scholarly journals A New Ore Grade Estimation Using Combine Machine Learning Algorithms

Minerals ◽  
2020 ◽  
Vol 10 (10) ◽  
pp. 847
Author(s):  
Umit Emrah Kaplan ◽  
Erkan Topal
Keyword(s):  
Mineral Exploration ◽  
Absolute Error ◽  
Drill Hole ◽  
Mineral Deposit ◽  
Machine Learning Algorithms ◽  
Feed Forward Neural Network ◽  
Grade Estimation ◽  
Rock Types ◽  
Geological Information ◽  
Mining Projects

Accurate prediction of mineral grades is a fundamental step in mineral exploration and resource estimation, which plays a significant role in the economic evaluation of mining projects. Currently available methods are based either on geometrical approaches or geostatistical techniques that often considers the grade as a regionalised variable. In this paper, we propose a grade estimation technique that combines multilayer feed-forward neural network (NN) and k-nearest neighbour (kNN) models to estimate the grade distribution within a mineral deposit. The models were created by using the available geological information (lithology and alteration) as well as sample locations (easting, northing, and altitude) obtained from the drill hole data. The proposed approach explicitly maintains pattern recognition over the geological features and the chemical composition (mineral grade) of the data. Prior to the estimation of grades, rock types and alterations were predicted at unsampled locations using the kNN algorithm. The presented case study demonstrates that the proposed approach can predict the grades on a test dataset with a mean absolute error (MAE) of 0.507 and R2=0.528, whereas the traditional model, which only uses the coordinates of sample points as an input, yielded an MAE value of 0.862 and R2=0.112. The proposed approach is promising and could be an alternative way to estimates grades in a similar modelling tasks.

VALUE OF GEOLOGICAL INFORMATION IN EXPLOITATION MANAGEMENT: THE CASE OF EXPLOITATION UNITS OF THE POLKOWICE-SIEROSZOWICE MINE

2014 ◽  
Vol 59 (1) ◽  
pp. 239-256
Author(s):  
Mariusz Krzak ◽  
Paweł Panajew
Keyword(s):  
Decision Making ◽  
Mining Industry ◽  
Mineral Deposit ◽  
Mineral Deposits ◽  
Decision Making Problem ◽  
Geological Information ◽  
Close Cooperation ◽  
Mathematical Techniques ◽  
Mining Projects ◽  
States Of Nature

Abstract The application of mathematical techniques of management is particularly significant in managing mineral deposits as well as generally in the mining industry, in which the execution of geological-mining projects is usually time-consuming and expensive. Such projects are usually undertaken in conditions of uncertainty, and the incurred expenses do not always generate satisfactory revenues. Mineral deposit management requires close cooperation between the geologist providing necessary information about the deposit and the miner conducting exploitation work. A real decision-making problem was undertaken, in which three exploitation divisions of a certain area in the Polkowice-Sieroszowice mine, differing in ore quality, could be developed in an order which would guarantee maximisation of income. First, the ore price was calculated with the NSR formula; next, the decision-making problem was presented as a kind of game between the geologist (the mine) and states of Nature.

Prediction of aircraft estimated time of arrival using machine learning methods

2021 ◽  
pp. 1-15
Author(s):  
O. Basturk ◽  
C. Cetek
Keyword(s):  
Machine Learning ◽  
Web Application ◽  
Absolute Error ◽  
Machine Learning Algorithms ◽  
Weather Data ◽  
Time Of Arrival ◽  
Learning Models ◽  
Trajectory Data ◽  
Different Sources ◽  
Machine Learning Models

ABSTRACT In this study, prediction of aircraft Estimated Time of Arrival (ETA) is proposed using machine learning algorithms. Accurate prediction of ETA is important for management of delay and air traffic flow, runway assignment, gate assignment, collaborative decision making (CDM), coordination of ground personnel and equipment, and optimisation of arrival sequence etc. Machine learning is able to learn from experience and make predictions with weak assumptions or no assumptions at all. In the proposed approach, general flight information, trajectory data and weather data were obtained from different sources in various formats. Raw data were converted to tidy data and inserted into a relational database. To obtain the features for training the machine learning models, the data were explored, cleaned and transformed into convenient features. New features were also derived from the available data. Random forests and deep neural networks were used to train the machine learning models. Both models can predict the ETA with a mean absolute error (MAE) less than 6min after departure, and less than 3min after terminal manoeuvring area (TMA) entrance. Additionally, a web application was developed to dynamically predict the ETA using proposed models.

scholarly journals An interpretable prediction method for university student academic crisis warning

