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...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Computers & geosciences Jg. 149; S. 104702
Hauptverfasser:	Yao, Lingqing, Dimitrakopoulos, Roussos, Gamache, Michel
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.04.2021
Schlagworte:	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
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!