Application of continuous restricted Boltzmann machine to identify multivariate geochemical anomaly

Chen, Yongliang; Lu, Laijun; Li, Xuebin

doi:10.1016/j.gexplo.2014.02.013

Cited by 113 publications

(24 citation statements)

References 21 publications

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“…Two extended versions of restricted Boltzmann machines are introduced in literature. They are, respectively, Gaussian-Bernoulli restricted Boltzmann machine (Cho et al, 2011) and continuous restricted Boltzmann machine (Chen and Murray, 2003;Chen et al, 2014). These two extended models can be applied to map mineral potential if the evidence map patterns are continuousvalued ones.…”

Section: Discussionmentioning

confidence: 99%

Mineral potential mapping with a restricted Boltzmann machine

Yongliang

2015

Ore Geology Reviews

Self Cite

113

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Section: Discussionmentioning

confidence: 99%

Mineral potential mapping with a restricted Boltzmann machine

Yongliang

2015

Ore Geology Reviews

Self Cite

113

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“…The second column (AUC) indicates the correct identification probability of the known Fe deposits. The third column shows the range of anomaly recognition, denoting the proportion of the identified anomaly grids [24]. The fourth column shows the ratio of the number of Fe deposits falling in the forecast area to the total number of known Fe deposits [24].…”

Section: Weights-of-evidence and Student's T-valuementioning

confidence: 99%

“…For example, DBN has been used to extract the characteristics of the relationships between the elements related to the Fe ore, and to reconstruct the backgrounds to separate anomalies from the background [23]. Chen et al used the continuous restricted Boltzmann machine (CRBM) model to learn the relationship between known deposits and an evidence map [24]. In short, applications of ANN/DL models in geochemical prospecting have only focused on the relationships among the geochemical elements and/or the correlations between known deposits and geochemical elements.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Convolutional Autoencoder Approach to Multivariate Geochemical Anomaly Recognition

Chen

Guan

Feng³

et al. 2019

Minerals

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The spatial structural patterns of geochemical backgrounds are often ignored in geochemical anomaly recognition, leading to the ineffective recognition of valuable anomalies in geochemical prospecting. In this contribution, a multi-convolutional autoencoder (MCAE) approach is proposed to deal with this issue, which includes three unique steps: (1) a whitening process is used to minimize the correlations among geochemical elements, avoiding the diluting of effective background information embedded in redundant data; (2) the Global Moran’s I index is used to determine the recognition domain of the background spatial structure for each element, and then the domain is used for convolution window size setting in MCAE; and (3) a multi-convolutional autoencoder framework is designed to learn the spatial structural pattern and reconstruct the geochemical background of each element. Finally, the anomaly score at each sampling location is calculated as the difference between the whitened geochemical features and the reconstructed features. This method was applied to the southwestern Fujian Province metalorganic belt in China, using the concentrations of Cu, Mn, Pb, Zn, and Fe2O3 measured from stream sediment samples. The results showed that the recognition domain determination greatly improved the quality of anomaly recognition, and MCAE outperformed several existing methods in all aspects. In particular, the anomalies from MCAE were the most consistent with the known Fe deposits in the area, achieving an area under the curve (AUC) of 0.89 and a forecast area of 17%.

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“…To establish another approach to geochemical mapping, Zuo et al [26] applied deep learning and indicated that this method could deal with nonlinear and complex problems. In addition, various ML techniques have been used to detect the content as well as the potential of minerals and geochemical anomalies as the following works [18,25,[27][28][29][30][31][32][33][34].…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Different Machine Learning Algorithms in Predicting the Content of Ilmenite in Titanium Placer

Bui

et al. 2020

Applied Sciences

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In this study, the ilmenite content in beach placer sand was estimated using seven soft computing techniques, namely random forest (RF), artificial neural network (ANN), k-nearest neighbors (kNN), cubist, support vector machine (SVM), stochastic gradient boosting (SGB), and classification and regression tree (CART). The 405 beach placer borehole samples were collected from Southern Suoi Nhum deposit, Binh Thuan province, Vietnam, to test the feasibility of these soft computing techniques in estimating ilmenite content. Heavy mineral analysis indicated that valuable minerals in the placer sand are zircon, ilmenite, leucoxene, rutile, anatase, and monazite. In this study, five materials, namely rutile, anatase, leucoxene, zircon, and monazite, were used as the input variables to estimate ilmenite content based on the above mentioned soft computing models. Of the whole dataset, 325 samples were used to build the regarded soft computing models; 80 remaining samples were used for the models’ verification. Root-mean-squared error (RMSE), determination coefficient (R2), a simple ranking method, and residuals analysis technique were used as the statistical criteria for assessing the model performances. The numerical experiments revealed that soft computing techniques are capable of estimating the content of ilmenite with high accuracy. The residuals analysis also indicated that the SGB model was the most suitable for determining the ilmenite content in the context of this research.

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Application of continuous restricted Boltzmann machine to identify multivariate geochemical anomaly

Cited by 113 publications

References 21 publications

Mineral potential mapping with a restricted Boltzmann machine

Mineral potential mapping with a restricted Boltzmann machine

A Multi-Convolutional Autoencoder Approach to Multivariate Geochemical Anomaly Recognition

A Comparative Study of Different Machine Learning Algorithms in Predicting the Content of Ilmenite in Titanium Placer

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