Marchenko redatuming and seismic interferometry based internal multiple prediction for salt structures

Zhiwei Gu, Jianhua Geng, Ru-Shan Wu

International Meeting for Applied Geoscience and Energy (IMAGE)

...-pseudo-depth domain but simultaneously incur higher computational costs. Jakubowicz (1998) extends the Surface-Related Multiple Elimination (SRME...

Deep learning based automatic marker separation

Atul Laxman Katole, Aria Abubakar, Edo Hoekstra, Srikanth Ryali, Tao Zhao

International Meeting for Applied Geoscience and Energy (IMAGE)

... entirely dispenses with convolutional and recurrence-based approaches, and instead rely on the attention mechanism to model the sequential nature...

Abstract: Correlation of P-P and P-S Data in Yinggehai Basin, South China Sea; #90171 (2013)

Jinfeng Ma, Le Gao, and Igor Morozov

Search and Discovery.com

... for making P-S offset synthetic seismograms using convolutional model was proposed by Stewart (1991). In the weak contrast of the boundary, P-S wave...

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

Search and Discovery.com

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

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

Search and Discovery.com

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

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...

Implementation of Seismic Data Quality Characterisation Using Supervised Deep Learning

Joshua Thorp, Krista Davies, Julien Bluteau, Peter Hoiles

Australian Petroleum Production & Exploration Association (APPEA) Journal

... convolutional autoencoders. Geophysics 83, A39–A43. doi:10.1190/geo2017-0524.1 Tishchenko, I. (2016). Different methods of QC the low frequency content...

Machine learning applications to seismic structural interpretation: Philosophy, progress, pitfalls, and potential

Kellen L. Gunderson, Zhao Zhang, Barton Payne, Shuxing Cheng, Ziyu Jiang, and Atlas Wang

AAPG Bulletin

... geometric invariance-enforced deep learning based on the Mask region-based convolutional neural network (R-CNN) model. Mask R-CNN is a deep learning model...

Abstract: Machine Learning and Deep Learning in Oil and Gas Industry: A Review Ofapplications, Opportunities and Challenges; #91204 (2023)

Tejas Balasaheb Sabale, Syed Aaquib Hussain, Mohd Zuhair, Mohammad Saud Afzal, Arnab Ghosh

Search and Discovery.com

... algorithms to predict the outcome correctly [17]. The model is initialized with control parameters and then the input data is fed into the model...

Pyseis: A high-performance, user-friendly Python package for GPU-accelerated seismic modeling and subsurface imaging

Stuart Farris, Guillaume Barnier, Ettore Biondi, Robert Clapp

International Meeting for Applied Geoscience and Energy (IMAGE)

... NVIDIA V100 GPU. In these tests, we kept the number of time steps constant while varying the size of the model domain, a realistic scenario where high...

Abstract: New Approach to Finite-Difference Memory Variables by Using Lagrangian Mechanics; #90187 (2014)

Wubing Deng and Igor Morozov

Search and Discovery.com

...  J (Pa.s) k Figure 1. Dissipation factor as a function of frequency for a GSLS model with five Maxwell bodies. k J (MPa) 1000 15 15 15 15 15...

Comparison of Seismic Reconvolution and Gabor Deconvolution in Improving Seismic Images to Detect Local Fluid Trapping

Madaniya Oktariena, Wahyu Triyoso

Indonesian Petroleum Association

....   The convolutional model is constructed using the Gabor Transform of a non-stationary seismic to estimate Gabor Transform of the reflectivity. While...

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...

How Machine Learning is Helping Seismic Structural Interpreters in The Age of Big Data

Çağil Karakaş, James Kiely

GEO ExPro Magazine

... is a very time-consuming task, often leading to a simplified fault model, a geology-driven, machine-learning workflow can significantly improve...

Machine-Learning-Assisted Segmentation of FIB-SEM Images with Artifacts for Improved of Pore Space Characterization of Tight Reservoir Rocks

Andrey Kazak, Kirill Simonov, Victor Kulikov

Unconventional Resources Technology Conference (URTEC)

... on a convolutional neural network (CNN) in the DeepUnet configuration. The implementation utilized the Pytorch framework in a Linux environment...

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

Search and Discovery.com

Generating geophysical models from text for constructing the dataset of learning-based MT inversion

Yutong Li, Hongyu Zhou, Rui Guo, Maokun Li, Aria Abubakar

International Meeting for Applied Geoscience and Energy (IMAGE)

..., Rapid surrogate modeling of electromagnetic data in frequency domain using neural operator: IEEE Transactions on Geoscience and Remote Sensing, 60...

Deep Learning for Quantitative Hydraulic Fracture Profiling from Fiber Optic Measurements

Weichang Li, Han Lu, Yuchen Jin, Frode Hveding

Unconventional Resources Technology Conference (URTEC)

... to the synced pump data independently; and II) a convolutional LSTM (long short-term memory) sequence learning model maps time segments...

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...

Kutei Basin: Feasibility Study of a Broadband Acquisition

Gilbert Del Molino, Fabri Ikhlas Gumulya, Dedy Sulistiyo Purnomo, Paolo Battini, Bonita Nurdiana Ersan, Francesca Brega, Ferdinando Rizzo, Giorgio Cavanna, Buia Michele

Indonesian Petroleum Association

... conventional and broadband synthetic elastic inversions, the frequency bandwidth of prior model to be included in order to obtain optimal results appeared...