Physics-informed deep learning quantifies propagated uncertainty in seismic structure and hypocenter determination
Abstract Subsurface seismic velocity structure is essential for earthquake source studies, including hypocenter determination. Conventional hypocenter determination methods ignore the inherent uncertainty in seismic velocity structure models, and the impact of this oversight has not been thoroughly...
Saved in:
Main Authors: | Ryoichiro Agata, Kazuya Shiraishi, Gou Fujie |
---|---|
Format: | Article |
Language: | English |
Published: |
Nature Portfolio
2025-01-01
|
Series: | Scientific Reports |
Online Access: | https://doi.org/10.1038/s41598-024-84995-9 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Quantifying uncertainty in the estimation of probability distributions
by: H.T. Banks, et al.
Published: (2008-09-01) -
Quantifying Uncertainty of Insurance Claims Based on Expert Judgments
by: Budhi Handoko, et al.
Published: (2025-01-01) -
Towards Quantifying the Uncertainty in Estimating Observed Scaling Rates
by: Haider Ali, et al.
Published: (2022-06-01) -
Quantifying Uncertainty in Laser-Induced Damage Threshold for Cylindrical Gratings
by: Yuan Li, et al.
Published: (2024-12-01) -
Quantifying uncertainty in anthropogenic causes of injury and mortality for an endangered baleen whale
by: Daniel W. Linden, et al.
Published: (2024-12-01)