[HTML][HTML] Mapping prospectivity for regolith-hosted REE deposits via convolutional neural network with generative adversarial network augmented data
The regolith-hosted rare earth elements (REE) deposits are the dominant source of the
global heavy REE resources. This study proposed a convolutional neural network (CNN) …
global heavy REE resources. This study proposed a convolutional neural network (CNN) …
Development and applications of GIS-based spatial analysis in environmental geochemistry in the big data era
The research of environmental geochemistry entered the big data era. Environmental big
data is a kind of new method and thought, which brings both opportunities and challenges to …
data is a kind of new method and thought, which brings both opportunities and challenges to …
A physically constrained hybrid deep learning model to mine a geochemical data cube in support of mineral exploration
Geochemical survey data provide rich information on geochemical elemental concentrations
and their spatial patterns in relation to mineralization or pollution. A geochemical data cube …
and their spatial patterns in relation to mineralization or pollution. A geochemical data cube …
Combination of machine learning algorithms with concentration-area fractal method for soil geochemical anomaly detection in sediment-hosted Irankuh Pb-Zn deposit …
Prediction of geochemical concentration values is essential in mineral exploration as it plays
a principal role in the economic section. In this paper, four regression machine learning (ML) …
a principal role in the economic section. In this paper, four regression machine learning (ML) …
A geologically-constrained deep learning algorithm for recognizing geochemical anomalies
The effective identification of geochemical anomalies is essential in mineral exploration.
Recently, data-driven deep learning algorithms have gained popularity for recognizing the …
Recently, data-driven deep learning algorithms have gained popularity for recognizing the …
Applications of data augmentation in mineral prospectivity prediction based on convolutional neural networks
N Yang, Z Zhang, J Yang, Z Hong - Computers & geosciences, 2022 - Elsevier
The supervised deep learning methods applied in mineral prospectivity mapping usually
need sufficient samples for training models. However, mineralization is a rare event …
need sufficient samples for training models. However, mineralization is a rare event …
Visualization and interpretation of geochemical exploration data using GIS and machine learning methods
Geochemical exploration has provided significant clues for mineral exploration and has
helped discover many mineral deposits. Although various methods, including classic …
helped discover many mineral deposits. Although various methods, including classic …
A physically constrained variational autoencoder for geochemical pattern recognition
Quantification and recognition of geochemical patterns are extremely important for
geochemical prospecting and can facilitate a better understanding of regional …
geochemical prospecting and can facilitate a better understanding of regional …
Metallogenic-factor variational autoencoder for geochemical anomaly detection by ad-hoc and post-hoc interpretability algorithms
Deep learning algorithms (DLAs) are becoming hot tools in processing geochemical survey
data for mineral exploration. However, it is difficult to understand their working mechanisms …
data for mineral exploration. However, it is difficult to understand their working mechanisms …
The graph attention network and its post-hoc explanation for recognizing mineralization-related geochemical anomalies
Deep learning algorithms have become a cutting-edge technology for mining geochemical
survey data to identify geochemical patterns related to mineralization. Similarities in the …
survey data to identify geochemical patterns related to mineralization. Similarities in the …