In recent years, Earth system sciences are urgently calling for innovation on improving
accuracy, enhancing model intelligence level, scaling up operation, and reducing costs in …

Abstract Machine learning algorithms have been applied widely in the fields of natural
science, social science and engineering. It can be expected that machine learning …

Finite mixture models are being used increasingly to model a wide variety of random
phenomena for clustering, classification and density estimation. mclust is a powerful and …

Few books on statistical data analysis in the natural sciences are written at a level that a non-
statistician will easily understand. This is a book written in colloquial language, avoiding …

In this paper, in order to reveal the regional geochemical patterns of regularly sampled
stream sediment data, we have employed the K-means and self-organizing map (SOM) as …

There is lack of research and documentation of actual (as opposed to theoretical) benefits
(eg, mineral deposit discovery) of developments in compositional data analysis and …

A comprehensive study was conducted to identify the salinization origins and the major
hydrogeochemical processes controlling the salinization and deterioration of the Gaza …

Integration of stream sediment geochemical data with other types of mineral exploration
data, especially in knowledge-driven mineral potential mapping (MPM), is a challenging …

The understanding of sources and controlling factors of potentially toxic elements (PTEs) in
soils plays an important role in the improvement of environmental management. With the …

Stream sediment geochemical data are usually subjected to methods of multivariate
analysis (eg factor analysis) in order to extract an anomalous geochemical signature (factor) …