A supervised technique for drill-core mineral mapping using Hyperspectral data

Drilling is a key task in exploration campaigns to characterize mineral deposits at depth. Drillcores are first logged in the field by a geologist and with regards to, e.g., mineral assemblages, alteration patterns, and structural features. The core-logging information is then used to locate and target the important ore accumulations and select representative samples that are further analyzed by laboratory measurements (e.g., Scanning Electron Microscopy (SEM), Xray diffraction (XRD), X-ray Fluorescence (XRF)). However, core-logging is a laborious task and subject to the expertise of the geologist. Hyperspectral imaging is a non-invasive and non-destructive technique that is increasingly being used to support the geologist in the analysis of drill-core samples. Nonetheless, the benefit and impact of using hyperspectral data depend on the applied methods. With this in mind, machine learning techniques, which have been applied in different research fields, provide useful tools for an advance and more automatic analysis of the data. Lately, machine learning frameworks are also being implemented for mapping minerals in drill-core hyperspectral data. In this context, this work follows an approach to map minerals on drill-core hyperspectral data using supervised machine learning techniques, in which SEM data, integrated with the mineral liberation analysis (MLA) software, are used in training a classifier. More specifically, the highresolution mineralogical data obtained by SEM-MLA analysis is resampled and co-registered to the hyperspectral data to generate a training set. Due to the large difference in spatial resolution between the SEM-MLA and hyperspectral images, a pre-labeling strategy is required to link these two images at the hyperspectral data spatial resolution. In this study, we use the SEM-MLA image to compute the abundances of minerals for each hyperspectral pixel in the corresponding SEM-MLA region. We then use the abundances as features in a clustering procedure to generate the training labels. In the final step, the generated training set is fed into a supervised classification technique for the mineral mapping over a large area of a drill-core. The experiments are carried out on a visible to near-infrared (VNIR) and shortwave infrared (SWIR) hyperspectral data set and based on preliminary tests the mineral mapping task improves significantly.

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Preliminary Cardiac Disease Risk Prediction Based on Medical and Behavioural Data Set Using Supervised Machine Learning Techniques

Indian Journal of Science and Technology ◽

10.17485/ijst/2016/v9i31/96740 ◽

2016 ◽

Vol 9 (31) ◽

Cited By ~ 3

Author(s):

Thendral Puyalnithi ◽

V. Madhu Viswanatham

Keyword(s):

Machine Learning ◽

Risk Prediction ◽

Cardiac Disease ◽

Disease Risk ◽

Supervised Machine Learning ◽

Machine Learning Techniques ◽

Data Set ◽

Learning Techniques

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3D modelling of a mineral deposit using drill core hyperspectral data

10.5194/egusphere-egu2020-14952 ◽

2020 ◽

Author(s):

Roberto De La Rosa ◽

Mahdi Khodadadzadeh ◽

Cecilia Contreras ◽

Laura Tusa ◽

Moritz Kirsch ◽

...

Keyword(s):

Categorical Data ◽

Near Infrared ◽

Mining Industry ◽

Structural Features ◽

Derived Categories ◽

Hyperspectral Data ◽

Drill Core ◽

Data Set ◽

Core Samples ◽

Core Logging

Drill core samples have been traditionally used by the mining industry to make resource estimations and to build geological models. The hyperspectral drill core scanning has become a popular tool in mineral exploration because it provides a non-destructive method to rapidly characterise structural features, alteration patterns and rock mineralogy in a cost effective way. Typically, the hyperspectral sensors cover a wide spectral range from visible and near-infrared (VNIR) to short and long wave infrared (SWIR and LWIR). The spectral features in this range will help to characterize a large number of mineral phases and complement the traditional core logging techniques. The hyperspectral core scanning provide mineralogical information in a millimetre scale for the entire borehole, which fills the gap between the microscopic scale of some of the laboratory analytical methods or the sparse chemical assays and the meter scale from the lithological descriptions.However, applying this technique to the core samples of an entire ore deposit results in big datasets. Therefore, there is the need of a workflow to build a 3D geological model conditioned by the data applying suitable data reduction methods and appropriate interpolation techniques.This contribution presents a case study in the combination of traditional core logging and hyperspectral core logging for geological modelling. To attain mineral and alteration maps from the hyperspectral data, unsupervised classification techniques were applied generating a categorical data set. The amount of data was reduced by the application of a domain generation algorithm based on the hyperspectral information. The domain generated by the algorithm is a compositional categorical data set that was then fed to condition the application of stochastic Plurigaussian simulations in the construction of 3D models of geological domains. This technique allows to simulate the spatial distribution of the hyperspectral derived categories, to make a resource estimation and to calculate its associated uncertainty.

