Periodic Reporting for period 1 - m4mining (m4mining: Multi-scale, Multi-sensor Mapping and dynamic Monitoring for sustainable extraction and safe closure in Mining environments)
Okres sprawozdawczy: 2023-01-01 do 2024-06-30
Access to a range of mineral raw materials is critical in key areas of the European economy. Earth observation technologies have high potential for transforming the effectiveness of the mining industry, to improve operational efficiency and help to reduce energy and environmental footprints.
The m4mining project is designed to transform how we map and monitor both active and inactive mining sites using advanced earth observation technologies. By combining high precision three-dimensional measurements with imaging spectroscopic techniques, the project aims to accurately identify and characterize different minerals at mining sites. This mapping will be achieved using drones and satellite sensors.
The project focuses on integrating data from drones and satellites to make quicker and better decisions that can improve mining operations and manage environmental risks. A special drone system is under development that uses advanced imaging and computing technology to capture detailed mineralogical information about open pits, mine waste areas, and tailings dams, including steep surfaces. This system will process data in real-time and share initial findings with on-site workers, allowing for immediate and interactive decision-making even while the drone is still in the air. The high-quality data collected will also be integrated with satellite data for broader coverage and periodic observations.
This innovative approach is expected to bring significant improvements to mining operations, such as better sorting of materials, assessing material quality, extracting valuable secondary materials, and monitoring environmental impact. The m4mining project aims to reduce energy consumption by providing faster responses for mine operators and promoting the digitalization of mining processes.
The developments in the project will make the technology available to customer groups who would traditionally need dedicated researchers to interpret and visualize the results. The increased user-friendliness, fully automatic processing, rapid provision of results, and focus on integrable data products into the existing mining software and planning structures are expected to open the technology to nonexpert users. To prove and promote the effectiveness of this technology, it is tested in real-world conditions at mining sites in Cyprus, Greece, and Australia. Potential end users will be invited to demonstrations towards the end of the project.
The m4mining project is designed to transform how we map and monitor both active and inactive mining sites using advanced earth observation technologies. By combining high precision three-dimensional measurements with imaging spectroscopic techniques, the project aims to accurately identify and characterize different minerals at mining sites. This mapping will be achieved using drones and satellite sensors.
The project focuses on integrating data from drones and satellites to make quicker and better decisions that can improve mining operations and manage environmental risks. A special drone system is under development that uses advanced imaging and computing technology to capture detailed mineralogical information about open pits, mine waste areas, and tailings dams, including steep surfaces. This system will process data in real-time and share initial findings with on-site workers, allowing for immediate and interactive decision-making even while the drone is still in the air. The high-quality data collected will also be integrated with satellite data for broader coverage and periodic observations.
This innovative approach is expected to bring significant improvements to mining operations, such as better sorting of materials, assessing material quality, extracting valuable secondary materials, and monitoring environmental impact. The m4mining project aims to reduce energy consumption by providing faster responses for mine operators and promoting the digitalization of mining processes.
The developments in the project will make the technology available to customer groups who would traditionally need dedicated researchers to interpret and visualize the results. The increased user-friendliness, fully automatic processing, rapid provision of results, and focus on integrable data products into the existing mining software and planning structures are expected to open the technology to nonexpert users. To prove and promote the effectiveness of this technology, it is tested in real-world conditions at mining sites in Cyprus, Greece, and Australia. Potential end users will be invited to demonstrations towards the end of the project.
Functional and performance requirements for the full drone system were established and reported, along with safety guidelines. A first version of the drone with imaging and 3D mapping sensors was developed and demonstrated. Onboard algorithms for navigation and path planning were created, with further implementation and testing continuing into the project's second phase.
Software for providing near-real-time (i.e. same day) results from drone-acquired data was developed, validated, and presented. A simulator of real-time processing based on test data was designed to develop real-time processing software.
Meetings with different potential end-users provided input to the development of a visualization system. Data flow and storage solutions were developed, and the visualization of the 3D surface mapping component was demonstrated.
Data was collected at two mining sites (Australia and Cyprus). Satellite and drone hyperspectral images, in situ spectral measurements and geochemical sampling provided the necessary data for testing of equipment and validation of workflows and algorithms. The drone/satellite data products were interpreted in the context of surface mineralogy at the sites.
