Deep Learning for Seismic Inverse Problems: Toward the Acceleration of Geophysical Analysis Workflows

Seismic inversion is a fundamental tool in geophysical analysis, providing a window into Earth. In particular, it enables the reconstruction of large-scale subsurface Earth models for hydrocarbon exploration, mining, earthquake analysis, shallow hazard assessment, and other geophysical tasks.

[1]  Frank J. Goulding,et al.  Benchmarking exploration predictions and performance using 20+ yr of drilling results: One company’s experience , 2017 .

[2]  A. Tarantola.  Inversion of seismic reflection data in the acoustic approximation , 1984 .

[3]  Fangyu Li,et al.  Seismic spectral decomposition using deconvolutive short-time Fourier transform spectrogram , 2013 .

[4]  Rebecca Latimer,et al.  An interpreter's guide to understanding and working with seismic-derived acoustic impedance data , 2000 .

[5]  Tapan Mukerji,et al.  Convolutional neural network for seismic impedance inversion , 2018, GEOPHYSICS.

[6]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[7]  Dong Liang,et al.  Deep Magnetic Resonance Image Reconstruction: Inverse Problems Meet Neural Networks , 2020, IEEE Signal Processing Magazine.

[8]  Gabriel Fabien-Ouellet,et al.  Seismic velocity estimation: A deep recurrent neural-network approach , 2020, GEOPHYSICS.

[9]  Peng Jiang,et al.  Deep-Learning Inversion of Seismic Data , 2019, IEEE Transactions on Geoscience and Remote Sensing.

[10]  Aaron C. Courville,et al.  Improved Training of Wasserstein GANs , 2017, NIPS.

[11]  Tariq Alkhalifah,et al.  Deep learning for low-frequency extrapolation from multioffset seismic data , 2019, GEOPHYSICS.

[12]  Tom White,et al.  Generative Adversarial Networks: An Overview , 2017, IEEE Signal Processing Magazine.

[13]  Laurent Demanet,et al.  Extrapolated full-waveform inversion with deep learning , 2019, GEOPHYSICS.

[14]  Yuji Kim,et al.  Geophysical inversion versus machine learning in inverse problems , 2018, The Leading Edge.

[15]  Ghassan Al-Regib,et al.  Petrophysical property estimation from seismic data using recurrent neural networks , 2018, SEG Technical Program Expanded Abstracts 2018.

[16]  Martin J. Blunt,et al.  Stochastic Seismic Waveform Inversion Using Generative Adversarial Networks as a Geological Prior , 2018, Mathematical Geosciences.

[17]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[18]  Mauricio D. Sacchi,et al.  Automatic velocity analysis using convolutional neural network and transfer learning , 2020 .

[19]  Xinfei Yan,et al.  Seismic impedance inversion based on cycle-consistent generative adversarial network , 2019, SEG Technical Program Expanded Abstracts 2019.

[20]  Jianwei Ma,et al.  Deep-learning inversion: a next generation seismic velocity-model building method , 2019, GEOPHYSICS.

[21]  Zhou Wang,et al.  Multiscale structural similarity for image quality assessment , 2003, The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003.

[22]  Aggelos K. Katsaggelos,et al.  Using Deep Neural Networks for Inverse Problems in Imaging: Beyond Analytical Methods , 2018, IEEE Signal Processing Magazine.

[23]  Jean Virieux,et al.  An overview of full-waveform inversion in exploration geophysics , 2009 .

[24]  Ying Wang,et al.  Seismic Impedance Inversion Using Fully Convolutional Residual Network and Transfer Learning , 2020, IEEE Geoscience and Remote Sensing Letters.

[25]  Pietro Perona,et al.  Microsoft COCO: Common Objects in Context , 2014, ECCV.

[26]  L. Duque,et al.  Automated Velocity Estimation by Deep Learning Based Seismic-to-Velocity Mapping , 2019 .