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MULTI-LABEL CLASSIFICATION FOR DRILL-CORE HYPERSPECTRAL MINERAL MAPPING

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xliii-b3-2020-383-2020 ◽

2020 ◽

Vol XLIII-B3-2020 ◽

pp. 383-388

Author(s):

I. C. Contreras ◽

M. Khodadadzadeh ◽

R. Gloaguen

Keyword(s):

Near Infrared ◽

Short Wave ◽

Hyperspectral Data ◽

Training Data ◽

Electromagnetic Spectrum ◽

Drill Core ◽

Data Set ◽

Mineral Mapping ◽

Mapping Task ◽

Liberation Analysis

Abstract. A multi-label classification concept is introduced for the mineral mapping task in drill-core hyperspectral data analysis. As opposed to traditional classification methods, this approach has the advantage of considering the different mineral mixtures present in each pixel. For the multi-label classification, the well-known Classifier Chain method (CC) is implemented using the Random Forest (RF) algorithm as the base classifier. High-resolution mineralogical data obtained from Scanning Electron Microscopy (SEM) instrument equipped with the Mineral Liberation Analysis (MLA) software are used for generating the training data set. The drill-core hyperspectral data used in this paper cover the visible-near infrared (VNIR) and the short-wave infrared (SWIR) range of the electromagnetic spectrum. The quantitative and qualitative analysis of the obtained results shows that the multi-label classification approach provides meaningful and descriptive mineral maps and outperforms the single-label RF classification for the mineral mapping task.

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Application of Machine Learning Techniques to Predict Binding Affinity for Drug Targets: A Study of Cyclin-Dependent Kinase 2

Current Medicinal Chemistry ◽

10.2174/2213275912666191102162959 ◽

2020 ◽

Vol 28 (2) ◽

pp. 253-265 ◽

Cited By ~ 3

Author(s):

Gabriela Bitencourt-Ferreira ◽

Amauri Duarte da Silva ◽

Walter Filgueira de Azevedo

Keyword(s):

Machine Learning ◽

Binding Affinity ◽

Predictive Performance ◽

Supervised Machine Learning ◽

Machine Learning Techniques ◽

Scoring Functions ◽

Cyclin Dependent Kinase ◽

Learning Models ◽

Learning Techniques ◽

Machine Learning Models

Background: The elucidation of the structure of cyclin-dependent kinase 2 (CDK2) made it possible to develop targeted scoring functions for virtual screening aimed to identify new inhibitors for this enzyme. CDK2 is a protein target for the development of drugs intended to modulate cellcycle progression and control. Such drugs have potential anticancer activities. Objective: Our goal here is to review recent applications of machine learning methods to predict ligand- binding affinity for protein targets. To assess the predictive performance of classical scoring functions and targeted scoring functions, we focused our analysis on CDK2 structures. Methods: We have experimental structural data for hundreds of binary complexes of CDK2 with different ligands, many of them with inhibition constant information. We investigate here computational methods to calculate the binding affinity of CDK2 through classical scoring functions and machine- learning models. Results: Analysis of the predictive performance of classical scoring functions available in docking programs such as Molegro Virtual Docker, AutoDock4, and Autodock Vina indicated that these methods failed to predict binding affinity with significant correlation with experimental data. Targeted scoring functions developed through supervised machine learning techniques showed a significant correlation with experimental data. Conclusion: Here, we described the application of supervised machine learning techniques to generate a scoring function to predict binding affinity. Machine learning models showed superior predictive performance when compared with classical scoring functions. Analysis of the computational models obtained through machine learning could capture essential structural features responsible for binding affinity against CDK2.

