Strength in Numbers
Exploration discovery rates are down, while budgets are up. Can technology, deeper research and new interdisciplinary alliances improve the numbers and reverse the trend?
By Russell A. Carter, Contributing Editor
That’s because recent numbers aren’t good. Over the past decade, major deposit discovery rates in some major metal categories have dropped to zero, while the cost of exploration activities continues to rise. Potential finds may be under deeper cover, located in more-remote regions, and as the recent abduction and killing of a Canadian geologist in Burkina Faso illustrates, rising occurrences of local conflict and political unrest heightens the risk level. Major producers are under pressure to replace reserves depleted by mining, but discovery rates and tonnages have dropped to levels that observers predict won’t allow producers to maintain production levels in the not-too-distant future. Copper, for example, seems particularly threatened by this trend.
Kevin Murphy, senior research analyst at S&P Global Market Intelligence, noted in a recent report that “… increased copper exploration budgets have so far failed to identify more new discoveries, with only about 140 million metric tons (mt) of copper defined in 29 discoveries over the past 10 years, compared with 862.8 million mt in 191 discoveries in the preceding 18 years.” According to his report, current lead times for copper mines are about 20 years for an asset to advance from discovery to production: This timeline “implies that the reduced discovery rates of the past decade will limit the pool of projects that could come online in 15 to 20 years, and there could be a lack of quality assets available for development in the longer term.”
Needed: Data Diligence
One of the traditional hindrances to exploration success is that essential information is almost always subject to interpretation, leading to subjectively based decisions and biases. “Important” numbers can range from stats as clear as grams-per-ton results from drill-core analysis to fuzzy estimates of project risk evaluations based on everything from a target’s geological setting and mineralization characteristics to commodity prices and country risk. Any significant item, or range of items, that is mistranscribed, misinterpreted or simply missed can alter the overall picture available to an exploration team — and yet, the amount of data requiring analysis is growing at a remarkable rate. The latest technologies and instruments available for core logging, for example, can generate as much as 122 gigabytes of data from a single, typical drill hole.
Technological improvements in drilling and sampling tools, field data collection systems, global and local communications, computing power and data visualization should enable exploration teams and managers to make quicker, better-informed decisions. If they can meet the challenge of turning a rising tide of available data into a pool of useful information, free from subjective influences and in a standardized format. There is a rapidly expanding universe of hardware, software, and data-service tools and resources designed to help companies solve this many-faceted problem. Solutions range from systems and software that allow exploration teams to bypass manual- entry-on-forms and eliminate paper maps and documents altogether, to Artificial Intelligence (AI) technologies that not only can be trained to read, digest and make recommendations from myriad data sources but also can go back in time, if necessary, to reinterpret historical data and reports in accordance with more recent developments and insights.
Gabbitus said Micromine’s focus on exploration began in the early 2000s with the release of Field Marshal, a field data capture product designed to replace paper log sheets, and has continued into the present with recent releases of Geobank Mobile, which facilitates integration with data capture devices such as magnetic susceptibility readers, portable XRFs and geotechnical measurement devices. These devices, according to Gabbitus, are often connected to a service or database in the cloud, but “someone, somewhere still needs to merge it into the master database at some point.” Issues also can arise when there is no network connectivity, a common issue on the edge.
“If an exploration geologist can collect, validate and access their data in a single platform, while they are in the field, real-time insights can be drawn, enabling smarter decisions at the rig,” he said. “Having this data available would then allow a geologist to make decisions in the field, to end a hole early or justify extending a hole beyond its planned depth. This adds immediate value, either by reducing planned drilling costs or preventing costly re-drills later.”
The computing power available to field personnel in a ruggedized format has improved to the point where not only can a geologist run Geobank Mobile, said Gabbitus, but also can run Micromine, the company’s flagship modular exploration and mine design suite, on a tablet.
