arXiv subjects

Arman Melkumyan

Publications and source records attributed to Arman Melkumyan.

13 recordsLinked to original sources

arXiv2026

Effective information gathering for ore estimation, evaluation and perspectives on adaptive sampling

A computational/analytics framework for assessing the value of drill-hole information in ore grade estimation is described using Gaussian Process and statistics. A distinguishing feature is that it presents both a near-term and long-term vision, circumvents conditional simulations and avoids making rigid assumptions such as stationarity and uncorrelated errors. Two experiments are devised to cater for situations where geological domains are differentiated or mixed. In scenario 1, performance (learning) curves are obtained to inform in-fill drilling and spacing consideration consistent with current practice. Analysis shows it is possible to estimate the incremental cost and reward via a proxy measure without relying on the ground truth, using insights obtained from a similar deposit, adjacent bench or domain. Scenario 2 examines adaptive sampling strategies and focuses on applying these in geologically complex areas with discontinuities and heterogeneous composition. Evaluation is made based on structural similarity, the mean and uncertainty in the posterior predictive distribution for the grade. The results highlight situations where regular grid sampling is suboptimal, and demonstrate an adaptive strategy that targets spatial complexity is capable of narrowing this gap. The proposed methodology can potentially be used in the future in an exploration--exploitation setting that involves sampling, machine learning, reasoning and cooperation between robots with embodied intelligence on a mine site.

cs.CE↗

arXiv2025

IntegralGP: Volumetric estimation of subterranean geochemical properties in mineral deposits by fusing assay data with different spatial supports

This article presents an Integral Gaussian Process (IntegralGP) framework for volumetric estimation of subterranean properties in mineral deposits. It provides a unified representation for data with different spatial supports, which enables blasthole geochemical assays to be properly modelled as interval observations rather than points. This approach is shown to improve regression performance and boundary delineation. A core contribution is a description of the mathematical changes to the covariance expressions which allow these benefits to be realised. The gradient and anti-derivatives are obtained to facilitate learning of the kernel hyperparameters. Numerical stability issues are also discussed. To illustrate its application, an IntegralGP data fusion algorithm is described. The objective is to assimilate line-based blasthole assays and update a block model that provides long-range prediction of Fe concentration beneath the drilled bench. Heteroscedastic GP is used to fuse chemically compatible but spatially incongruous data with different resolutions and sample spacings. Domain knowledge embodied in the structure and empirical distribution of the block model must be generally preserved while local inaccuracies are corrected. Using validation measurements within the predicted bench, our experiments demonstrate an improvement in bench-below grade prediction performance. For material classification, IntegralGP fusion reduces the absolute error and model bias in categorical prediction, especially instances where waste blocks are mistakenly classified as high-grade.

stat.ME↗

arXiv2024

Automation and AI Technology in Surface Mining With a Brief Introduction to Open-Pit Operations in the Pilbara

This survey article provides a synopsis on some of the engineering problems, technological innovations, robotic development and automation efforts encountered in the mining industry -- particularly in the Pilbara iron-ore region of Western Australia. The goal is to paint the technology landscape and highlight issues relevant to an engineering audience to raise awareness of AI and automation trends in mining. It assumes the reader has no prior knowledge of mining and builds context gradually through focused discussion and short summaries of common open-pit mining operations. The principal activities that take place may be categorized in terms of resource development, mine-, rail- and port operations. From mineral exploration to ore shipment, there are roughly nine steps in between. These include: geological assessment, mine planning and development, production drilling and assaying, blasting and excavation, transportation of ore and waste, crush and screen, stockpile and load-out, rail network distribution, and ore-car dumping. The objective is to describe these processes and provide insights on some of the challenges/opportunities from the perspective of a decade-long industry-university R&D partnership.

cs.CY↗

arXiv2023

Unsupervised ore/waste classification on open-cut mine faces using close-range hyperspectral data