2021 ◽  
Author(s):  
Zhai Mingyu ◽  
Wang Sutong ◽  
Wang Yanzhang ◽  
Wang Dujuan
Keyword(s):  
Academic Achievement ◽  
Visual Analysis ◽  
Prediction Method ◽  
Absolute Error ◽  
University Student ◽  
Machine Learning Algorithms ◽  
Coefficient Of Determination ◽  
Achievement Prediction ◽  
Academic Achievement Prediction ◽  
Education Administrators

AbstractData-driven techniques improve the quality of talent training comprehensively for university by discovering potential academic problems and proposing solutions. We propose an interpretable prediction method for university student academic crisis warning, which consists of K-prototype-based student portrait construction and Catboost–SHAP-based academic achievement prediction. The academic crisis warning experiment is carried out on desensitization multi-source student data of a university. The experimental results show that the proposed method has significant advantages over common machine learning algorithms. In terms of achievement prediction, mean square error (MSE) reaches 24.976, mean absolute error (MAE) reaches 3.551, coefficient of determination ($$R^{2}$$ R 2 ) reaches 80.3%. The student portrait and Catboost–SHAP method are used for visual analysis of the academic achievement factors, which provide intuitive decision support and guidance assistance for education administrators.

Mineral exploration with 3-D controlled-source electromagnetic method: a synthetic study of Sukhoi Log gold deposit

2019 ◽  
Vol 219 (3) ◽  
pp. 1698-1716 ◽  
Author(s):  
M Malovichko ◽  
A V Tarasov ◽  
N Yavich ◽  
M S Zhdanov
Keyword(s):  
Finite Difference ◽  
Gold Deposit ◽  
Large Scale ◽  
Mineral Exploration ◽  
Mineral Deposit ◽  
Hydrocarbon Exploration ◽  
Contraction Operator ◽  
Inversion Algorithm ◽  
Controlled Source ◽  
Regularized Inversion

SUMMARY This paper presents a feasibility study of using the controlled-source frequency-domain electromagnetic (CSEM) method in mineral exploration. The method has been widely applied for offshore hydrocarbon exploration; however, nowadays this method is rarely used on land. In order to conduct this study, we have developed a fully parallelized forward modelling finite-difference (FD) code based on the iterative solver with contraction-operator preconditioner. The regularized inversion algorithm uses the Gauss–Newton method to minimize the Tikhonov parametric functional with the Laplacian-type stabilizer. A 3-D parallel inversion code, based on the iterative finite-difference solver with the contraction-operator preconditioner, has been evaluated for the solution of the large-scale inverse problems. Using the computer simulation for a synthetic model of Sukhoi Log gold deposit, we have compared the CSEM method with the conventional direct current sounding and the CSEM survey with a single remote transmitter. Our results suggest that, a properly designed electromagnetic survey together with modern 3-D inversion could provide detailed information about the geoelectrical structure of the mineral deposit.

scholarly journals Geostatistical Estimation of Multi-Domain Deposits with Transitional Boundaries: A Sensitivity Study for the Sechahun Iron Mine

Minerals ◽  
2019 ◽  
Vol 9 (2) ◽  
pp. 115 ◽  
Author(s):  
Sara Kasmaee ◽  
Giuseppe Raspa ◽  
Chantal de Fouquet ◽  
Francesco Tinti ◽  
Stefano Bonduà ◽  
...  
Keyword(s):  
Mineral Resource ◽  
Sensitivity Study ◽  
Accurate Estimate ◽  
Resource Estimation ◽  
Real Application ◽  
Grade Estimation ◽  
Boundary Identification ◽  
Geostatistical Estimation ◽  
Iron Mine ◽  
Geological Information

In mineral resource estimation, identification of the geological domains to be used for modeling, and the type of boundaries dividing them, is a major concern. Generally, the variables within a domain are estimated with an assumption of the hard boundaries (sharp contact). However, in many cases, the geologic structures that generate a deposit are transitional (overlapping of several geologic domains). Consequently, boundary identification of the geological domains is essential for an accurate estimate of resources. This paper considers a real application to examine whether the addition of geologic information benefits grade estimation in the presence of transitional boundaries. Results proved that the accuracy of the grade estimation can be improved by adding geological information and there is a significant sensitivity in grade estimation results in the existence of transitional boundaries.

scholarly journals MACHINE LEARNING METHODS IN MONITORING OPERATING BEHAVIOUR OF MARINE TWO-STROKE DIESEL ENGINE

Transport ◽  
2020 ◽  
Vol 35 (5) ◽  
pp. 462-473
Author(s):  
Aleksandar Vorkapić ◽  
Radoslav Radonja ◽  
Karlo Babić ◽  
Sanda Martinčić-Ipšić
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Fuel Consumption ◽  
Performance Monitoring ◽  
Absolute Error ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Operating Parameters ◽  
Detection Model ◽  
Modelling Framework

