Marchenko focusing using convolutional neural networks

Mert S. R. Kiraz, Roel Snieder

International Meeting for Applied Geoscience and Energy (IMAGE)

...Marchenko focusing using convolutional neural networks Mert S. R. Kiraz, Roel Snieder Marchenko focusing using convolutional neural networks Mert...

A hybrid machine learning model for improving regression of mineral composition estimation using well logging data

Xiaojun Liu, Kezhen Hu, Stephen E. Grasby, Benjamin Lee

International Meeting for Applied Geoscience and Energy (IMAGE)

... classification problems. This ConvXGB architecture consists of a network with several stacked convolutional layers and XGBoost as the last layer of the model...

An integrated workflow for deep learning-accelerated seismic modelling of the Groningen gas field, the Netherlands

Haibin Di, Vanessa Simoes, Zhun Li, Cen Li, Anisha Kaul, Aria Abubakar

International Meeting for Applied Geoscience and Energy (IMAGE)

... for their model building. In this paper, we propose accelerating the process of seismic modeling on the Groningen gas field in the Netherlands by integrating...

Seismic inversion with dictionary learning using unsupervised machine learning

Debajeet Barman, Mrinal K. Sen

International Meeting for Applied Geoscience and Energy (IMAGE)

... (ML) has recently gained immense popularity in almost every field. This popularity is attributed to the invention of the Convolutional Neural Network...

Predicting horizons for salt body models using machine learning from neighboring seismic surveys: A case study from the northern Gulf of Mexico

Andrew Reisdorf, Dan Ferdinand Fernandez, Hugo Enrique Munoz Cuenca, Ryan King, David Manzano, Gavin Menzel-Jones

International Meeting for Applied Geoscience and Energy (IMAGE)

... and manual effort are required to provide horizons that are input into the earth model building process. The quality of these horizons determines...

Generative modeling for inverse problems

Rami Nammour

International Meeting for Applied Geoscience and Energy (IMAGE)

... by model errors (triangle, Q1) and data errors (box, Q2) for fully connected, convolutional and variational autoencoder models. Note that the success rate...

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)

... model building with a modified fully convolutional network: 88th Annual International Meeting, SEG, Expanded Abstracts, 2086–290, doi: https://doi.org...

Introduction to Special Issue: Geoscience Data Analytics and Machine Learning

Michael J. Pyrcz

AAPG Bulletin

... complicated, multivariate, spatiotemporal subsurface systems, and in predictive mode, to make predictions for cases not used to train the model...

Geophysics and neural networks: learning from computer vision

Mark Grujic, Liam Webb, Tom Carmichael

Petroleum Exploration Society of Australia (PESA)

... the ResNet-50 convolutional neural network to the GGMplus regional gravity model of Australia. This results in the quantitative characterisation of geophysical...

Efficient seismic image super-resolution

Adnan Hamida, Motaz Alfarraj, Abdullatif A. Al-Shuhail, Salam A. Zummo

International Meeting for Applied Geoscience and Energy (IMAGE)

... a GAN-based model with four convolutional layers for both the generator and discriminator. Fehler and Keliher (2011) SEAM Phase I synthetic dataset...

Unsupervised deep learning for seismic data reconstruction

Gui Chen, Yang Liu, Mi Zhang

International Meeting for Applied Geoscience and Energy (IMAGE)

... for reconstructing missing traces in observed seismic data. While many DL-based reconstruction methods employ convolutional neural networks (CNNs...

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: Shaping the Wavelet, by Wang, Yuchun E.; Huo, Shoudong; #90141 (2012)

Search and Discovery.com

Automatic low-order weak faults detection from carbonate reservoir based on deep learning and ant tracking

Han Wang, Xingwei Wu, Hanqing Wang, Jin Meng, Ji Chang, Tianrui Ye, Yujie Zhou, Dongwei Zhang, Yitian Xiao

International Meeting for Applied Geoscience and Energy (IMAGE)

... of the improved convolutional neural network (3D attention-based U-Net) for low-order fault detection. (a) The model structure. (b) The structure...

