[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) …
[HTML][HTML] Model averaging for identification of geochemical anomalies linked to mineralization
The complexity of geochemical patterns in the surficial media makes it necessary to consider
the uncertainty in the process of identifying geochemical anomalies. The existence of …
the uncertainty in the process of identifying geochemical anomalies. The existence of …
Mineral prospectivity mapping via gated recurrent unit model
The application of deep learning algorithms in mineral prospectivity mapping (MPM) is a hot
topic in mineral exploration. However, few studies have focused on recurrent neural …
topic in mineral exploration. However, few studies have focused on recurrent neural …
Three-dimensional mineral prospectivity mapping by XGBoost modeling: A case study of the Lannigou gold deposit, China
Q Zhang, J Chen, H Xu, Y Jia, X Chen, Z Jia… - Natural Resources …, 2022 - Springer
Three-dimensional mineral prospectivity mapping (3DMPM) aims to explore deep mineral
resources and many methods have been developed for this task in recent years. The …
resources and many methods have been developed for this task in recent years. The …
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 …
Geochemical anomaly identification and uncertainty quantification using a Bayesian convolutional neural network model
Geochemical prospecting plays an important role in mineral exploration. In recent years,
deep learning algorithms (DLAs) have been applied in mapping geochemical anomalies …
deep learning algorithms (DLAs) have been applied in mapping geochemical anomalies …
Developments in quantitative assessment and modeling of mineral resource potential: an overview
Y Liu, EJM Carranza, Q Xia - Natural Resources Research, 2022 - Springer
The special issue entitled “Developments in Quantitative Assessment and Modeling of
Mineral Resource Potential” is composed of 17 papers that cover a diverse range of …
Mineral Resource Potential” is composed of 17 papers that cover a diverse range of …
Visual interpretable deep learning algorithm for geochemical anomaly recognition
Deep learning algorithms (DLAs) have achieved better results than traditional methods in
the field of multivariate geochemical anomaly recognition because of their strong ability to …
the field of multivariate geochemical anomaly recognition because of their strong ability to …
Geochemical survey data cube: A useful tool for lithological classification and geochemical anomaly identification
Geochemical survey data play a critical role in geological studies, mineral exploration, and
environmental applications by providing information on geological events and processes …
environmental applications by providing information on geological events and processes …
Handling Dataset with Geophysical and Geological Variables on the Bolivian Andes by the GMT Scripts
P Lemenkova - Data, 2022 - mdpi.com
In this paper, an integrated mapping of the georeferenced data is presented using the QGIS
and GMT scripting tool set. The study area encompasses the Bolivian Andes, South …
and GMT scripting tool set. The study area encompasses the Bolivian Andes, South …