Massive focal mechanism solutions from deep learning in west Texas

Yangkang Chen, Omar M. Saad, Alexandros Savvaidis, Fangxue Zhang, Yunfeng Chen, Dino Huang, Huijian Li, Farzaneh Aziz Zanjani

International Meeting for Applied Geoscience and Energy (IMAGE)

... and simulation of high-frequency waveforms. In this work, we focus on the first-motion-based methods. Picking the P-wave first-motion polarities can...

Unsupervised frequency…space domain deep learning framework for reconstructing 5D seismic data

Gui Chen, Yang Liu, Haoran Zhang, Mi Zhang, Yuhang Sun

International Meeting for Applied Geoscience and Energy (IMAGE)

...Unsupervised frequency…space domain deep learning framework for reconstructing 5D seismic data Gui Chen, Yang Liu, Haoran Zhang, Mi Zhang, Yuhang Sun...

Abstracts: Full Waveform Inversion Using One-way Migration and Well Calibration; #90173 (2015)

Gary F. Margrave, Robert J. Ferguson, and Chad M. Hogan

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...: GEOPHYSICS, 44, 3-26. Pratt, R. G., 1999, Seismic waveform inversion in the frequency domain, Part I: Theory and verification in a physical scale model...

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

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

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... 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...

Seismic Meta-Attributes and the Illumination of the Internal Reservoir Architecture of a Deepwater Synthetic Channel Model, #41267 (2014)

Staffan Van Dyke, Renjun Wen

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...Seismic Meta-Attributes and the Illumination of the Internal Reservoir Architecture of a Deepwater Synthetic Channel Model, #41267 (2014) Staffan...

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

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

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...

3D CNN for channel identification in seismic volume

Haishan Li, Wuyang Yang, Xiangyang Zhang, Xinjian Wei, Xin Xu

International Meeting for Applied Geoscience and Energy (IMAGE)

... volumes with complex structure using an end-to-end 3D convolutional neural network. To train the network, we automatically generate a training dataset...

Abstract: Recovering Low Frequencies for Impedance Inversion by Frequency Domain Deconvolution; #90224 (2015)

Sina Esmaeili and Gary Frank

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...Abstract: Recovering Low Frequencies for Impedance Inversion by Frequency Domain Deconvolution; #90224 (2015) Sina Esmaeili and Gary Frank Datapages...

3D velocity model building based upon hybrid neural network

Herurisa Rusmanugroho, Junxiao Li, M. Daniel Davis Muhammed, Jian Sun

International Meeting for Applied Geoscience and Energy (IMAGE)

... as an image are passed through some convolutional layers to estimate P-velocity model. This network is expected to learn from the features obtained from...

Deep water OBN multiple prediction from local reflectivity in the Stolt domain

Cesar Ricardez

International Meeting for Applied Geoscience and Energy (IMAGE)

...)] (2) This multiple model is then directly subtracted in the (x,y,t) domain or could be adapted using traditional L2 energy minimization techniques...

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

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Bringing ML models into mainstream applications by enabling cloud platform connections

Rafael Pinto, Ilya Agurov, Roman Emreis, Iurii Koniaev-Gurchenko, Dmitrii Zolotukhin, Viktar Huleu, Evgeny Shulikin, Andrey Derevyanka, Pavel Shashkin, Maksim Krug, Ivan Grechikhin, Anton Petrov, Simon Shaw, Brian Macy, Chengbo Li, Chuck Mosher, Anand Malgi

International Meeting for Applied Geoscience and Energy (IMAGE)

...) to calculate the zero-offset reflectivity. Then, we converted the result to the time domain using the P-wave velocity model and applied an antialias...

Self-parametrizing seismic data processing modules: An example on coherent noise suppression

Simone Re, Ran Bachrach, Massimo Clementi, Collin Wilson

International Meeting for Applied Geoscience and Energy (IMAGE)

... in the frequency-offset (FX) domain (Bilsby, 2015) thus enabling its application to uneven and irregular seismic acquisition layouts (i.e., non-uniform...

