Learned Regularizations for Multi‐Parameter Elastic Full Waveform Inversion Using Diffusion Models
Abstract Elastic full waveform inversion (EFWI) promises to account for the Earth's elastic nature and corresponding reflectivity, which is often disregarded in the commonly used acoustic FWI. However, EFWI usually requires a more sophisticated recording apparatus (beyond the usual single‐compo...
Saved in:
| Main Authors: | Mohammad H. Taufik, Fu Wang, Tariq Alkhalifah |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-03-01
|
| Series: | Journal of Geophysical Research: Machine Learning and Computation |
| Subjects: | |
| Online Access: | https://doi.org/10.1029/2024JH000125 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Daytime Equatorial Spread F‐Like Irregularities Detected by HF Doppler Receiver and Digisonde
by: B. Olugbon, et al.
Published: (2021-04-01) -
Why Do Small‐Scale Irregularities Migrate Beyond the Equatorial Plasma Bubble?
by: Yuanlin Jia, et al.
Published: (2025-07-01) -
Predicting Equatorial Spread F at JICAMARCA Sector Via Supervised Machine Learning
by: Shunzu Gao, et al.
Published: (2025-03-01) -
Using ICON Satellite Data to Forecast Equatorial Ionospheric Instability Throughout 2022
by: D. L. Hysell, et al.
Published: (2024-03-01) -
Predicting Equatorial Ionospheric Convective Instability Using Machine Learning
by: D. Garcia, et al.
Published: (2023-12-01)