Mineral Exploration Symposium

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Ambient noise, surface-wave tomography (ANSWT) is now a routine technique for imaging crustal and upper mantle structure at a regional scale. Its cost efficiency and environmental friendliness also make ANSWT an attractive method for mineral exploration. However, the application of the technique in mineral exploration requires the retrieval of wide-band, high-frequency surface waves from seismic noise, so as to obtain high-resolution images of shallow structures. We present a new workflow optimized to extend the bandwidth of high-frequency surface waves retrieved. It comprises short time-window stacking, cross-coherence and an improved phase velocity measurement method. We tested the workflow on data from a large-N array over a Cu-PGE deposit in Ontario, Canada, and successfully measured phase velocities for numerous inter-station pairs in a broad frequency ranges from 0.55 Hz to 23.8 Hz. Analysis of the phase velocity maps reveals a west-dipping high-velocity anomaly that matches the west-dipping, multi-staged gabbro intrusions associated with the deposit.

High-resolution seismic images of mineral deposits and their host rocks are an important asset for geological modeling and interpretation as well as for a reliable estimate of their economic potential. We present a tailored workflow for imaging of seismic data acquired for mineral exploration, comprising data pre-processing, construction of a velocity model by full waveform inversion and generation of a seismic depth image using focusing pre-stack depth migration techniques. This approach can handle challenging subsurface environments typical for hardrock environments, e.g. strong velocity contrasts in the shallow overburden or steeply dipping structures. We show a successful application of these techniques in two case studies, one using 3D seismic data acquired at the Ludvika iron-oxide mining site of Nordic Iron Ore AB in Sweden and the other one using 2D and sparse-3D seismic data sets from the Kylylahti polymetallic mine site of Boliden in Finland. Here, the application of this workflow is shown for controlled-source (active) seismic data but can be equally well applied to passive seismic data sets.

Lithium, cobalt and natural graphite are essential for energy storage technologies. Their demand is expected to surge with the increasing electrification in the transport sector. As they are all produced outside Europe, their supply for the European industry is potentially a threat. To address this, the FRAME project has produced prospectivity maps for Li, Co and Gr, to identify high potential mineral areas in Europe. They were calculated with the Cell Based Association (CBA) method, developed to better manage uncertainties related to cartographic data, often significant at continental scale. The base principle is to identify the associations of evidential features in the cells of a regular grid. Several techniques for scoring the favourability of the cells were tested. A prospectivity map was then calculated for each commodity (Co, Li and Gr) with the 1:1.5 million geological map of Europe, a regular grid of 10 by 10 km cells covering the whole Europe and the most performant scoring technique. These maps highlights favourable areas for the discovery of new energy critical elements deposits in Europe, and are valuable outputs of the FRAME project that help assessing highly prospective areas where mineral exploration should be focused in the coming years.

The use of Deep Learning technologies to analyze unstructured data, for instance images and texts, has recently known significant improvement and democratization. As a contribution to the digital transformation of the mining industry, we present three practical use cases where these technologies were successfully applied to geological data. They come from a petroleum exploration context, but illustrate the potential of Deep Learning to optimize geological activities. In this study we aim at discussing how much these business cases can relate to mineral exploration, and whether the Deep Learning revolution could also benefit mineral exploration workflows. The first case is a lithological classification of macroscopic rock samples pictures, which could be extended to automated core interpretation and foreshadows autonomous mining robots. The second case is a delineation of micro-fossils on thin sections scans, which could be adjusted for accelerated detection of mineral grains or metallic nuggets. The third case is a scientific texts mining based on an entity extraction engine, which could be used for assisted deposit characterization from geological literature. Eventually, we conclude that fantastic innovation opportunities lie in the integration of Deep Learning technologies into mineral exploration workflows.

The latest development in the AFMAG family, MobileMT airborne electromagnetic technology based on natural fields, has been used in commercial and test surveys in different geoelectric conditions and for different exploration problems. The presented results are from surveys in a uranium mineralization district, over a gold bearing orogen structure, and in a greenstone belt with the bounded iron formation type of gold mineralization demonstrate wide capabilities of the technique in subsurface exploration which include:

• wide depth range of investigation and deep resistivity imaging even in the presence of conductive overburden;
• mapping geoelectric boundaries from steeply dipping to stratiform horizontal directions;
• targeting discrete resistivity heterogeneity;
• differentiation in high and low resistivity background ranges.

The UNEXUP project, funded under EIT RawMaterials, aims to commercially deploy a novel mineral exploration and mine mapping service based on the UNEXMIN technology – the predecessor project. The service intends to address the needs and requirements of mining companies, mine owners, geological surveys and other sectors that can benefit from an underwater surveying campaign with geoscientific scope. The core objectives of UNEXUP include a) Improvements in the UX-1 robotic platform developed within UNEXMIN, 2) Build an additional, more complex robot, with further capabilities and sensors; 3) Test the robots’ performance in different pilot tests, 4) Bring commercial interest to the technology and launch it to the market.

The current version of the robot, to be further developed and tested on software, hardware and capabilities in the second part of 2020, is already equipped with enough navigation and geoscientific instrumentation to perform exploration missions in underground flooded environments. The instrumentation includes visible light cameras, multispectral unit, UV cameras, gamma-ray counter, pH and Eh devices, thermometer, water sampler, scanning sonar and structured light systems.

A second robot will be designed and built in the second year of the project, which will further extend the exploration possibilities of the UNEXUP technology.

SOLSA is the first automated expert system for on-site cores analysis. The scope is to provide a prototype to be an innovative and necessary tool for geo-metallurgy, in order to optimize the valorization of the ore. The Expert System consists in the combination of an integrated drilling rig providing cores of high quality, an automated scanner and phase identification software, developed for nickel laterites and bauxites but usable as well in other sectors. SOLSA combines non-destructive sensors and the whole system is driven by an innovative, user-friendly and intelligent software. SOLSA provides more complete information while optimizing the exploration stage, with a significant reduction of costs and return time. Such objective involves, in the first place, to fast, cheaper and systematic acquisition of the data needed for optimizing the process. The adding value takes place first at the exploration or grade control stage, furnishing systematic characterization and regionalization of the different types of ore. Then such information can be used for improving the ore scheduling at the mining and processing stages, toward improving the recovery and efficiency of the processing.

This paper introduces a framework of site selection to inform the optimal location of test sites in mineral exploration based on the three dimensions of technical, environmental and social factors. A novel two-stage procedure is introduced, consisting of exclusion criteria and evaluation criteria. The first stage analyzes geophysical and spatial conditions of potential sites and determines a sub-set of sites meeting specified pre-conditions. Based on the remaining sites, the second stage proceeds to assess sites based on a list of evaluation criteria that are weighted following the analytical hierachical process (AHP) method. The result of this two-stage procedure is a comprehensive ranking of test sites that considers the integration of environmental and social factors that were omitted from previous research. The framework is of practical importance in light of a recent push to develop test sites in Europe for the mineral exploration industry. It is carefully structured to allow extension to other industries.

Graphite has been considered as a critical raw material by EU Commission and it is a key resource to meet the ‘Green deal’ and the sustainable mobility 2030 plan. The increase in electric vehicles, as well as the growing interest in the development of new methods of obtaining graphene, make graphite one of the strategic elements of the world economy in the coming years, being key in the energy transition and the objectives reduction of greenhouse gas emissions.

This work describes the use of Raman spectroscopy as an effective quick graphite identification and quality determination tool to be used in carbonaceous complex samples.