Algorithms producing mineral maps must be transferable across different scales and resolutions of satellite and drone images. Algorithms were compared to identify the most suitable ones. The conclusions were documented, and suitable algorithms were selected for further use.
To maximize industry impact, a Minerals Industry Advisory Board (MIAB) was established. Interaction with the MIAB and an in-depth review of current practices led to a report clarifying the difference between state-of-the-art methods and expected improvements from this project.
Software for providing near-real-time (i.e. same day) results from drone-acquired data was developed, validated, and presented. A simulator of real-time processing based on test data was designed to develop real-time processing software.
Meetings with different potential end-users provided input to the development of a visualization system. Data flow and storage solutions were developed, and the visualization of the 3D surface mapping component was demonstrated.
Data was collected at two mining sites (Australia and Cyprus). Satellite and drone hyperspectral images, in situ spectral measurements and geochemical sampling provided the necessary data for testing of equipment and validation of workflows and algorithms. The drone/satellite data products were interpreted in the context of surface mineralogy at the sites.
Algorithms producing mineral maps must be transferable across different scales and resolutions of satellite and drone images. Algorithms were compared to identify the most suitable ones. The conclusions were documented, and suitable algorithms were selected for further use.
To maximize industry impact, a Minerals Industry Advisory Board (MIAB) was established. Interaction with the MIAB and an in-depth review of current practices led to a report clarifying the difference between state-of-the-art methods and expected improvements from this project.
Today's mine planning and open pit mining operations rely on manual interpretation of geology, visual and geophysical logging of drill cores, low-resolution surveys, multispectral satellite data, and sampling of rock properties. These methods are time-consuming and don't offer a complete picture of mineral distribution in active mines and tailing surfaces.
Spectral remote sensing which uses the unique signatures of materials in reflected light enables mineral mapping at various scales, including site-specific. However, this technique is currently used by only a few mining service providers. None include high-resolution mine-face scanning, correction, and data calibration from drone platforms, and no commercial solutions offer real-time processing results.
The results achieved should be compared to this background. Within the first 18 months of the project, m4mining has already achieved several results that are beyond the current state of the art. Notably, we have developed a functional drone system equipped with sensors for imaging/mineral classification and 3D surface mapping. While the individual components are commercially available, the integration for side-viewing data acquisition on steep surfaces and real-time analysis required research and custom development. Furthermore, we have developed a three-dimensional surface mapping system to accurately map the mineral distribution of commonly steep and complex faces of open pit benches, stock and mine waste piles, as well as tailings dams.
One reason that the reflectance imaging technique for material characterization hasn't been widely adopted in mining operations is the prohibitively long and complex data processing. To address this, we have developed software that delivers near real-time results (same day) without requiring dedicated experts for data interpretation, representing another advancement beyond the current state of the art.
Finally, we have identified suitable algorithms for real-time and near-real-time mineral classification, capable of handling the scale differences between drone and satellite imagery. This achievement contributes to establishing a standard practice for analyzing such data in the mining industry.
Spectral remote sensing which uses the unique signatures of materials in reflected light enables mineral mapping at various scales, including site-specific. However, this technique is currently used by only a few mining service providers. None include high-resolution mine-face scanning, correction, and data calibration from drone platforms, and no commercial solutions offer real-time processing results.
The results achieved should be compared to this background. Within the first 18 months of the project, m4mining has already achieved several results that are beyond the current state of the art. Notably, we have developed a functional drone system equipped with sensors for imaging/mineral classification and 3D surface mapping. While the individual components are commercially available, the integration for side-viewing data acquisition on steep surfaces and real-time analysis required research and custom development. Furthermore, we have developed a three-dimensional surface mapping system to accurately map the mineral distribution of commonly steep and complex faces of open pit benches, stock and mine waste piles, as well as tailings dams.
One reason that the reflectance imaging technique for material characterization hasn't been widely adopted in mining operations is the prohibitively long and complex data processing. To address this, we have developed software that delivers near real-time results (same day) without requiring dedicated experts for data interpretation, representing another advancement beyond the current state of the art.
Finally, we have identified suitable algorithms for real-time and near-real-time mineral classification, capable of handling the scale differences between drone and satellite imagery. This achievement contributes to establishing a standard practice for analyzing such data in the mining industry.