“Behind-the-outcrop” outcrop: A creative machine learning application for ground-penetrating radar data visualization as an artificial photorealistic rock volume

Ramdani, Ahmad; Perbawa, Andika; Puspita, Ingrid; Vahrenkamp, Volker

doi:10.1190/image2022-3745682.1

Second International Meeting for Applied Geoscience &Amp; Energy 2022

DOI: 10.1190/image2022-3745682.1

|View full text |Cite

“Behind-the-outcrop” outcrop: A creative machine learning application for ground-penetrating radar data visualization as an artificial photorealistic rock volume

Ahmad Ramdani

Andika Perbawa

Ingrid Puspita

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Other1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Visualizing Geoscience Data Interpretation Into Photorealistic Modern Analog Using Generative AI: A Preliminary Result From Carbonate Platform Environment

Ramdani,

Perbawa,

Vahrenkamp

2024

All Days

View full text Add to dashboard Cite

Geoscience datasets are fundamental for subsurface investigation. Paradoxically, they are sometimes exclusive and require subject-specific expertise to interpret and visualize. One such example is seismic interpretation. Geophysicists typically reconstruct ancient depositional settings by interpreting a myriad of seismic attributes and drawing analogs to the sedimentary process of the modern depositional environment (Posamentier et al. 2007; Vahrenkamp et al. 2019; Ramdani et al. 2021). Most of these interpretations will likely be reflection amplitude, frequency, impedance, or other geophysical attributes interpreted and "visualized" in the present-day geomorphology context (Posamentier et al. 2007; Warrlich et al. 2019; Ramdani et al. 2022b). The interpreter will then rely on verbal or written descriptions to convey their interpretation. Often, these descriptions are only well understood by fellow interpreters. Attempting to convey the same interpretation to a non-expert requires some degree of visual aid. Thus, a method to picture geophysical signals as a "depositional environment" is needed to bridge this gap. This study aims to leverage the application of generative AI as a tool for seismic interpretation. We propose a Conditional Generative Adversarial Network (CGAN)-based methodology capable of converting seismic attribute maps into photorealistic images of the modern satellite imagery analog as a visual aid for seismic interpretation.

show abstract

Visualizing Geoscience Data Interpretation Into Photorealistic Modern Analog Using Generative AI: A Preliminary Result From Carbonate Platform Environment

Ramdani,

Perbawa,

Vahrenkamp

2024

All Days

View full text Add to dashboard Cite

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.

“Behind-the-outcrop” outcrop: A creative machine learning application for ground-penetrating radar data visualization as an artificial photorealistic rock volume

Cited by 1 publication

References 10 publications

Visualizing Geoscience Data Interpretation Into Photorealistic Modern Analog Using Generative AI: A Preliminary Result From Carbonate Platform Environment

Visualizing Geoscience Data Interpretation Into Photorealistic Modern Analog Using Generative AI: A Preliminary Result From Carbonate Platform Environment

Contact Info

Product

Resources

About