[27]  Pascal Vincent,et al.  Representation Learning: A Review and New Perspectives , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[28]  Zhongping Zhang,et al.  Data-Driven Seismic Waveform Inversion: A Study on the Robustness and Generalization , 2018, IEEE Transactions on Geoscience and Remote Sensing.

[29]  Kurt M. Strack,et al.  Society of Exploration Geophysicists , 2007 .

[30]  Jürgen Schmidhuber,et al.  Stacked Convolutional Auto-Encoders for Hierarchical Feature Extraction , 2011, ICANN.

[31]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[32]  D. L. Anderson,et al.  Preliminary reference earth model , 1981 .

[33]  Jianwei Ma,et al.  Velocity model building in a crosswell acquisition geometry with image-trained artificial neural networks , 2020 .

[34]  Pan He,et al.  Adversarial Examples: Attacks and Defenses for Deep Learning , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[35]  Hua-wei Zhou,et al.  Practical Seismic Data Analysis , 2014 .

[36]  Gary Gibson,et al.  An introduction to seismology , 1996, Inf. Manag. Comput. Secur..

[37]  Xiaoou Tang,et al.  Image Super-Resolution Using Deep Convolutional Networks , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[38]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[39]  M. Becquey,et al.  Acoustic impedance logs computed from seismic traces , 1979 .

[40]  Alan Richardson,et al.  Seismic Full-Waveform Inversion Using Deep Learning Tools and Techniques , 2018, 1801.07232.

[41]  C. Bunks,et al.  Multiscale seismic waveform inversion , 1995 .

[42]  Mauricio Araya-Polo,et al.  Deep learning-driven velocity model building workflow , 2019, The Leading Edge.

[43]  B. Mao,et al.  Subsurface velocity inversion from deep learning-based data assimilation , 2019, Journal of Applied Geophysics.

[44]  Jianwei Ma,et al.  Velocity model building with a modified fully convolutional network , 2018, SEG Technical Program Expanded Abstracts 2018.

[45]  Youzuo Lin,et al.  Inversionet: Accurate and efficient seismic-waveform inversion with convolutional neural networks , 2018, SEG Technical Program Expanded Abstracts 2018.

[46]  Jean Virieux,et al.  Challenges in the Full Waveform Inversion Regarding Data, Model and Optimisation , 2012 .

[47]  Yunzhi Shi,et al.  Applications of supervised deep learning for seismic interpretation and inversion , 2019, The Leading Edge.

[48]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[49]  Cooperative deep learning inversion: Seismic-constrained CSEM inversion for salt delineation , 2019, SEG Technical Program Expanded Abstracts 2019.

[50]  Armin Iske,et al.  Mathematical Methods and Modelling in Hydrocarbon Exploration and Production , 2005 .

[51]  Aren Jansen,et al.  CNN architectures for large-scale audio classification , 2016, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[52]  Denes Vigh,et al.  Deep learning prior models from seismic images for full-waveform inversion , 2017 .

[53]  Carola-Bibiane Schönlieb,et al.  Adversarial Regularizers in Inverse Problems , 2018, NeurIPS.

[54]  Seismic inversion: What it is, and what it is not , 2016 .

[55]  Mauricio Hanzich,et al.  Assessing Accelerator-Based HPC Reverse Time Migration , 2011, IEEE Transactions on Parallel and Distributed Systems.

[56]  Martin Burger,et al.  Modern regularization methods for inverse problems , 2018, Acta Numerica.

[57]  Tapan Mukerji,et al.  Prestack and poststack inversion using a physics-guided convolutional neural network , 2019, Interpretation.

[58]  Amir Adler,et al.  Deep Recurrent Architectures for Seismic Tomography , 2019, 81st EAGE Conference and Exhibition 2019.

[59]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[60]  Ghassan AlRegib,et al.  Semi-supervised learning for acoustic impedance inversion , 2019, SEG Technical Program Expanded Abstracts 2019.

[61]  S. Brandsberg-Dahl,et al.  The 2004 BP Velocity Benchmark , 2005 .

[62]  Amir Adler,et al.  Deep-learning tomography , 2018 .