ABSTRACT: Selected Topics in Seismic Dispersion

Christopher L. Liner

Houston Geological Society Bulletin

..., reflection and transmission coefficients, head waves, etc. The convolutional reflection models we use to model thick and thin bed thin response...

Detect Oil Spill in Offshore Facility Using Convolutional Neural Network and Transfer Learning

Dharmawan Raharjo, Muhamad Solehudin

Indonesian Petroleum Association

...Detect Oil Spill in Offshore Facility Using Convolutional Neural Network and Transfer Learning Dharmawan Raharjo, Muhamad Solehudin This paper has...

Abstract: Modeling of Seismic Signatures of Carbonate Rock Types, by B. Jan and Y. Sun, #90188 (2014)

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Abstract: Machine Learning Assisted Fracture Characterization with Borehole Image Logs in Geothermal Wells; #91204 (2023)

Chicheng Xu

Search and Discovery.com

... from multiple sources of data, we build a convolutional neural network model and train it with the labeled results from borehole image log. The model...

Transfer learning seismic and GPR diffraction separation with a convolutional neural network

Alexander Bauer, Jan Walda, Dirk Gajewski

International Meeting for Applied Geoscience and Energy (IMAGE)

...Transfer learning seismic and GPR diffraction separation with a convolutional neural network Alexander Bauer, Jan Walda, Dirk Gajewski Transfer...

Improve automatic migrated gather processing with feature engineering and 4D convolutional neural networks

Wen Pan, Harry Rynja, Ramakrishna Dandu, Zaifeng Liu, Shuzhen Ye, Antonio De Lilla, Jay Chen, Jeremy Vila

International Meeting for Applied Geoscience and Energy (IMAGE)

... generalizable machinelearning model, we propose to use 4D convolution neural networks (4D GAP) that are built with consecutive 3D convolutional layers...

Abstract: Reflectivity Color Correction in Gabor Deconvolution; #90211 (2015)

Carlos Montana and Gary Margrave

Search and Discovery.com

... relies on the fulfillment of a set of assumptions on which the convolutional model is based: stationarity, minimum phase wavelet, white reflectivity...

Representation Learning in Seismic Interpretation

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Abstract: Deep Learning Inversion on Seismic Cubes; #91204 (2023)

Aleksandr Koriagin, Alexey Kozhevin, Stepan Goriachev, Roman Khudorozhkov

Search and Discovery.com

... show how one can perform inference on full seismic cubes using convolutional neural networks and specific prediction aggregation techniques...

Depositional Facies Identification in Wireline Log Patterns Using 1D Convolutional Neural Network (CNN) Deep Learning Algorithms

Galatio Giovani Prabowo, Muhammad Fahmi Ramdani, Abiyyu Daffa Revanzha, Brian Muara Sianturi, Natalia Angel Momongan

Indonesian Petroleum Association

... to use Python, generating dummy data, training data, and model testing. The chosen tool for this research is the Convolutional Neural Network (CNN...

Seismic diffractions separation and imaging based on convolutional neural network

Jiaxing Sun, Jidong Yang, Zhenchun Li, Jianping Huang, Jie Xu

International Meeting for Applied Geoscience and Energy (IMAGE)

...Seismic diffractions separation and imaging based on convolutional neural network Jiaxing Sun, Jidong Yang, Zhenchun Li, Jianping Huang, Jie Xu...

Unsupervised compensation of spiral-shaped drone magnetic survey using a recurrent convolutional autoencoder

Brett Bernstein, Yaoguo Li, Richard Hammack, Colton Kohnke

International Meeting for Applied Geoscience and Energy (IMAGE)

...Unsupervised compensation of spiral-shaped drone magnetic survey using a recurrent convolutional autoencoder Brett Bernstein, Yaoguo Li, Richard...

Fracture-cavity carbonate reservoir identification based on channel attention mechanisms

Liuxin Yang, Yongqiang Ma, Guangxiao Deng, Zhen Wang

International Meeting for Applied Geoscience and Energy (IMAGE)

... attention mechanisms in a semi-supervised learning framework. The architecture of our inversion model consists of several attention blocks, which combine...