The aim of this article is to enhance performance monitoring of a two-stroke electronically controlled ship propulsion engine on the operating envelope. This is achieved by setting up a machine learning model capable of monitoring influential operating parameters and predicting the fuel consumption. Model is tested with different machine learning algorithms, namely linear regression, multilayer perceptron, Support Vector Machines (SVM) and Random Forests (RF). Upon verification of modelling framework and analysing the results in order to improve the prediction accuracy, the best algorithm is selected based on standard evaluation metrics, i.e. Root Mean Square Error (RMSE) and Relative Absolute Error (RAE). Experimental results show that, by taking an adequate combination and processing of relevant sensory data, SVM exhibit the lowest RMSE 7.1032 and RAE 0.5313%. RF achieve the lowest RMSE 22.6137 and RAE 3.8545% in a setting when minimal number of input variables is considered, i.e. cylinder indicated pressures and propulsion engine revolutions. Further, article deals with the detection of anomalies of operating parameters, which enables the evaluation of the propulsion engine condition and the early identification of failures and deterioration. Such a time-dependent, self-adopting anomaly detection model can be used for comparison with the initial condition recorded during the test and sea run or after survey and docking. Finally, we propose a unified model structure, incorporating fuel consumption prediction and anomaly detection model with on-board decision-making process regarding navigation and maintenance.

scholarly journals <i>dh2loop</i> 1.0: an open-source Python library for automated processing and classification of geological logs

2021 ◽  
Vol 14 (11) ◽  
pp. 6711-6740
Author(s):  
Ranee Joshi ◽  
Kavitha Madaiah ◽  
Mark Jessell ◽  
Mark Lindsay ◽  
Guillaume Pirot
Keyword(s):  
Open Source ◽  
Mineral Exploration ◽  
String Matching ◽  
Drill Hole ◽  
Geological Survey ◽  
Database Structure ◽  
Automated Processing ◽  
Study Case ◽  
Textual Form

Abstract. A huge amount of legacy drilling data is available in geological survey but cannot be used directly as they are compiled and recorded in an unstructured textual form and using different formats depending on the database structure, company, logging geologist, investigation method, investigated materials and/or drilling campaign. They are subjective and plagued by uncertainty as they are likely to have been conducted by tens to hundreds of geologists, all of whom would have their own personal biases. dh2loop (https://github.com/Loop3D/dh2loop, last access: 30 September 2021​​​​​​​) is an open-source Python library for extracting and standardizing geologic drill hole data and exporting them into readily importable interval tables (collar, survey, lithology). In this contribution, we extract, process and classify lithological logs from the Geological Survey of Western Australia (GSWA) Mineral Exploration Reports (WAMEX) database in the Yalgoo–Singleton greenstone belt (YSGB) region. The contribution also addresses the subjective nature and variability of the nomenclature of lithological descriptions within and across different drilling campaigns by using thesauri and fuzzy string matching. For this study case, 86 % of the extracted lithology data is successfully matched to lithologies in the thesauri. Since this process can be tedious, we attempted to test the string matching with the comments, which resulted in a matching rate of 16 % (7870 successfully matched records out of 47 823 records). The standardized lithological data are then classified into multi-level groupings that can be used to systematically upscale and downscale drill hole data inputs for multiscale 3D geological modelling. dh2loop formats legacy data bridging the gap between utilization and maximization of legacy drill hole data and drill hole analysis functionalities available in existing Python libraries (lasio, welly, striplog).

Classification of Sleep Apnea Using ECG Signals With Machine Learning Techniques

2021 ◽  
pp. 184-203
Author(s):  
Karthik R. ◽  
Ifrah Alam ◽  
Bandaru Umamadhuri ◽  
Bharath K. P. ◽  
Rajesh Kumar M.
Keyword(s):  
Machine Learning ◽  
Sleep Apnea ◽  
Soft Tissues ◽  
Amplitude Distribution ◽  
Feature Reduction ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Feed Forward Neural Network ◽  
Ecg Signals

In this chapter, the authors use various signal processing techniques to analyze and gain insights on how ECG signals for patients suffering from sleep apnea (sleep apnea or obstructive sleep apnea occurs when the muscles that support the soft tissues in the throat, such as tongue and soft palate, relax temporarily) disease vary with respect to a normal person's ECG. The work has three stages: firstly, to identify waves, complexes, morphology in an ECG which reflect the presence of the disease; second, feature extraction techniques to extract features of ECG such as duration of the wave, amplitude distribution, and morphology classes; and third, detailed clustering (unsupervised) algorithm analysis of the extracted features with efficient feature reduction methodologies such as PCA and LDA. Finally, the authors use supervised machine learning algorithms (SVM, naive Bayes classifier, feed forward neural network, and decision tree) to distinguish between ECG signals with sleep apnea and normal ECG signals.