Join the SRK.IS
Maps have always been essential in the exploration process, likely starting with crude “X marks the spot” diagrams used by prospectors in past centuries and progressing to the detailed prospect maps produced by exploration teams today. But, as Jason Beltran, a senior GIS consultant for SRK Australia, pointed out in a recent exploration newsletter, traditional paper maps are static, limited to the size of the paper on which they are printed, and difficult to read when they contain dense detail. The solution: either split a map into separate sheets or remove some of the features, which lessens the map’s usefulness.
Now, in a digitally connected world, smart devices can be used for collecting and sharing mapping data, enabling geologists to produce accurate digital maps collaboratively and more quickly, and without the limitations of size and scale. SRK has leveraged a customized in-house mapping portal using ArcGIS technology initially developed by ESRI using IS for “information system,” it’s called SRK.IS (pronounced “circus”). SRK.IS works with Web apps that are customized for a particular client or project. Essentially, SRK.IS integrates digital data collection in the field with a centralized database, making it possible to access maps and other geographical information in real time and viewed by a variety of users simultaneously.
The advantage of SRK.IS, said Beltran, is that it provides different connection options, whether the data is collected in the field or viewed and/or edited in a client’s office. Using ESRI’s ArcMap or ArcPro, the data is transferred to maps in the SRK.IS Web app and published as a map service to SRK.IS, so users can interact with the map service. The level of accessibility is subject to the map service requirements and user permission settings.
For collecting data, SRK.IS connects to a dedicated ESRI application called Collector for ArcGIS, which works on Android, iOS and Windows mobile devices. Data are customized according to the requirements of the mapping task, and data pre-filling options can be activated to save time and reduce exposure to human error in data entry. Digital photos can be attached to a datapoint to automatically georeference the location of each photo.
Users can download maps beforehand, so data can be collected in remote locations with no internet or mobile connection. Later, when internet connection is available, the offline data can be synced to SRK.IS and integrated with existing data.
The data on SRK.IS is accessed via a portal, using a Web browser, or by a direct Web App URL. The Web browser interface works like the Google Earth application by toggling between different map layer options, users can control the geospatial information to view. Office-based ArcGIS administrators can log in to the SRK.IS portal to view mapping progress in real time. The data can be edited and synced back to the SRK.IS user in the field, reducing any delay from waiting until the map is produced before processing any edits and enabling faster map creation than previously possible.
The latest generation of handheld or transportable XRF analyzers, which allow quick identification of minerals on-site, work well with digital data-capture solutions that eliminate paper-based forms and documentation and provide quick information turnaround. These include units such as Malvern Panalytical’s ASD Terraspec Halo, Spectral Evolution’s oreXpress and SR-6500, Bruker’s S1 Titan and Thermal Fisher Scientific’s line of Niton analyzers. X-ray fluorescence, or XRF, is a process whereby electrons are displaced from their atomic orbital positions, releasing a burst of energy that is characteristic of a specific element. This release of energy is then registered by the detector in the XRF instrument, which in turn categorizes the energies by element.
Handheld analyzers are convenient and adequate for a range of in-the-field identification requirements, but if greater accuracy, flexibility and worker safety (protection from accidental X-ray exposure) are desired, a range of so-called benchtop analyzers are available as well. These can vary in size and configuration from units the size of a laser printer that can be used and stowed in the back of a vehicle or in a site office, to trailer or containerized setups that are touted as “laboratories in the field.”
Recent offerings in this area include Boart Longyear’s TruScan, comprising a trailer-mounted module fitted with sample handling and XRF scanning equipment. According to the company, TruScan is designed to provide same-day continuous analysis of the drill core and provide non-destructive, accurate, high-density elemental concentration data. Boart Longyear’s Drilling Services utilizes TruScan for elemental and photo scanning of core at the exploration site, providing geologists access to real-time geological data as the core is drilled.