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Comparative Analysis of Machine Learning Techniques Using Predictive Modeling

Recent Advances in Computer Science and Communications ◽

10.2174/2666255813999200904164539 ◽

2020 ◽

Vol 13 ◽

Author(s):

Ritu Khandelwal ◽

Hemlata Goyal ◽

Rajveer Singh Shekhawat

Keyword(s):

Machine Learning ◽

Comparative Analysis ◽

Data Science ◽

Training Data ◽

Machine Learning Techniques ◽

Future Trends ◽

Data Set ◽

Learning Stage ◽

Learning Techniques ◽

Different Types

Introduction: Machine learning is an intelligent technology that works as a bridge between businesses and data science. With the involvement of data science, the business goal focuses on findings to get valuable insights on available data. The large part of Indian Cinema is Bollywood which is a multi-million dollar industry. This paper attempts to predict whether the upcoming Bollywood Movie would be Blockbuster, Superhit, Hit, Average or Flop. For this Machine Learning techniques (classification and prediction) will be applied. To make classifier or prediction model first step is the learning stage in which we need to give the training data set to train the model by applying some technique or algorithm and after that different rules are generated which helps to make a model and predict future trends in different types of organizations. Methods: All the techniques related to classification and Prediction such as Support Vector Machine(SVM), Random Forest, Decision Tree, Naïve Bayes, Logistic Regression, Adaboost, and KNN will be applied and try to find out efficient and effective results. All these functionalities can be applied with GUI Based workflows available with various categories such as data, Visualize, Model, and Evaluate. Result: To make classifier or prediction model first step is learning stage in which we need to give the training data set to train the model by applying some technique or algorithm and after that different rules are generated which helps to make a model and predict future trends in different types of organizations Conclusion: This paper focuses on Comparative Analysis that would be performed based on different parameters such as Accuracy, Confusion Matrix to identify the best possible model for predicting the movie Success. By using Advertisement Propaganda, they can plan for the best time to release the movie according to the predicted success rate to gain higher benefits. Discussion: Data Mining is the process of discovering different patterns from large data sets and from that various relationships are also discovered to solve various problems that come in business and helps to predict the forthcoming trends. This Prediction can help Production Houses for Advertisement Propaganda and also they can plan their costs and by assuring these factors they can make the movie more profitable.

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Local mortality estimates during the COVID-19 pandemic in Italy

Journal of Population Economics ◽

10.1007/s00148-021-00857-y ◽

2021 ◽

Author(s):

Augusto Cerqua ◽

Roberta Di Stefano ◽

Marco Letta ◽

Sara Miccoli

Keyword(s):

Machine Learning ◽

Excess Mortality ◽

Control Method ◽

Local Level ◽

Supervised Machine Learning ◽

Machine Learning Techniques ◽

Mortality Data ◽

Official Method ◽

Learning Techniques ◽

Mortality Estimates

AbstractEstimates of the real death toll of the COVID-19 pandemic have proven to be problematic in many countries, Italy being no exception. Mortality estimates at the local level are even more uncertain as they require stringent conditions, such as granularity and accuracy of the data at hand, which are rarely met. The “official” approach adopted by public institutions to estimate the “excess mortality” during the pandemic draws on a comparison between observed all-cause mortality data for 2020 and averages of mortality figures in the past years for the same period. In this paper, we apply the recently developed machine learning control method to build a more realistic counterfactual scenario of mortality in the absence of COVID-19. We demonstrate that supervised machine learning techniques outperform the official method by substantially improving the prediction accuracy of the local mortality in “ordinary” years, especially in small- and medium-sized municipalities. We then apply the best-performing algorithms to derive estimates of local excess mortality for the period between February and September 2020. Such estimates allow us to provide insights about the demographic evolution of the first wave of the pandemic throughout the country. To help improve diagnostic and monitoring efforts, our dataset is freely available to the research community.

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