Machine learning for anomaly detection: A systematic review

AB Nassif, MA Talib, Q Nasir, FM Dakalbab - Ieee Access, 2021 - ieeexplore.ieee.org
Anomaly detection has been used for decades to identify and extract anomalous
components from data. Many techniques have been used to detect anomalies. One of the …

[HTML][HTML] GIS-based mineral prospectivity map** using machine learning methods: A case study from Tongling ore district, eastern China

T Sun, F Chen, L Zhong, W Liu, Y Wang - Ore Geology Reviews, 2019 - Elsevier
Predictive modelling of mineral prospectivity using GIS is a valid and progressively more
accepted tool for delineating reproducible mineral exploration targets. In this study, machine …

The processing methods of geochemical exploration data: past, present, and future

R Zuo, J Wang, Y **
J Yin, N Li - Ore geology reviews, 2022 - Elsevier
Abstract Machine learning algorithms have been widely applied in mineral prospectivity
map** (MPM). In this study, we implemented ensemble learning of extreme gradient …

Artificial intelligence for mineral exploration: A review and perspectives on future directions from data science

F Yang, R Zuo, OP Kreuzer - Earth-Science Reviews, 2024 - Elsevier
The massive accumulation of available multi-modal mineral exploration data for most
metallogenic belts worldwide provides abundant information for the discovery of mineral …

Random-drop data augmentation of deep convolutional neural network for mineral prospectivity map**

T Li, R Zuo, Y **ong, Y Peng - Natural Resources Research, 2021 - Springer
Convolutional neural network (CNN) has demonstrated promising performance in
classification and prediction in various fields. In this study, a CNN is used for mineral …

Data-driven predictive modelling of mineral prospectivity using machine learning and deep learning methods: A case study from southern Jiangxi Province, China

T Sun, H Li, K Wu, F Chen, Z Zhu, Z Hu - Minerals, 2020 - mdpi.com
Predictive modelling of mineral prospectivity, a critical, but challenging procedure for
delineation of undiscovered prospective targets in mineral exploration, has been spurred by …

Deep reinforcement learning for anomaly detection: A systematic review

K Arshad, RF Ali, A Muneer, IA Aziz, S Naseer… - Ieee …, 2022 - ieeexplore.ieee.org
Anomaly detection has been used to detect and analyze anomalous elements from data for
years. Various techniques have been developed to detect anomalies. However, the most …