Characterization of Spatially Heterogeneous Environmental Variables Through Multi‐Modal Generalized Sub‐Gaussian Distributions
Abstract We provide a sound theoretical framework for the characterization of randomly heterogeneous spatial fields exhibiting multi‐modal, long‐tailed probability densities. Multi‐modal distributions are at the core of conceptual models employed to represent heterogeneity of hydrogeological or geoc...
Saved in:
| Main Authors: | Chiara Recalcati, Alberto Guadagnini, Monica Riva |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-03-01
|
| Series: | Water Resources Research |
| Subjects: | |
| Online Access: | https://doi.org/10.1029/2024WR038487 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Non-Gaussian Process Dynamical Models
by: Yaman Kindap, et al.
Published: (2025-01-01) -
FREQUENCY DOMAIN DAMAGE CALCULATION AND EXPERIMENTAL VERIFICATION OF NON-GAUSSIAN EXCITATION BASED ON GMM MODEL (MT)
by: XU Yang, et al.
Published: (2023-01-01) -
COMPARISON OF K-MEANS AND GAUSSIAN MIXTURE MODEL IN PROFILING AREAS BY POVERTY INDICATORS
by: Zumrotul Wahidah, et al.
Published: (2023-06-01) -
STUDY OF MAGNETOSTRICTIVE PROPERTIES OF MATERIALS BY MEANS OF METHOD OF ATOMIC FORCE MICROSCOPY
by: D. A. Stepanenko, et al.
Published: (2015-03-01) -
Generating Multi-Codebook Neural Network by Using Intelligent Gaussian Mixture Model Clustering Based on Histogram Information for Multi-Modal Data Classification
by: M. Anwar Ma'Sum, et al.
Published: (2024-01-01)