scholarly journals Development and Evaluation of the Combined Machine Learning Models for the Prediction of Dam Inflow

Water ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 2927
Author(s):  
Jiyeong Hong ◽  
Seoro Lee ◽  
Joo Hyun Bae ◽  
Jimin Lee ◽  
Woon Ji Park ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Random Forest ◽  
Multilayer Perceptron ◽  
Short Term Memory ◽  
Learning Algorithms ◽  
Absolute Error ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Dam Inflow

Predicting dam inflow is necessary for effective water management. This study created machine learning algorithms to predict the amount of inflow into the Soyang River Dam in South Korea, using weather and dam inflow data for 40 years. A total of six algorithms were used, as follows: decision tree (DT), multilayer perceptron (MLP), random forest (RF), gradient boosting (GB), recurrent neural network–long short-term memory (RNN–LSTM), and convolutional neural network–LSTM (CNN–LSTM). Among these models, the multilayer perceptron model showed the best results in predicting dam inflow, with the Nash–Sutcliffe efficiency (NSE) value of 0.812, root mean squared errors (RMSE) of 77.218 m3/s, mean absolute error (MAE) of 29.034 m3/s, correlation coefficient (R) of 0.924, and determination coefficient (R2) of 0.817. However, when the amount of dam inflow is below 100 m3/s, the ensemble models (random forest and gradient boosting models) performed better than MLP for the prediction of dam inflow. Therefore, two combined machine learning (CombML) models (RF_MLP and GB_MLP) were developed for the prediction of the dam inflow using the ensemble methods (RF and GB) at precipitation below 16 mm, and the MLP at precipitation above 16 mm. The precipitation of 16 mm is the average daily precipitation at the inflow of 100 m3/s or more. The results show the accuracy verification results of NSE 0.857, RMSE 68.417 m3/s, MAE 18.063 m3/s, R 0.927, and R2 0.859 in RF_MLP, and NSE 0.829, RMSE 73.918 m3/s, MAE 18.093 m3/s, R 0.912, and R2 0.831 in GB_MLP, which infers that the combination of the models predicts the dam inflow the most accurately. CombML algorithms showed that it is possible to predict inflow through inflow learning, considering flow characteristics such as flow regimes, by combining several machine learning algorithms.

Effect of tropical weathering on electrical and electromagnetic measurements

Geophysics ◽  
1979 ◽  
Vol 44 (1) ◽  
pp. 69-88 ◽  
Author(s):  
G. J. Palacky ◽  
Kiyoshi Kadekaru
Keyword(s):  
Electrical Properties ◽  
Mineral Exploration ◽  
Annual Rainfall ◽  
Tropical Regions ◽  
Rock Types ◽  
Electromagnetic Measurements ◽  
Weathered Layer ◽  
Efficient Exploration ◽  
Effective Use ◽  
Airborne Em

Electrical properties of the weathered layer in tropical regions of Brazil were investigated by means of resistivity soundings, airborne, and ground electromagnetic measurements. Five case histories illustrate how changes of climate, lithology, and geomorphology affect geophysical measurements. In humid and subhumid tropical regions (annual rainfall over 650 mm) the weathered layer is between 10 and 80 m thick and moderately conductive. Results from one region (Minas Gerais) indicate that excessive depth of weathering and leaching of massive sulfides, rather than the conductivity of overburden, present the greatest obstacle to effective use of airborne EM methods in mineral exploration. Seasonal variations of precipitation cause changes in soil resistivity, but such changes are not apparent in the underlying weathered layer. In semiarid and temperate regions of Brazil, the weathered layer is 10 to 20 m thick and regional airborne EM surveys are an efficient exploration tool. In all regions, the degree of weathering depends upon lithology and, in several areas, anomaly patterns obtained from airborne EM surveys correlate well with the surface geologic map. However, when comapring electrical properties of similar rock types among regions of the same climatic type, a considerable variation is observed. It seems that also geomorphology plays an important role in weathering. A careful interpretation of airborne EM data is necessary to distinguish anomalies caused by the weathered layer from those due to underlying conductors. Highly conductive, saline alluvia, which cause strong EM anomalies in Australia, were encountered (sporadically) in only one region of Brazil, the semiarid Valley of Curaçá, Bahia.

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