Knowledge-Driven Adaptive Direct Sampling for Reconstructing Geochemical Fields Under Sampling Bias

Deriving meaningful mineralization information from raw geospatial datasets is fundamental to the sustainable evaluation and management of mineral resources. As a cornerstone of mineral resource evaluation, identifying geochemical anomalies often faces the significant challenge of sampling bias in p...

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Detalhes bibliográficos

Principais autores:	Yameng Liu, Jiali Zi, Yanqi Dong, Nuo Xu, Qing Zhang, Feixiang Chen
Formato:	Artigo
Idioma:	Inglês
Publicado em:	MDPI AG 2026-03-01
coleção:	ISPRS International Journal of Geo-Information
Assuntos:	spatial sampling bias geochemical anomaly spatial structure reconstruction knowledge-driven modeling adaptive direct sampling sustainable mineral exploration
Acesso em linha:	https://www.mdpi.com/2220-9964/15/3/111
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