Looking for a simplified and generalized training set in ML applications for gravity modelling

Luigi Bianco, Ciro Messina, Maurizio Fedi

International Meeting for Applied Geoscience and Energy (IMAGE)

... be seen as the building blocks of each gravimetric anomaly. Here, we discuss preliminary results obtained with a Convolutional Neural Network (CNN...

Deep learning based wavefield separation method for VSP data

Gang Feng, Qin Su, Zhe Yang, Wei Yang, Jian-Hua Wang

International Meeting for Applied Geoscience and Energy (IMAGE)

... separation task is implemented using a convolutional neural network and synthetic VSP data, and a geological model construction method based...

Increasing signal-to-noise ratio of borehole image logs using convolutional neural networks

Mustafa A. Al Ibrahim, Mokhles M. Mezghani

International Meeting for Applied Geoscience and Energy (IMAGE)

... using a convolutional neural network. Results are evaluated quantitatively and qualitatively. Finally, the model is applied on the image log...

Feature Detection for Digital Images Using Machine Learning Algorithms and Image Processing

Xiao Tian, Hugh Daigle, Han Jiang

Unconventional Resources Technology Conference (URTEC)

... is increased greatly. There are 16 weight layers in vgg16 model, including 13 convolutional layers and 3 fully-connected layers. There are 19 weight...

Realistic synthetic data generation using neural style transfer: Application to automatic fault interpretation

Min Jun Park, Joseph Jennings, Bob Clapp, Biondo Biondi

International Meeting for Applied Geoscience and Energy (IMAGE)

... suitable to be used as training images than the original synthetic images. To verify the effectiveness of our workflow, we train a model on the synthetic...

Mitigating elastic effects of acoustic full-waveform inversion with deep learning and application to field data

Dimitri Voytan, Adriano Gomes, Debanjan Datta, Ren-douard Plessix, Anu Chandran, Ken Matson

International Meeting for Applied Geoscience and Energy (IMAGE)

... convolutional network approach presented by Li et al. (2019) to a realistic 3D synthetic velocity model under a narrow-azimuth marine streamer acquisition...

Predicting Facies, Rock, and Geomechanical Properties Using Convolutional Neural Networks: A Case Study From an Unconventional Shale Reservoir

Ted Holden, Ruth Kurian, Mohammed Ibrahim, Daniel Hampson, Jonathan Downton

Unconventional Resources Technology Conference (URTEC)

...) Synthetic angle gathers are then generated for each pseudo-well using a convolutional model in which the P-wave reflection coefficients calculated using...

Deep neural networks for 1D impedance inversion

Vladimir Puzyrev, Anton Egorov, Anastasia Pirogova, Chris Elders, Claus Otto

Petroleum Exploration Society of Australia (PESA)

... such as the 160-layer velocity model used as an example in this study require large synthetic datasets for training, which are not always possible...

Automated active learning for seismic facies classification

Haibin Di, Leigh Truelove, Aria Abubakar

International Meeting for Applied Geoscience and Energy (IMAGE)

... convolutional neural networks have been popularly implemented for seismic image interpretation including facies classification, the performance...

Integrating U-net with full-waveform inversion for an efficient salt body construction

Abdullah Alaliand, Tariq Alkhalifah

International Meeting for Applied Geoscience and Energy (IMAGE)

..., T., 2016, Full-model wavenumber inversion: An emphasis on the appropriate wavenumber continuation: Geophysics, 81, no. 3, R89–R98, doi: https...

The Hybrid Theory-Guided Data Science-Based Method: Unlocking the Full Potential of Seismic Reservoirs Characterization

Rino Saputra, Akash Mathur, Awal Mandong

Indonesian Petroleum Association

... model. 5. Synthetic angle gathers are then generated for each pseudo-well using a convolutional model in which the P-wave reflection coefficients...

Deep Learning Applied to Fault Interpretation and Attribute Computation

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