In a similar vein, the Swedish-Australian company Orexplore launched the Geo- Core X10, a digital drilling cores laboratory, which analyzes element concentrations and minerals contained in a drill core and provides a 3D representation of the rock’s internal structure. Orexplore said the Geo- Core X10 is designed to work in remote locations and has several flexible options for data storage and data transfer. Normally, the data generated by the GeoCore X10’s high-resolution scanning engine is sent to a central server where it can be accessed by Orexplore’s Insight analysis software. At sites that have access to a fast network connection, this is done automatically. In locations where there is either a very slow or no network available, the GeoCore X10 can store data locally on removable SSD (Solid State Drive) hard drives, which can then be transported and uploaded to a central server when a fast network is available. The drives also can be shipped directly to an Orexplore office for immediate uploading.
Recognizing the need for overall improvements in exploration data management and analysis capabilities, IT companies both large and small see it as an opportunity for application of Artificial Intelligence (AI) technology. Goldcorp and IBM, for example, have been collaborating since 2017 on a project aimed at teaching IBM’s Watson AI platform to “think like a geologist” in order to make AI-based recommendations on where the company should focus its exploration efforts in the Red Lake gold district in Ontario, Canada. Other companies, such as Goldspot Discoveries, headquartered in Toronto, Ontario, and Earth AI, an Australian company, are recent startups that apply a branch of AI known as machine learning to develop and refine mineral targeting systems that more effectively handle and analyze the growing volume of data associated with exploration programs.
Describing the Goldcorp/IBM AI collaboration, Mark Fawcett, partner, Global Business Services at IBM, said, “One of the biggest challenges facing geologists is interpreting the vast amount of data that includes field mapping data, geochemical surveys, drill hole data, geophysical surveys, geological maps, Landsat imagery, aerial photographs, mine level plans, alteration models, resource model data and reports in order to make the best decision on where to drill next.
“Inputting that information into Watson and educating Watson on the process of exploration, gives the system the capacity to make more informed exploration decisions to improve the probability of discovery.” The first phase of the IBM/Goldcorp program involved providing Watson with structured data from a wide range of sources, such as geophysical and geological surveys, drillhole datasets, reports, academic papers and conference proceedings, providing insights into Red Lake’s geology, current and historic mining and exploration activities and successful exploration techniques.
“Phase two is where we start to educate Watson to refine and build the exploration model the system will use to make predictions and provide recommendations on where Goldcorp should direct its exploration activities,” said Dariusz Piotrowski, global leader of cognitive and analytics development for Natural Resources at IBM.
Exploring New Partnerships
In addition to in-house corporate initiatives aimed at improving exploration efficiency and chances for success, companies are forming agreements and alliances with universities and research organizations with an eye toward expanding and/or refining exploration techniques and knowledge. As an example, a new four-year, $3.6 million research partnership between the University of Western Australia (UWA) and Rio Tinto Iron Ore will lead to improved efficiency in geological modelling, through innovative data science solutions.
Building on a collaboration that started in 2010, a new partnership called Data Fusion Projects involves UWA’s geodata algorithms team working with a Rio Tinto Iron Ore team led by Resource Evaluation Manager Thomas Green. Green said the challenge faced by Rio Tinto’s resource evaluation group was to quickly and consistently interpret and integrate increasing volumes of different types of data collected. “We were actively seeking automated solutions not to replace but to assist our interpretation to model geology and resource, which can be inconsistent and uncertain,” he said. The partnership involves diverse themes including the development of machine-learning-based methods and tools to integrate diverse drill hole data to model stratigraphy, their material compositions and geomechanical proxies for resource evaluation and mining.
The research also aims to incorporate advanced machine learning methods to improve certainty in modelling the spatial extent of subsurface geological interfaces. The team will develop image analysis and visualization methods and tools to assist the interpretation of large volumes of 2D and 3D data from satellites and drones for planning and geological mapping.