The remote mapping of minerals and discrimination of ore and waste on surfaces are important tasks for geological applications such as those in mining. Such tasks have become possible using ground-based, close-range hyperspectral sensors which can remotely measure the reflectance properties of the environment with high spatial and spectral resolution. However, autonomous mapping of mineral spectra measured on an open-cut mine face remains a challenging problem due to the subtleness of differences in spectral absorption features between mineral and rock classes as well as variability in the illumination of the scene. An additional layer of difficulty arises when there is no annotated data available to train a supervised learning algorithm. A pipeline for unsupervised mapping of spectra on a mine face is proposed which draws from several recent advances in the hyperspectral machine learning literature. The proposed pipeline brings together unsupervised and self-supervised algorithms in a unified system to map minerals on a mine face without the need for human-annotated training data. The pipeline is evaluated with a hyperspectral image dataset of an open-cut mine face comprising mineral ore martite and non-mineralised shale. The combined system is shown to produce a superior map to its constituent algorithms, and the consistency of its mapping capability is demonstrated using data acquired at two different times of day.

cs.CV↗

arXiv2022

A Machine Learning Approach for Material Type Logging and Chemical Assaying from Autonomous Measure-While-Drilling (MWD) Data

Understanding the structure and mineralogical composition of a region is an essential step in mining, both during exploration (before mining) and in the mining process. During exploration, sparse but high-quality data are gathered to assess the overall orebody. During the mining process, boundary positions and material properties are refined as the mine progresses. This refinement is facilitated through drilling, material logging, and chemical assaying. Material type logging suffers from a high degree of variability due to factors such as the diversity in mineralization and geology, the subjective nature of human measurement even by experts, and human error in manually recording results. While laboratory-based chemical assaying is much more precise, it is time-consuming and costly and does not always capture or correlate boundary positions between all material types. This leads to significant challenges and financial implications for the industry, as the accuracy of production blasthole logging and assaying processes is essential for resource evaluation, planning, and execution of mine plans. To overcome these challenges, this work reports on a pilot study to automate the process of material logging and chemical assaying. A machine learning approach has been trained on features extracted from measurement-while-drilling (MWD) data, logged from autonomous drilling systems (ADS). MWD data facilitate the construction of profiles of physical drilling parameters as a function of hole depth. A hypothesis is formed to link these drilling parameters to the underlying mineral composition. The results of the pilot study discussed in this paper demonstrate the feasibility of this process, with correlation coefficients of up to 0.92 for chemical assays and 93% accuracy for material detection, depending on the material or assay type and their generalization across the different spatial regions.

cs.LG↗

arXiv2021

Bayesian Surface Warping Approach For Rectifying Geological Boundaries Using Displacement Likelihood And Evidence From Geochemical Assays

This paper presents a Bayesian framework for manipulating mesh surfaces with the aim of improving the positional integrity of the geological boundaries that they seek to represent. The assumption is that these surfaces, created initially using sparse data, capture the global trend and provide a reasonable approximation of the stratigraphic, mineralisation and other types of boundaries for mining exploration, but they are locally inaccurate at scales typically required for grade estimation. The proposed methodology makes local spatial corrections automatically to maximise the agreement between the modelled surfaces and observed samples. Where possible, vertices on a mesh surface are moved to provide a clear delineation, for instance, between ore and waste material across the boundary based on spatial and compositional analysis; using assay measurements collected from densely spaced, geo-registered blast holes. The maximum a posteriori (MAP) solution ultimately considers the chemistry observation likelihood in a given domain. Furthermore, it is guided by an apriori spatial structure which embeds geological domain knowledge and determines the likelihood of a displacement estimate. The results demonstrate that increasing surface fidelity can significantly improve grade estimation performance based on large-scale model validation.

cs.CE↗

arXiv2016

Hyperspectral CNN Classification with Limited Training Samples

Hyperspectral imaging sensors are becoming increasingly popular in robotics applications such as agriculture and mining, and allow per-pixel thematic classification of materials in a scene based on their unique spectral signatures. Recently, convolutional neural networks have shown remarkable performance for classification tasks, but require substantial amounts of labelled training data. This data must sufficiently cover the variability expected to be encountered in the environment. For hyperspectral data, one of the main variations encountered outdoors is due to incident illumination, which can change in spectral shape and intensity depending on the scene geometry. For example, regions occluded from the sun have a lower intensity and their incident irradiance skewed towards shorter wavelengths. In this work, a data augmentation strategy based on relighting is used during training of a hyperspectral convolutional neural network. It allows training to occur in the outdoor environment given only a small labelled region, which does not need to sufficiently represent the geometric variability of the entire scene. This is important for applications where obtaining large amounts of training data is labourious, hazardous or difficult, such as labelling pixels within shadows. Radiometric normalisation approaches for pre-processing the hyperspectral data are analysed and it is shown that methods based on the raw pixel data are sufficient to be used as input for the classifier. This removes the need for external hardware such as calibration boards, which can restrict the application of hyperspectral sensors in robotics applications. Experiments to evaluate the classification system are carried out on two datasets captured from a field-based platform.