3D GPR data mel-frequency cepstral coefficients features for effective CNN classification of urban utilities

Jide Nosakare Ogunbo, Sang Hun Baek, Sang-Wook Kim

International Meeting for Applied Geoscience and Energy (IMAGE)

... misclassifications because of the nonuniqueness inherent in the restrictive geometrical extent. Therefore, the Convolutional Neural Network classification of 3D GPR...

Intelligent Prediction of Shale Oil Fracturing Curves Based on A Sequence-to-Sequence Model

Leyi Zheng, Tianbo Liang, Yunjin Wang, Fujian Zhou, Junlin Wu, Bin Wang, Jiaming Zhang, Maoqin Yang, Gong Chen, Xingyuan Liang

Unconventional Resources Technology Conference (URTEC)

... of critical events during fracturing. A novel sequence-tosequence prediction model (TCN-LSTM) is proposed that integrates a temporal convolutional...

High-resolution seismic reservoir monitoring with multitask and transfer learning

Ahmed M. Ahmed, Ilya Tsvankin, Yanhua Liu

International Meeting for Applied Geoscience and Energy (IMAGE)

... or hydrocarbon production. This study leverages convolutional neural networks (CNNs), multitask learning (MTL), and transfer learning (TL) to accurately...

Development of deep learning method for automatic seismic first break picking

Albert Farkhutdinov, Ruslan Malikov, Izat Shahsenov

International Meeting for Applied Geoscience and Energy (IMAGE)

... learning based techniques, such as support vector machines, convolutional image segmentation, and U-Net networks, have been studied for automatic FBP (Qu et...

Implementation of frequency-dependent fault identification by convolutional neural networks with uncertainty analysis

Fan Jiang, Alejandro Jaramillo, Steve Angelovich, Phill Norlund, Julianna Toms

International Meeting for Applied Geoscience and Energy (IMAGE)

...Implementation of frequency-dependent fault identification by convolutional neural networks with uncertainty analysis Fan Jiang, Alejandro Jaramillo...

InvMixer An efficient deep neural network for seismic inversion

Tianyi Zhang, Mauricio Araya-Polo, Anshumali Shrivastava

International Meeting for Applied Geoscience and Energy (IMAGE)

... layers in UNet use learnable kernels with of size 3×3 or 5×5 to model the relationships between traces. Since a single convolutional layer has limited...

An automatic velocity picking method based on object detection

Ce Bian, Weifeng Geng, Ping Yang, Pengyuan Sun, Guiren Xue, Haikun Lin

International Meeting for Applied Geoscience and Energy (IMAGE)

... an automatic velocity spectrum picking method based on object detection, and applies neural network model named FCOS (Fully Convolutional OneStage Object...

Abstract: 3-D Volumetric Interpretation with Computational Stratigraphy Models

Lisa Goggin, Tao Sun, Maisha Amaru, Ashley Harris, Anne Dutranois, Andrew Madof

Houston Geological Society Bulletin

... of a fluvially-dominated delta was created. The depositional model is converted into seismic volumes of various frequencies (1D convolutional approach...

Abstract: Hydrocarbon Generation and Expulsion Model for Upper Paleozoic Source Rocks in the Hangjinqi Area, Northern Ordos Basin, China;

Huijun Wang, Shuangfang Lu, Guiping Zhao

Search and Discovery.com

...Abstract: Hydrocarbon Generation and Expulsion Model for Upper Paleozoic Source Rocks in the Hangjinqi Area, Northern Ordos Basin, China; Huijun Wang...

Abstract: Missing Well-Log Data Prediction Using a Hybrid U-Net and LSTM Network Model; #91212 (2025)

Benard Sasu Oppong, Po Chen, En-Jui Lee, Wu-yu Liao

Search and Discovery.com

...Abstract: Missing Well-Log Data Prediction Using a Hybrid U-Net and LSTM Network Model; #91212 (2025) Benard Sasu Oppong, Po Chen, En-Jui Lee, Wu-yu...

GAN-based priors for Bayesian inference of subsurface geology at large scale

Kevin B. Daly, Tuan A. Tran, Brent D. Wheelock, Grant J. Seastream

International Meeting for Applied Geoscience and Energy (IMAGE)

... in the process stratigraphy simulations. One solution is to select a generative ML model with a fully convolutional architecture, where the latent coordinate...