Seismic imaging profile noise suppression based on self supervised deep learning: a case study in the Yellow Sea

Qiang Xu

International Meeting for Applied Geoscience and Energy (IMAGE)

...). This paper proposes an approach using an improved extended autoencoder convolutional neural network model for deep learning to achieve denoising in seismic...

High-resolution seismic data processing method based on deep convolutional dictionary learning

Xiayu Gao, Qingyu Feng, Yaojun Wang, Bangli Zou, Yang Luo

International Meeting for Applied Geoscience and Energy (IMAGE)

... decomposition on the entire image, fully considering the local relevance of seismic data and strictly following the seismic data convolutional model...

Facies Classification Based on Well Logs by Using an Convolutional Neural Network

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Convolutional Neural Networks for Semantic Segmentation of Micro-Pores in SEM Based Images of Shales

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Deep Convolutional Neural Networks for Seismic Salt-Body Delineation

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ABSTRACT: Maximum Likelihood Deconvolution: a New Perspective, by Jerry M. Mendel; #91035 (2010)

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Abstract: A Convolutional Neural Network for Vuggy Facies Classification from Borehole Images;

Jiajun Jiang, Dawn McAlpin, Chicheng Xu, Rui Xu, Scott James, Weichang Li

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...Abstract: A Convolutional Neural Network for Vuggy Facies Classification from Borehole Images; Jiajun Jiang, Dawn McAlpin, Chicheng Xu, Rui Xu, Scott...

Abstract: Horizontal Stress Prediction Using Seismic Velocities Based on Convolutional Neural Network; #91206 (2023)

Fatemeh Saberi, Esmael Makarian, Ayub Elyasi, Tomomewo Stanley Olusegun

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...Abstract: Horizontal Stress Prediction Using Seismic Velocities Based on Convolutional Neural Network; #91206 (2023) Fatemeh Saberi, Esmael Makarian...

Extending Engine Change Out Respective to Running Hours Using Data Driven Using One Dimension (1-D) Convolutional Neural Network Algorithm

Subhan Malik, Harry Poetra Soedarsono

Indonesian Petroleum Association

...Extending Engine Change Out Respective to Running Hours Using Data Driven Using One Dimension (1-D) Convolutional Neural Network Algorithm Subhan...

Abstract: Predicting the Distribution of Subsurface Sedimentary Facies Using Deep Convolutional Progressive Generative Adversarial Network (Progressive GAN);

Suihong Song, Tapan Mukerji, Jiagen Hou

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...Abstract: Predicting the Distribution of Subsurface Sedimentary Facies Using Deep Convolutional Progressive Generative Adversarial Network...

Abstract: AI- Assisted Palynological Analysis Using an Expert-Trained Convolutional Neural Network: A Case Study form the Jurassic in the North Sea; #91204 (2023)

Rader Abdul Fattah, Merijn de Bakker, Alexander Houben, Roel Verreussel, Robert Williams

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...Abstract: AI- Assisted Palynological Analysis Using an Expert-Trained Convolutional Neural Network: A Case Study form the Jurassic in the North Sea...

AI to Improve the Reliability and Reproducibility of Descriptive Data: A Case Study Using Convolutional Neural Networks to Recognize Carbonate Facies in Cores

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Abstract: Grain Segmentation and Region Mask Generation in Digital Rock Images Using Convolutional Neural Networks;

Rengarajan Pelapur, Arash Aghaei, Connor Burt, Bidur Bohara

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... neural networks. This model is trained on a database of rock models generated using a 3D process-based modeling technique. Convolutional Neural Network...

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... Lithotypes Classification with Convolutional Neural Networks Evgeny E. Baraboshkin1, Evdokiya A. Panchenko2, Andrey E. Demidov1, Ardiansyah...

