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)

..., and G. Hu, 2022, Ground truth-free 3-D seismic random noise attenuation via deep tensor convolutional neural networks in the time-frequency domain...

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...

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...

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

Carlos Montana and Gary Margrave

Search and Discovery.com

.... In contrast with the stationary convolutional model, which can be formulated in a simple way either in the time or the frequency domain...

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)

... convolutional neural networks and channel attention mechanisms. We use seismic data and low-frequency impedance data to generate inputs of training...

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)

... inversion. We add a low-frequency model to the network output, calculate the reflectivity r(t), and convolve it with the wavelet w(t) to generate...

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...

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...

RNN-based seismic velocity model building: Improving generalization using hybrid training data

Hani Alzahrani, Jeffrey Shragge

International Meeting for Applied Geoscience and Energy (IMAGE)

... the underlying velocity model and produces improved results compared to other frequency-domain network architectures, it still suffers from the common...

DL-fused elastic FWI: Application to marine streamer data

Pavel Plotnitskii, Oleg Ovcharenko, Vladimir Kazei, Daniel Peter, Tariq Alkhalifah

International Meeting for Applied Geoscience and Energy (IMAGE)

.... METHOD OVERVIEW The idea of this method is to directly map high-wavenumber model updates from time-domain FWI into low-wavenumber model updates (Figure...

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)

... parameters are shown in Table 1. Table 1 forward parameters Type parameters Wavelet Domain frequency Maximum record time Time sample rate Ricker...

Seismic simulations of experimental strata

Lincoln Pratson, Wences Gouveia

AAPG Bulletin

.... These reflection coefficients are then converted from the depth to the time domain using the model velocities. Note that the convolutional model is one...

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...

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...

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

Search and Discovery.com

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...

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...

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...

Enhancing seismic data quality: A machine learning approach to denoising and signal damage reduction

Mark Roberts, Olga Brusova, Leandro Gabioli, Alejandro Valenciano

International Meeting for Applied Geoscience and Energy (IMAGE)

... in the common shot domain and a self-supervised ML signal-add back model in the common channel domain. The supervised ML-based denoise (Brusova et al., 2021...

CNN for image super-resolution of airborne magnetic data in Ontario, Canada

Rafael Pires de Lima

International Meeting for Applied Geoscience and Energy (IMAGE)

... the two first convolutional blocks of a VGG model (Liu and Deng, 2015) pretrained on ImageNet (Russakovsky et al., 2015). RESULTS AND DISCUSSION We begin...

Retrieving high-resolution acoustic impedance using full-waveform inversion in presalt reservoir setting, offshore Brazil

Akshat Abhishek, Alok K. Soni, Rene-Edouard Plessix, Martijn Blaauw, Jaydip Guha, Gautam Kumar

International Meeting for Applied Geoscience and Energy (IMAGE)

... in the frequency domain, Part 1: Theory and verification in a physical scale model: Geophysics, 64, 659–992, doi: https://doi.org/10.1190/1.1444597. Rutherford...

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...

A Siamese network-based full-wave inversion: Application on real data

Omar M. Saad, Randy Harsuko, Tariq Alkhalifah

International Meeting for Applied Geoscience and Energy (IMAGE)

... two identical Convolutional Neural Networks (CNNs) with shared weights to ensure consistent feature extraction from observed and simulated data...

Seismic interpolation based on quadratic denoising neural network

Yuhan Sui, Xiaojing Wang, Jianwei Ma

International Meeting for Applied Geoscience and Energy (IMAGE)

.... Recently, a deep learning-based method is developed to achieve seismic interpolation by integrating the well-trained denoising convolutional neural...

Elastic-AdjointNet: A physics-guided deep autoencoder to overcome crosstalk effects in multiparameter full-waveform inversion

Arnab Dhara, Mrinal Sen

International Meeting for Applied Geoscience and Energy (IMAGE)

... of the decoder is fed into a convolutional layer which reduces the channel dimension to 1. The intermediate output is added to a starting model and given...