Bridging the gap: Deep learning on seismic field data with synthetic training for building Gulf of Mexico velocity models

Stuart Farris, Robert Clapp

International Meeting for Applied Geoscience and Energy (IMAGE)

...; Tarantola, 1984). However, the success of FWI is closely linked to factors like the accuracy of the starting model, the frequency range of the recorded...

The impact of the synthetic seismic data generation method on automated AI-based horizon interpretation

F. Vizeu, J. Zambrini, A. Canning

International Meeting for Applied Geoscience and Energy (IMAGE)

... by using the convolutional model with full control of the synthetic wavelet, and add noise to it. To convert the 2D data into 3D we use a technique...

Abstract: Post-stack Inversion of the Hussar Low Frequency Seismic Data; #90187 (2014)

Patricia E. Gavotti, Don C. Lawton, Gary F. Margrave, and J. Helen Isaac

Search and Discovery.com

... when the low-frequency component is absent in the seismic data. Filtered seismic-data (10-15-60-85 Hz) and an initial model with a 10-15 Hz cut-off were...

Abstract: Kirchhoff Imaging with Adaptive Greens Functions for Compensation for Dispersion, Attenuation, and Velocity Imprecision; #90187 (2014)

Andrew V. Barrett

Search and Discovery.com

... frequencies appear to propagate at the velocity of the asymptotic high frequency. If we know the attenuation constant ‘Q’, and if the model for attenuation...

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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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...

Convolutional neural networks as an aid to biostratigraphy and micropaleontology: a test on late Paleozoic microfossils

Rafael Pires De Lima, Katie F. Welch, James E. Barrick, Kurt J. Marfurt, Roger Burkhalter, Murphy Cassel, Gerilyn S. Soreghan

PALAIOS

...) Inception V3 CNN architecture reached a 3.5% top-5 error (frequency in which the model cannot predict the correct class as one of the top five most...

Abstract: Automated Fault Detection from 3-D Seismic Using Artificial Intelligence „ Practical Application and Examples from the Gulf of Mexico and North Slope Alaska;

Andrew Pomroy, Zachary Wolfe

Search and Discovery.com

... in the realm of seismic attributes given its well established strengths in image pattern analysis and recognition. With this in mind, a Convolutional...

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

Pengfei Xie, Jiagen Hou

Search and Discovery.com

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

Unsupervised deep learning for seismic data reconstruction

Gui Chen, Yang Liu, Mi Zhang

International Meeting for Applied Geoscience and Energy (IMAGE)

... (RSVD) and projection onto convex sets (POCS) algorithms iteratively to reconstruct each frequency slice of the incomplete data in the f-x domain...

High-resolution angle gather tomography with Fourier neural operators

Sean Crawley, Guanghui Huang, Ramzi Djebbi, Jaime Ramos, Nizar Chemingui

International Meeting for Applied Geoscience and Energy (IMAGE)

... data and field data. Additionally, migrated data already occupies the same domain as the target velocity model (plus some kind of angle/extended image...

Abstract: Variable-factor S-transform for Time-frequency Decomposition, Deconvolution, and Noise Attenuation; #90172 (2014)

Todor I. Todorov, Gary F. Margrave

Search and Discovery.com

... to the physical phenomena of the seismic wave propagation in the earth over the traditional stationary convolutional model. Margrave and Lamoureux...

Abstract: Push the Limits of Seismic Resolution Using Surface Consistent Gabor Deconvolution; #90171 (2013)

Xinxiang Li and Darren P. Schmidt

Search and Discovery.com

... and the time-variant earth wavelet in a nonstationary convolutional trace model, which can be approximately factorized in the Gabor domain...

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...

Abstract: Can Q Explain Observations Made from a VSP?, by Hamish Wilson, Scott W. Peters, and Robert W. Wiley; #90105 (2010)

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Joint data and physics model driven full-waveform inversion using CMP gathers and well-logging data

Shuliang Wu, Jianhua Geng

International Meeting for Applied Geoscience and Energy (IMAGE)

... can get more accurate and stable inversion result in the situation of lacking low-frequency data and bad initial model. Introduction Velocity model...

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: 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...

S/N RATIO AND BANDWIDTH CONSIDERATIONS WHEN UTILIZING SEISMIC DATA IN EXPLORING FOR SUBTLE TRAPS - EXAMPLES FROM THE KNOX PLAY

Edward R. Tegland, Exploration Development, Inc., S. Pikes Peak Dr., Parker, CO Patrick H. Bygott, Exploration Development, Inc., S. Pikes Peak Dr., Parker, CO

Ohio Geological Society

.... What is bandwidth? Bandwidth is the difference between the highest and lowest measurable frequency present in the data...

What samples must seismic interpreters label for efficient machine learning?

Ryan Benkert, Mohit Prabhushankar, Ghassan AlRegib

International Meeting for Applied Geoscience and Energy (IMAGE)

... resources AlRegib et al. (2018). At the core of successful machine learning algorithms, stands the mathematical model representation of data points...

Multi-realization seismic data processing with deep variational preconditioners

Matteo Ravasi

International Meeting for Applied Geoscience and Energy (IMAGE)

... at the available traces. The modelling operator combines the up- and down-going fields in the frequency-wavenumber domain to produce the total pressure...

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

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Representation Learning in Seismic Interpretation

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Conditioning Stratigraphic, Rule-Based Models with Generative Adversarial Networks: A Deepwater Lobe, Deep Learning Example; #42402 (2019)

Honggeun Jo, Javier E. Santos, Michael J. Pyrcz

Search and Discovery.com

... trend model, parameterized by gradients, orientations, mean, and standard deviation. Our deep learning-based, local data conditioning workflow consists...

Seismic Data Preconditioning for Improved Reservoir Characterization (Inversion and Fracture Analysis); #41347 (2014)

Darren Schmidt, Alicia Veronesi, Franck Delbecq, and Jeff Durand

Search and Discovery.com

... inversion schemes use well logs to construct the low frequency model to account for the missing low frequencies in the seismic. When the model has to fill...