Deep generative networks for multivariate fullstack seismic data inversion using inverse autoregressive flows

Miele, Roberto; Levy, Shiran; Linde, Niklas; Soares, Amilcar; Azevedo, Leonardo

doi:10.1016/j.cageo.2024.105622

Computers & Geosciences

2024

DOI: 10.1016/j.cageo.2024.105622

|View full text |Cite

Deep generative networks for multivariate fullstack seismic data inversion using inverse autoregressive flows

Roberto Miele,

Shiran Levy,

Niklas Linde

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

2025

Publication Types

Select...

Article2

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Discriminator-based stratigraphic sequence semantic augmentation seismic facies analysis

Wang,

Yan,

Sun

et al. 2025

Computers & Geosciences

View full text Add to dashboard Cite

Discriminator-based stratigraphic sequence semantic augmentation seismic facies analysis

Wang,

Yan,

Sun

et al. 2025

Computers & Geosciences

View full text Add to dashboard Cite

When geoscience meets generative AI and large language models: Foundations, trends, and future challenges

Hadid,

Chakraborty,

Busby

2024

Expert Systems

View full text Add to dashboard Cite

Generative Artificial Intelligence (GAI) represents an emerging field that promises the creation of synthetic data and outputs in different modalities. GAI has recently shown impressive results across a large spectrum of applications ranging from biology, medicine, education, legislation, computer science, and finance. As one strives for enhanced safety, efficiency, and sustainability, generative AI indeed emerges as a key differentiator and promises a paradigm shift in the field. This article explores the potential applications of generative AI and large language models in geoscience. The recent developments in the field of machine learning and deep learning have enabled the generative model's utility for tackling diverse prediction problems, simulation, and multi‐criteria decision‐making challenges related to geoscience and Earth system dynamics. This survey discusses several GAI models that have been used in geoscience comprising generative adversarial networks (GANs), physics‐informed neural networks (PINNs), and generative pre‐trained transformer (GPT)‐based structures. These tools have helped the geoscience community in several applications, including (but not limited to) data generation/augmentation, super‐resolution, panchromatic sharpening, haze removal, restoration, and land surface changing. Some challenges still remain, such as ensuring physical interpretation, nefarious use cases, and trustworthiness. Beyond that, GAI models show promises to the geoscience community, especially with the support to climate change, urban science, atmospheric science, marine science, and planetary science through their extraordinary ability to data‐driven modelling and uncertainty quantification.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Deep generative networks for multivariate fullstack seismic data inversion using inverse autoregressive flows

Cited by 2 publications

References 45 publications

Discriminator-based stratigraphic sequence semantic augmentation seismic facies analysis

Discriminator-based stratigraphic sequence semantic augmentation seismic facies analysis

When geoscience meets generative AI and large language models: Foundations, trends, and future challenges

Contact Info

Product

Resources

About