UWA and Rio Tinto’s past collaboration resulted in UWA’s commercialization of automated downhole image analysis software in 2015, and a RTIO-driven joint patent application in 2017 on Automated Validation Assistant (AVA) for geological and mineralogical composition logging from rock samples from drill holes using machine learning. In North America, Vancouver Island University (VIU) recently announced a mapping-related research project focused on the glacial landscape of Canada’s North regions. The project, funded by Natural Resources Canada’s Earth Sciences Sector, is aimed at assisting in the development of better remote predictive mapping (RPM) methods for mineral exploration.
“Traditional methods of surficial mapping, employing aerial photographs and field verification, are both time-consuming and expensive,” said Brad Maguire, a professor in VIU’s Geography department. He and Professor Jerome Lesemann of the school’s Earth Sciences department are overseeing the project. The research project aims to develop a methodology for computerized detection of the sediment components of eskers — ridges of gravel and sand, which occur in formerly glaciated regions of northern Canada, and which can host diamond deposits and other valuable minerals.
Currently, RPM is a promising avenue of semiautomated mapping using widely available digital datasets like multispectral satellite imagery. “However, there are gaps in the methodology,” said Lesemann. “Part of the problem is that the type of imagery used to date gives us information about spectral characteristics of the surface, which reflects mostly the type of material on the surface, like bedrock or sand and gravel. The imagery does not contain information about the three-dimensional shapes of landforms.”
The VIU project team proposes to develop an esker element detection methodology based on deep machine learning supported by a Convoluted Neural Network (CNN). CNN uses computer algorithms to try and replicate complex cognitive processes of the human brain. “We will be using CNN to identify eskers from newly available, high-resolution digital elevation models (DEM) of the Canadian Arctic,” Maguire said. Lesemann explained the aim is to train a computer to recognize patterns. “The form and structure of eskers are complex and if we can teach a computer to learn what an esker looks like, we may then be able to identify other eskers automatically,” he added.
Back in Australia, the newly established MinEx Cooperative Research Centre (CRC) began operating last July at the University of South Australia and in Western Australia. Supported by a $50 million grant from the Australian government and more than $150 million in cash and in-kind support from industry participants, the MinEx CRC is tasked to develop cost-effective and eco-friendly mining technologies related to in-field sensing and real-time data analytics.
David Giles, chairman of Minerals and Resources Engineering at the Future Industries Institute, University of South Australia, and chief scientific officer for the CRC, said the objective is to enhance the efficiency of minerals exploration nationally. “In the Australian context, the cost of exploration for new deposits has risen over the past 30 years and our success rate has declined,” Giles said, noting that cheaper and more effective drilling technologies have the potential to improve the discovery and affordability of identifying new mineral deposits. MinEx CRC is tasked to implement the National Drilling Initiative (NDI), a collaboration of government geological surveys, researchers and industry that will undertake drilling in underexplored areas of potential mineral wealth.
Another part of of MinEx CRC’s focus is to extend the capability of technologies such as Coiled Tubing (CT) drilling so it can drill deeper, is steerable and delivers the highest quality sampling. CT technology for deep rock exploration, developed by Deep Exploration Technologies CRC, holds promise of drilling at 20% of the cost of conventional diamond drilling and has been tested at a Barrick Gold site in Nevada, USA.
In Europe, a consortium of research institutions and commercial interests have joined in the X Mine project to look for increases in exploration efficiency by developing equipment for scanning drill core samples on site using new, highly sensitive layered imaging technology based on X-ray fluorescence, as well as composition analyses. The consortium conducted rock classification trials in late 2018 comparing the mineralogical results obtained from Orexplore’s XRF detector, Advacam’s XRT detector and Antmicro’s 3D camera.
The X Mine project, with funding through Horizon2020, is an EU research and development program funded with 80 billion to award to European research initiatives over a seven-year period (between 2014 and 2020). It is based on international cooperation between research institutions from Finland, Sweden and Romania, sensor and equipment manufacturers from Finland, Poland, the Czech Republic and Sweden, and end users such as mining companies in Bulgaria, Greece, Cyprus and Sweden, along with Australian drilling services and equipment supplier Swick Mining Services.