengqiang Zhang* CNOOC (China National Offshore Oil Corporation) China Limited, Tianjin Branch, P.R.China
segam2020-3427757.1
Real-Time Seismic Attributes Computation with Conditional GANs
João Paulo Navarro, Pedro Mário Cruz e Silva, Doris Pan and Ken Hester, NVIDIA
segam2020-3427796.1
Waveform impedance sensitivity kernel for elastic reverse time migration
Hong Liang*, Houzhu Zhang, Aramco Americas: Aramco Research Center-Houston,
Hongwei Liu, EXPEC Advanced Research Center, Saudi Aramco
segam2020-3427836.1
Automatic velocity model building with machine learning
Chaoguang Zhou* and Samuel Brown, PGS
segam2020-3427882.1
Transfer learning in large-scale ocean bottom seismic wavefield reconstruction
Mi Zhang1;3, Ali Siahkoohi2, and Felix J. Herrmann1;2
1School of Earth and Atmospheric Sciences, Georgia Institute of Technology,
2School of Computational Science and Engineering, Georgia Institute of Technology,
3State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum - Beijing
segam2020-3427887.1
Deep learning in seismic processing: Trim statics and demultiple
Alexander Breuer , Norman Ettrich, and Peter Habelitz, Fraunhofer ITWM
segam2020-3427911.1
Rotation invariant CNN using scattering transform for seismic facies classification
Fu Wang*, Huazhong Wang and Xinquan Huang, Tongji University
segam2020-3427984.1
VTI parameters determination from synthetic sonic logging data using a convolutional neural network
Maksim Bazulin and Denis Sabitov; Skolkovo Institute of Science and Technology, Marwan Charara, Aramco Innovations LLC., Aramco Research Center - Moscow
segam2020-3428013.1
Quality control of deep generator priors for statistical seismic inverse problems
Zhilong Fang1,3, Hongjian Fang2,3, Laurent Demanet1,2,3
1Department of Mathematics, Massachusetts Institute of Technology
2Department of Earth, Atmospheric, and Planetary Sciences, Massachusetts Institute of Technology
3Earth Resource Laboratory, Massachusetts Institute of Technology
segam2020-3428018.1
Deep learning for recognition of sedimentary microfacies with logging data
Hanpeng Cai*, Yongxiang Hu, School of Resources and Environment & Center for Information Geoscience, University of Electronic Science and Technology of China.
segam2020-3428087.1
Elastic full waveform inversion with extrapolated low-frequency data
Hongyu Sun and Laurent Demanet, Earth Resources Laboratory, Massachusetts Institute of Technology
segam2020-3428098.1
Uncertainty estimation in impedance inversion using Bayesian deep learning
Junhwan Choi*, Dowan Kim, and Joongmoo Byun, RISE.ML Lab., Hanyang University
segam2020-3428135.1
Inferring fault friction properties and background stress using fluid flow and dynamic rupture modeling, and machine learning techniques – concept case study of the M4.8 Timpson (TX) earthquake.
Dawid Szafranski* and Benchun Duan, Texas A&M University
segam2020-3428270.1
Low frequency generation and denoising with recursive convolutional neural networks
Gabriel Fabien-Ouellet*, Polytechnique Montreal
segam2020-3428298.1
Spatiotemporal Modeling of Seismic Images for Acoustic Impedance Estimation
Ahmad Mustafa, Motaz Alfarraj and Ghassan AlRegib, Center for Energy and Geo Proceiing (CeGP), School of Electrical and Computer Engineering, Georgia Institute of Technology, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia
segam2020-3428331.1
3D Seismic Data Compression With Multi-resolution Autoencoders
Ana Paula Schiavon1; Kevyn Swhants dos Santos Ribeiro1; Jo ˜ ao Paulo Navarro2; Marcelo Bernardes Vieira1 and Pedro M´ ario Cruz e Silva2; UFJF1 and NVIDIA2
segam2020-3428333.1
RNN-based gradient prediction for solving magnetotelluric inverse problem
Yuchen Jin , Yanyan Hu, Xuqing Wu, and Jiefu Chen, University of Houston
segam2020-3428348.1
Crossline interpolation with the traces-to-trace approach using machine learning
Zeu Yeeh* and Joongmoo Byun, Rise.ml. lab., Hanyang Univ.; Daeung Yoon, Chonnam National University
segam2020-3428378.1
Joint Learning for Seismic Inversion: An Acoustic Impedance Estimation Case Study
Mustafa and Ghassan AlRegib, Center for Energy and Geo Processing (CeGP), School of Electrical and Computer Engineering, Georgia Institute of Technology
segam2020-3428410.1
Application of genetic inversion for rock property prediction in the F3 Block, North Sea Basin
Anuola Osinaike*1, John Onayemi2, 1Reighshore Energy Services Limited, 2University of Lagos
赵改善于2020年10月17日整理
原文来源：https://mp.weixin.qq.com/s?__biz=MzA3MzI5NTQ2MQ==&mid=2452870968&idx=1&sn=4109cf9bc027a28fb54b88d86ac225b7&chksm=88d64432bfa1cd246f36786eef8d4ae151fccfa6ea3a097fc11adac8ff790a73a0f3c27219dc&mpshare=1&scene=23&srcid=1018r73momfHbH11ZoIAoI99&sharer_sharetime=1603023469546&sharer_shareid=1a7dcac6f418983c456dd6b9ec485160#rd