Search Menu
DOE PAGES title logo U.S. Department of Energy
Office of Scientific and Technical Information
Search Scholarly Publications

Title: Making Invisible Visible: Data-Driven Seismic Inversion With Spatio-Temporally Constrained Data Augmentation

Journal Article · · IEEE Transactions on Geoscience and Remote Sensing
ORCiD logo [1]; ORCiD logo [2];  [3]; ORCiD logo [4]
  1. Los Alamos National Laboratory (LANL), Los Alamos, NM (United States); Kent State Univ., Kent, OH (United States)
  2. Los Alamos National Laboratory (LANL), Los Alamos, NM (United States); Michigan State Univ., East Lansing, MI (United States)
  3. Kent State Univ., Kent, OH (United States)
  4. Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
+ Show Author Affiliations

Deep learning and data-driven approaches have shown great potential in scientific domains. The promise of data-driven techniques relies on the availability of a large volume of high-quality training datasets. Due to the high cost of obtaining data through expensive physical experiments, instruments, and simulations, data augmentation techniques for scientific applications have emerged as a new direction for obtaining scientific data recently. However, existing data augmentation techniques originating from computer vision yield physically unacceptable data samples that are not helpful for the domain problems that we are interested in. In this article, we develop new data augmentation techniques based on convolutional neural networks. Specifically, our generative models leverage different physics knowledge (such as governing equations, observable perception, and physics phenomena) to improve the quality of the synthetic data. To validate the effectiveness of our data augmentation techniques, we apply them to solve a subsurface seismic full-waveform inversion using simulated CO2 leakage data. Our interest is to invert for subsurface velocity models associated with very small CO2 leakage. We validate the performance of our methods using comprehensive numerical tests. Here via comparison and analysis, we show that data-driven seismic imaging can be significantly enhanced by using our data augmentation techniques. Particularly, the imaging quality has been improved by 15% in test scenarios of general-sized leakage and 17% in small-sized leakage when using an augmented training set obtained with our techniques.

Research Organization:
Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
Sponsoring Organization:
USDOE National Nuclear Security Administration (NNSA); USDOE Laboratory Directed Research and Development (LDRD) Program
Grant/Contract Number:
89233218CNA000001
OSTI ID:
1998110
Report Number(s):
LA-UR--21-22294
Journal Information:
IEEE Transactions on Geoscience and Remote Sensing, Journal Name: IEEE Transactions on Geoscience and Remote Sensing Vol. 60; ISSN 0196-2892
Publisher:
IEEECopyright Statement
Country of Publication:
United States
Language:
English

References (41)

Pattern Recognition and Machine Learning book January 2006
A Survey on Deep Transfer Learning book January 2018
Carbon storage: the economic efficiency of storing CO 2 in leaky reservoirs journal October 2003
Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data journal April 2020
CO2 migration and pressure evolution in deep saline aquifers journal September 2015
A data-driven CO2 leakage detection using seismic data and spatial–temporal densely connected convolutional neural networks journal November 2019
Assessment of geophysical monitoring methods for detection of brine and CO2 leakage in drinking water aquifers journal November 2019
Downhole pressure and chemical monitoring for CO2 and brine leak detection in aquifers above a CO2 storage reservoir journal December 2019
Time-lapse gravity monitoring of CO2 migration based on numerical modeling of a faulted storage complex journal April 2020
Optimal design of microseismic monitoring network: Synthetic study for the Kimberlina CO2 storage demonstration site journal April 2020
Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations journal February 2019
Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data journal October 2019
Learning the parts of objects by non-negative matrix factorization journal October 1999
Physics-informed machine learning journal May 2021
Data augmentation for enhancing EEG-based emotion recognition with deep generative models journal October 2020
Artificial neural networks for solving ordinary and partial differential equations journal January 1998
Building Complex Seismic Velocity Models for Deep Learning Inversion journal January 2021
Data augmentation and feature extraction using variational autoencoder for acoustic modeling conference December 2017
Unsupervised domain adaptation for robust speech recognition via variational autoencoder-based data augmentation conference December 2017
Learning from Simulated and Unsupervised Images through Adversarial Training conference July 2017
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric conference June 2018
DADA: Deep Adversarial Data Augmentation for Extremely Low Data Regime Classification conference May 2019
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification conference December 2015
Data Augmentation via Latent Space Interpolation for Image Classification conference August 2018
Physics-Consistent Data-Driven Waveform Inversion With Adaptive Data Augmentation journal January 2022
InversionNet: An Efficient and Accurate Data-Driven Full Waveform Inversion journal January 2020
Data-Driven Seismic Waveform Inversion: A Study on the Robustness and Generalization journal October 2020
Image Quality Assessment: From Error Visibility to Structural Similarity journal April 2004
Optimizing deep learning hyper-parameters through an evolutionary algorithm
  • Young, Steven R.; Rose, Derek C.; Karnowski, Thomas P.
  • Proceedings of the Workshop on Machine Learning in High-Performance Computing Environments - MLHPC '15 https://doi.org/10.1145/2834892.2834896
conference January 2015
Towards Physics-informed Deep Learning for Turbulent Flow Prediction
  • Wang, Rui; Kashinath, Karthik; Mustafa, Mustafa
  • KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining https://doi.org/10.1145/3394486.3403198
conference August 2020
Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems journal November 2022
A survey on Image Data Augmentation for Deep Learning journal July 2019
Time‐lapse seismic reservoir monitoring journal January 2001
An overview of full-waveform inversion in exploration geophysics journal November 2009
Building realistic structure models to train convolutional neural networks for seismic structural interpretation journal January 2020
Deep-learning seismic full-waveform inversion for realistic structural models journal January 2021
Style transfer for generation of realistically textured subsurface models conference August 2019
Physically realistic training data construction for data-driven full-waveform inversion and traveltime tomography conference September 2020
Deep-learning tomography journal January 2018
Characterizing Construction of Existing Wells to a CO2 Storage Target: The Kimberlina Site, California dataset January 2017
RenderGAN: Generating Realistic Labeled Data journal June 2018

Similar Records

Related Subjects

58 GEOSCIENCES
Earth sciences
brain modeling
computational seismic imaging
computer science
data augmentation
data models
deep learning
full-waveform inversion (FWI)p physics
mathematical models
mathematics
neural networks
training