Horizontal Stresses Prediction Using Sonic Transition Time Based on Convolutional Neural Network; #42587 (2023)

Esmael Makarian, Ayub Elyasi, Fatemeh Saberi, Olusegun Stanley Tomomewo

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...Horizontal Stresses Prediction Using Sonic Transition Time Based on Convolutional Neural Network; #42587 (2023) Esmael Makarian, Ayub Elyasi, Fatemeh...

Abstract: GAN-Based Multipoint Geostatistical Inversion Method and Application;

Pengfei Xie, Jiagen Hou

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... technology. Multi-point statistics (MPS) generate model realizations by training image (TI) that are consistent with prior information. This method often...

Automated metallic pipeline detection using magnetic data and convolutional neural networks

Brett Bernstein, Yaoguo Li, Richard Hammack

International Meeting for Applied Geoscience and Energy (IMAGE)

...Automated metallic pipeline detection using magnetic data and convolutional neural networks Brett Bernstein, Yaoguo Li, Richard Hammack Automated...

Multichannel seismic deconvolution via 2D K-SVD and convolutional sparse coding

Guiqian Zhang, Xiayu Gao, Bangli Zou, Yaojun Wang, Yingzhu Chen

International Meeting for Applied Geoscience and Energy (IMAGE)

... to the deconvolution objective function in the form of regularization. Frequency Decomposition of Seismic Profile According to the convolutional model...

Explainable AI: Can neural networks recognize first arrivals after wave separation?

Yanwen Wei, Zhenyu Zhu, Jicai Ding, Yichuan Wang

International Meeting for Applied Geoscience and Energy (IMAGE)

... principles that how neural networks separate waves. Research in explainable AI for interpreting deep convolutional neural networks involves visualizing...

Automated detection of ferromagnetic pipelines from magnetic total-field anomaly data using convolutional neural networks

Brett Bernstein, Yaoguo Li, Richard Hammack

International Meeting for Applied Geoscience and Energy (IMAGE)

...Automated detection of ferromagnetic pipelines from magnetic total-field anomaly data using convolutional neural networks Brett Bernstein, Yaoguo Li...

High precision microseismic phase picking and monitoring based on advanced deep learning

Jiayu Qiao, Jingye Li, Yaru Xue, Wenhua Xu, Yangkang Chen

International Meeting for Applied Geoscience and Energy (IMAGE)

.... In this paper, a convolutional neural network model based on a self-attention mechanism is proposed. It can not only meet the requirement that the input...

Semi-automated prestack seismic inversion workflow using temporal convolutional networks

Hussain Alfayez, Robert Smith, Ayman Suleiman, Nasher AlBinHasan

International Meeting for Applied Geoscience and Energy (IMAGE)

... problems, the model often needs access to a long history of the input sequence. Regular convolutional neural networks (CNN) struggle to capture long-term...

Abstract: Recognizing facies in the Red River Formation of North Dakota using a Convolutional Neural Network; #90301 (2017)

Stephan H. Nordeng, Ian E. Nordeng, Jeremiah Neubert, Emily G. Sundell

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...Abstract: Recognizing facies in the Red River Formation of North Dakota using a Convolutional Neural Network; #90301 (2017) Stephan H. Nordeng, Ian E...

Abstract: Object Detection in SEM Images Using Convolutional Neural Networks: Application on Pyrite Framboid Size-Distribution in Fine-Grained Sediments;

Artur Davletshin, Lucy Tingwei Ko, Kitty Milliken, Priyanka Periwal, Wen Song

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...Abstract: Object Detection in SEM Images Using Convolutional Neural Networks: Application on Pyrite Framboid Size-Distribution in Fine-Grained...

Physics-directed unsupervised machine learning: Quantifying uncertainty in seismic inversion

Sagar Singh, Yu Zhang, David Thanoon, Pandu Devarakota, Long Jin, Ilya Tsvankin

International Meeting for Applied Geoscience and Energy (IMAGE)

...) methodology for efficient and accurate seismic impedance inversion. The first stage utilizes the generalization capability of convolutional neural...