Learning high-order spatial statistics at multiple scales: A kernel-based stochastic simulation algorithm and its implementation

This paper presents a learning-based stochastic simulation method that incorporates high-order spatial statistics at multiple scales from sources with different resolutions. Regarding the simulation of a certain spatial attribute, the high-order spatial information from different sources is encapsul...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Computers & geosciences Ročník 149; s. 104702
Hlavní autori: Yao, Lingqing, Dimitrakopoulos, Roussos, Gamache, Michel
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Ltd 01.04.2021
Predmet:
algorithms
computer software
data collection
Geostatistical simulation
gold
High-order simulation software
High-order spatial statistics
Kernel
probability distribution
reproduction
spatial data
Statistical learning
High-order spatial statistics
High-order simulation software
Geostatistical simulation
Statistical learning
Kernel
ISSN:0098-3004, 1873-7803
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:This paper presents a learning-based stochastic simulation method that incorporates high-order spatial statistics at multiple scales from sources with different resolutions. Regarding the simulation of a certain spatial attribute, the high-order spatial information from different sources is encapsulated as aggregated kernel statistics in a spatial Legendre moment kernel space, and the probability distribution of the underlying random field model is derived by a statistical learning algorithm, which matches the high-order spatial statistics of the target model to the observed ones. In addition, a related software is developed as the SGeMS plugin. Case studies are conducted with a known data set and a gold deposit, demonstrating reproduction of high-order spatial statistics from the available data, as well as practical aspects in mining applications. •Incorporating high-order spatial information at multiple scales based on kernel methods.•An extension of high-order simulation based on statistical learning.•Software plugin of learning-based high-order simulation.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0098-3004
1873-7803
DOI:10.1016/j.cageo.2021.104702