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SEG2020年会139篇机器学习地球物理应用论文分享

2020-10-18 20:19:13 administrator 5

      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


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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


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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


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Automatic velocity model building with machine learning

Chaoguang Zhou* and Samuel Brown, PGS


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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


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Deep learning in seismic processing: Trim statics and demultiple

Alexander Breuer , Norman Ettrich, and Peter Habelitz, Fraunhofer ITWM


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Rotation invariant CNN using scattering transform for seismic facies classification

Fu Wang*, Huazhong Wang and Xinquan Huang, Tongji University


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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


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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.


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Elastic full waveform inversion with extrapolated low-frequency data

Hongyu Sun  and Laurent Demanet, Earth Resources Laboratory, Massachusetts Institute of Technology


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Uncertainty estimation in impedance inversion using Bayesian deep learning

Junhwan Choi*, Dowan Kim, and Joongmoo Byun, RISE.ML Lab., Hanyang University


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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


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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


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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


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RNN-based gradient prediction for solving magnetotelluric inverse problem

Yuchen Jin , Yanyan Hu, Xuqing Wu, and Jiefu Chen, University of Houston


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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


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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