[HTML][HTML] Ensemble learning models with a Bayesian optimization algorithm for mineral prospectivity map**
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 …
map** (MPM). In this study, we implemented ensemble learning of extreme gradient …
Deep learning in image segmentation for mineral production: A review
Y Liu, X Wang, Z Zhang, F Deng - Computers & Geosciences, 2023 - Elsevier
Mineral image segmentation is widely used in mining, sorting, exploration, composition
analysis, and other production works. The burgeoning field of deep learning provides …
analysis, and other production works. The burgeoning field of deep learning provides …
Stacking: A novel data-driven ensemble machine learning strategy for prediction and map** of Pb-Zn prospectivity in Varcheh district, west Iran
Various ensemble machine learning techniques have been widely studied and implemented
to construct the predictive models in different sciences, including bagging, boosting, and …
to construct the predictive models in different sciences, including bagging, boosting, and …
A novel scheme for map** of MVT-type Pb–Zn prospectivity: LightGBM, a highly efficient gradient boosting decision tree machine learning algorithm
The gradient boosting decision tree is a well-known machine learning algorithm. Despite
numerous advancements in its application, its efficiency still needs to be improved for large …
numerous advancements in its application, its efficiency still needs to be improved for large …
Mineral prospectivity map** over the Gomoa Area of Ghana's southern Kibi-Winneba belt using support vector machine and naive bayes
Geospatial modeling of mineral prospective regions is essential, owing to its significant
contribution towards the development and economic gains of many mineral-endowed …
contribution towards the development and economic gains of many mineral-endowed …
Deep GMDH neural networks for predictive map** of mineral prospectivity in terrains hosting few but large mineral deposits
There has been in recent years a trend towards adopting deep neural networks for
addressing earth science problems. Of the various deep neural networks applied to different …
addressing earth science problems. Of the various deep neural networks applied to different …
Learning 3D mineral prospectivity from 3D geological models using convolutional neural networks: Application to a structure-controlled hydrothermal gold deposit
H Deng, Y Zheng, J Chen, S Yu, K ** 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) …
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 map** 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 …