Abstract: Fault System Delineation Driven by New Technology in Tazhong Karsted Carbonate Reservoirs; #91204 (2023)

Yanming Tong, Xingliang Deng, Chuan Wu, Shiti Cui, Pin Yang, Chunguang Shen, Gaige Wang, Jiangyong Wu, Chenqing Tan

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..., i.e. Radon domain signal and resolution enhancement, Time-domain amplitude balancing and Frequency-domain lowfrequency balancing. From the raw...

Noise analysis and ML denoising of DAS VSP data acquired from ESP lifted wells

Ge Zhan, Yao Zhao, Cheng Cheng, Josef Heim, Weihong Fei, Mike Craven, Scott Baker, Gilles Hennenfent

International Meeting for Applied Geoscience and Energy (IMAGE)

... developed a machine learning (ML) workflow that uses a deep convolutional U-Net architecture to model the ESP noise first and then subtract it from...

Conditional image prior for uncertainty quantification in full-waveform inversion

Lingyun Yang, Omar M. Saad, Tariq Alkhalifah, Guochen Wu

International Meeting for Applied Geoscience and Energy (IMAGE)

... data. However, FWI results are effected by the limited illumination of the model domain and the quality of that illumination, which is related...

Representation Learning in Seismic Interpretation

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Deep learning seismic full-waveform inversion and transient EM joint inversion for near surface velocity modeling

Daniele Colombo, Ernesto Sandoval-Curiel, Ersan Turkoglu, Weichang Li

International Meeting for Applied Geoscience and Energy (IMAGE)

...rg/10.1111/j.1365-246X.1989.tb00521.x. Brossier, R., S. Operto, and J. Virieux, 2009, Seismic imaging of complex onshore structures by 2D elastic frequency-domain fu...

Abstracts: Application of Neural Network Analysis and Post-Stack Inversion - Case Studies in Alberta; #90173 (2015)

Somanath Misra and Satinder Chopra

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... the P-impedance from the post-stack data by way of model based inversion as well as neural network analysis. We are showing comparisons of the results...

Equivariant imaging for self-supervised regularly undersampled seismic data interpolation

Weiwei Xu, Vincenzo Lipari, Paolo Bestagini, Politecnico di Milano, Wenchao Chen, Stefano Tubaro

International Meeting for Applied Geoscience and Energy (IMAGE)

... of complex field conditions and economic circumstance, seismic data is usually undersampled in the spatial domain, which needs to be interpolated to meet...

Validating machine learning-based seismic property prediction through self-supervised seismic reconstruction

Tao Zhao, Haibin Di, Aria Abubakar

International Meeting for Applied Geoscience and Energy (IMAGE)

... inversion, one uses impedance from well logs to build a low-frequency initial model, then computes the misfit between measured and modeled seismic data...

Facies-constrained elastic full-waveform inversion for tilted orthorhombic media

Ashish Kumar, Ilya Tsvankin

International Meeting for Applied Geoscience and Energy (IMAGE)

... offset is 3.6 km and maximum offsetto-depth ratio for the bottom of the model is about 2.6. The Ricker wavelet with a central frequency of 10 Hz...

Deep learning velocity model building using an ensemble regression approach

Stuart Farris, Guillaume Barnier, Robert Clapp

International Meeting for Applied Geoscience and Energy (IMAGE)

... framework that uses a convolutional neural network (CNN) to form an ensemble of low wavenumber model predictions which can be integrated to form...

Correlating Versus Inverting Vibroseis Records: Recovering What You Put into the Ground; #41577 (2015)

Glen Larsen, Paul Hewitt, Art Siewert

Search and Discovery.com

...) based on work of Allen et al. (1998). In effect, spiking the trace reduces it to a phase only operator. The usual vibroseis convolutional model is: x...