Deep learning and its application in geochemical mapping
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 …
science, social science and engineering. It can be expected that machine learning …
The processing methods of geochemical exploration data: past, present, and future
Geochemical exploration data is popular in mineral exploration in that it plays a notable role
in discovering unknown mineral deposits. In this study, we review the state-of-the-art popular …
in discovering unknown mineral deposits. In this study, we review the state-of-the-art popular …
Wind turbine power output prediction using a new hybrid neuro-evolutionary method
Short-term wind power prediction is challenging due to the chaotic characteristics of wind
speed. Since, for wind power industries, designing an accurate and reliable wind power …
speed. Since, for wind power industries, designing an accurate and reliable wind power …
Mapping mineral prospectivity through big data analytics and a deep learning algorithm
Identification of anomalies related to mineralization and integration of multi-source
geoscience data are essential for mapping mineral prospectivity. In this study, we applied …
geoscience data are essential for mapping mineral prospectivity. In this study, we applied …
Robust feature extraction for geochemical anomaly recognition using a stacked convolutional denoising autoencoder
Deep neural networks perform very well in learning high-level representations in support of
multivariate geochemical anomaly recognition. Geochemical exploration data typically …
multivariate geochemical anomaly recognition. Geochemical exploration data typically …
Data-driven mineral prospectivity mapping by joint application of unsupervised convolutional auto-encoder network and supervised convolutional neural network
The excellent performance of convolutional neural network (CNN) and its variants in image
classification makes it a potential perfect candidate for dealing with multi-geoinformation …
classification makes it a potential perfect candidate for dealing with multi-geoinformation …
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 …
Recognition of geochemical anomalies using a deep variational autoencoder network
Deep learning (DL) algorithms have received increased attention in various fields. In the
field of geoscience, DL has been shown to be a powerful tool for mining complex, high-level …
field of geoscience, DL has been shown to be a powerful tool for mining complex, high-level …
Recognizing multivariate geochemical anomalies for mineral exploration by combining deep learning and one-class support vector machine
The recognition of multivariate geochemical anomalies is important for mineral exploration.
Big data analytics, which involves the whole data and variables, is an alternative manner to …
Big data analytics, which involves the whole data and variables, is an alternative manner to …
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 …