Rapid resource model updating
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Citations since 2017
9 Research Items
My research interests are Multivariate Geostatistics, Multiple-Point Statistics, Mine Planning and Resource Model Updating.
September 2019 - September 2021
- Research Assistant
- Research Assistant in the School of Mining and Geosciences, Nazarbayev University. Project Name: "Multivariate Mapping of Geometallurgical Variables with Complex Cross-Correlation Characteristics". Principal Investigator: Dr. Nasser Madani.
This work addresses the problem of the cosimulation of cross-correlated variables with inequality constraints. A hierarchical sequential Gaussian cosimulation algorithm is proposed to address this problem, based on establishing a multicollocated cokriging paradigm; the integration of this algorithm with the acceptance–rejection sampling technique e...
A hierarchical sequential Gaussian cosimulation method is applied in this study for modeling the variables with an inequality constraint in the bivariate relationship. An algorithm is improved by embedding an inverse transform sampling technique in the second simulation to reproduce bivariate complexity and accelerate the process of cosimulation. A...
This work addresses the problem of quantifying iron content in a coal deposit in the Republic of Kazakhstan. The process of resource estimation in the mining industry usually involves building geological domains and then estimating the grade of interest within them. In coal deposits, the seam layers usually define the estimation domains. However, t...
Traditional geostatistical simulation techniques rely on the assumption of multi-Gaussianity. Although the normal score transform is widely used to convert data to a Gaussian distribution, it only guarantees that the normal scores will be univariate Gaussian and the variables may still have complex multivariate relationships. For this reason, multi...
Geostatistical simulation of two or more continuous variables is a common requirement in mining applications. In these applications, it is essential to consider the spatial correlation of each variable and the cross-correlations among them. For example, conventional co-simulation methods use a linear model of co-regionalisation to account for univa...
Resource models are generally constructed from directly observed data (e.g., grades of drill cores) that have relatively high accuracy. However, the accuracy of resource models is therefore limited by the scale on which the data are collected. As mining progresses, more information becomes available on different scales from various types and source...
Conventional geostatistical algorithms cannot reproduce bivariate complexities such as inequality constraint, nonlinearity and heteroscedasticity. Poor reproduction of these features may decrease the accuracy and reliability of mine planning results. For example, it is not unusual to have an inequality constraint between primary and disturbing elem...
In multivariate geostatistics, it is common to have different types of complexities between variables of interest. In this context, an inequality constraint is an example of complex bivariate relationships. Unfortunately, traditional co-kriging and co-simulation algorithms cannot reproduce this type of bivariate complexity, leading to the overestim...
The modern mining industry employs plenty of exploration data digitization and utilizes computational resources for future forecasting and production scheduling. In that regard, geostatistics and mine planning as disciplines are critical parts of the mining business. However, traditional mine planning does not allow the risk management associated w...
Rapid and stochastic updating of resource models with upstream (drill) and downstream (belt) sensor information for high-resolution reconciliation and rapid decision-making. Objectives: Calibrating the various types of data, integrating/fusing the data and developing and adapting methods for rapidly (near real-time) updating resource models with newly acquired data.
1. Propose a stochastic multivariate algorithm to model variables with inequality constraints. 2. Develop a mine planning methodology that can incorporate uncertainty from geostatistical realizations.