Microseismic Source Imaging Using Physics-Informed Neural Networks With Hard Constraints

Huang, Xinquan; Alkhalifah, Tariq A.

doi:10.1109/tgrs.2024.3366449

IEEE Trans. Geosci. Remote Sensing

2024

DOI: 10.1109/tgrs.2024.3366449

|View full text |Cite

Microseismic Source Imaging Using Physics-Informed Neural Networks With Hard Constraints

Xinquan Huang,

Tariq A. Alkhalifah

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...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Review of physics-informed machine-learning inversion of geophysical data

Schuster,

Chen,

Feng

2024

GEOPHYSICS

View full text Add to dashboard Cite

We review five types of physics-informed machine learning (PIML) algorithms for inversion and modeling of geophysical data. Such algorithms use the combination of a data-driven machine learning (ML) method and the equations of physics to model and/or invert geophysical data. By incorporating the constraints of physics, PIML algorithms can effectively reduce the size of the solution space for machine learning models, enabling them to be trained on smaller datasets. This is especially advantageous in scenarios where data availability may be limited or expensive to obtain.In this review we restrict the {\it physics} to be that from the governing wave equation, either as a constraint that must be satisfied or by using numerical solutions to the wave equation for both modeling and inversion.#xD;This approach ensures that the resulting models adhere to physical principles while leveraging the power of machine learning to analyze and interpret complex geophysical data.#xD;The key challenge with PIML methods is to strike a balance between computational efficiency and predictive accuracy. While PIML algorithms may offer computational advantages over standard numerical methods, their accuracy must justify the trade-off. In cases where PIML models are efficient but somewhat inaccurate, they can still serve valuable purposes, such as providing initial velocity models for more accurate techniques like Full Waveform Inversion (FWI).

show abstract

Review of physics-informed machine-learning inversion of geophysical data

Schuster,

Chen,

Feng

2024

GEOPHYSICS

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.

Microseismic Source Imaging Using Physics-Informed Neural Networks With Hard Constraints

Cited by 1 publication

References 43 publications

Review of physics-informed machine-learning inversion of geophysical data

Review of physics-informed machine-learning inversion of geophysical data

Contact Info

Product

Resources

About