cs.CV↗

arXiv2013

Information fusion in multi-task Gaussian processes

This paper evaluates heterogeneous information fusion using multi-task Gaussian processes in the context of geological resource modeling. Specifically, it empirically demonstrates that information integration across heterogeneous information sources leads to superior estimates of all the quantities being modeled, compared to modeling them individually. Multi-task Gaussian processes provide a powerful approach for simultaneous modeling of multiple quantities of interest while taking correlations between these quantities into consideration. Experiments are performed on large scale real sensor data.

stat.ML↗

arXiv2006

Twelve pure shear surface waves guided by bimaterial clamped/free boundaries in magneto-electro-elastic materials

It is shown that surface waves with twelve different velocities in the cases of different magneto-electrical boundary conditions can be guided by the interface of two magneto-electro-elastic half-spaces. The plane boundary of one of the half-spaces is clamped while the plane boundary of the other one is free of stresses. The twelve velocities of propagation of these surface waves and the corresponding existence conditions are obtained in explicit forms. It is shown that the material coefficients of the half-space which has a clamped boundary have only quantitative influence on the surface waves and that the existence and absence of the possibility for the surface waves to be guided by the interface is determined by the material coefficients of the half-space which has a free boundary. The number of the possible different surface wave velocities decreases from 12 to 2 when the magneto-electro-elastic materials are changed to piezoelectric materials.

physics.gen-ph↗

arXiv2006

Twelve shear surface waves guided by clamped/free boundaries in magneto-electro-elastic materials

It is shown that surface waves with twelve different velocities in the cases of different magneto-electrical boundary conditions can be guided by the interface of two identical magneto-electro-elastic half-spaces. The plane boundary of one of the half-spaces is clamped while the plane boundary of the other one is free of stresses. The 12 velocities of propagation of these surface waves are obtained is explicit forms. It is shown that the number of different surface wave velocities decreases from 12 to 2 if the magneto-electro-elastic material is changed to a piezoelectric material.

physics.gen-ph↗

arXiv2006

New pure shear elastic surface waves in magneto-electro-elastic half-space

Pure shear surface waves guided by the free surface of a magneto-electro-elastic material are investigated. Three surface waves are obtained for various magneto-electrical conditions on the free surface of the magneto-electro-elastic half-space. The velocities of propagation and the existence conditions for each of these waves are obtained in explicit exact form.

physics.gen-ph↗

arXiv2006

Eleven shear surface waves guided by inclusions in magneto-electro-elastic materials

Eleven pure shear surface waves are shown to exist in magneto-electro-elastic materials, which contain different plane inclusions inside them. The plane inclusions affect the electric and magnetic fields only, so that the medium remains mechanically continuous across it. Expressions for velocities of propagation of these 11 surface waves are obtained in explicit forms. It is expected that these waves will have several applications in surface acoustic wave devices.

physics.gen-ph↗

arXiv2006

New pure shear acoustic surface waves guided by cuts in magneto-electro-elastic materials

It is shown that new pure shear acoustic surface waves with five different velocities can be guided by stress free plane cuts with different magneto-electrical properties in magneto-electro-elastic materials. The possibility for the surface waves to be guided by a cut in pairs, which is reported in this paper, is new in magneto-electro-elastic materials and has no counterpart in piezoelectric materials. The five velocities of propagation of the surface waves are obtained in explicit forms. It is shown that the possibility for the surface waves to be guided in pairs disappears and the number of surface waves decreases from 5 to 1 if the magneto-electro-elastic material is changed to a piezoelectric material.

physics.gen-ph↗