GI6.7 | Application of remote sensing and artificial intelligence for safe, sustainable and cost-efficient mining operations

GI6.7

EDI

European production and infrastructure depend on a supply of high-quality raw materials. Ensuring that the needed materials are produced responsibly in European mines guarantees sustainable supply and prevents European countries from becoming dependent upon imports from global markets. To support the development of the European mining industry through technological solutions integrating remote sensing (both satellite and aerial) and on-site recorded data is needed. One such solution is an H2020 GoldenEye project developed Goldeneye platform. Technologies involved, but not limited to are, artificial intelligence, Earth observations data (InSAR, RGB, multispectral), drone-based data (RGB, multispectral and hyperspectral imaging, electromagnetic and conductivity surveys) supported with extensive ground-truth sampling.

The purpose of the session is to gather experts, service providers, trial site providers and interested alike in highlighting the ongoing research, sister projects and their applications on remote sensing and artificial intelligence for safe, sustainable and cost-efficient mining operations.

The conveners encourage both applied and theoretical contributions, together with how the trial sites can support the application development.

This session is organised in the frame of the Horizon 2020 co-funded project GOLDENEYE, which has received funds through Grant Agreement 869398.

Co-organized by NH6
Convener: Jari JoutsenvaaraECSECS | Co-conveners: Kamen Bogdanov, Sanna Uusitalo
Orals
| Mon, 24 Apr, 10:45–12:30 (CEST)
 
Room 0.51
Posters on site
| Attendance Mon, 24 Apr, 14:00–15:45 (CEST)
 
Hall X4
Posters virtual
| Attendance Mon, 24 Apr, 14:00–15:45 (CEST)
 
vHall ESSI/GI/NP
Orals |
Mon, 10:45
Mon, 14:00
Mon, 14:00

Orals: Mon, 24 Apr | Room 0.51

Chairpersons: Jari Joutsenvaara, Kamen Bogdanov, Julia Puputti
10:45–10:50
10:50–11:20
|
EGU23-17075
|
GI6.7
|
solicited
|
Highlight
|
On-site presentation
Marko Paavola

The Goldeneye project has implemented a unique combination of remote sensing and positioning technologies, exploiting Earth observation and Earth GNSS data, together with data fusion and processing powered by data analytics and machine-learning algorithms. The platform allows satellites, drones and in-situ sensors to collect high-resolution data, which can be processed and converted into actionable intelligence for safety, environmental monitoring and overall productivity, allowing more efficient exploration, extraction and closure. These tools have been demonstrated in 5 field trials in Germany, Bulgaria, Romania, Kosovo and Finland, and the initial results show significant time and cost savings, even up to 80%, for example, in exploration and mine safety, environmental and operations reporting. The project has a duration of 3,5 years and an EC funding of €8.36M. The multidisciplinary consortium includes industrial partners, SMEs, academic/research centres and end-users.

The project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No [869398].

How to cite: Paavola, M.: Goldeneye –a multisource AI-enabled earth observation platform for mining applications, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17075, https://doi.org/10.5194/egusphere-egu23-17075, 2023.

11:20–11:30
|
EGU23-9595
|
GI6.7
|
Highlight
|
On-site presentation
Francisco Gutierres, Taras Matselyukh, Marko Paavola, Florian Franziskakis, Paola De Salvo, and Felipe Carlos

To accomplish strategic objectives on zero-pollution, the entire mining life cycle (exploration, extraction, closure, mine- site rehabilitation) needs to develop minimal impact exploration and monitoring technologies and applications which are open to the broadest groups of stakeholders. In this respect Earth Observation (EO), Drone & Proximal Sensing and relevant in-situ data bring significant contribution for both, sustainable management of mineral resources and efficient multi-scale monitoring of mining impacts. In this sense, the purpose of the GEOMIN activity, part of the Group on Earth Observations (GEO) Work Programme [1] [2], is to increase awareness and use of state-of-the-art EO data and methods which represent a novel means for sustainable monitoring and management of mineral resources and efficient multi-scale monitoring mining impacts. 

How is GoldenEye project through AI-driven tools and applications enhances the GEOMIN community?

In the scope of the GoldenEye project, OPT/NET delivered the next generation of AI exploitation system: a hybrid platform which combines the processing & automation capabilities of AI with the natural problem-solving abilities of humans. We have developed dedicated applications with our novel approach based on the Artificial Intelligence Knowledge Packs (AI KPs), integrated in GOLDENAI Engine, to rapidly interpret the geographical patterns and environmental impacts caused by the mining activity.

What is the role of the GEO Knowledge Hub (GKH) as the Digital portal in promoting the replicability and re-usability of AI KP in the mining sector and how it relates to Goldeneye ?

The GEO Knowledge Hub (GKH) is a central cloud-based digital library providing access to Earth Observations applications developed by GEO. The GEO Knowledge Hub is part of the GEOSS Infrastructure and helps the  GEO to advance Open Knowledge. The scope of the GKH is to promote the replicability and re-usability of EO Applications by sharing with the end users, all the Knowledge Resources essential to fully understand and re-use them. All the Knowledge Resources are directly shared, curated and organized by the Knowledge Provider to ensure replicability with proper documentation.

Therefore, several Knowledge Packages (KPs) related to the technological solutions of the GoldenEye project can be found in the GKH. In this paper, the KP related to the GOLDENAI platform will be presented, including the description of the integrated AIKPs, such as:

  • AI KP for mineral mapping - Band-ratios based on WorldView-3 
  • AI KP for mineral mapping - SPCA based on WorldView-3
  • AI KP for UML clustering based on Copernicus Satellite imagery (Sentinel-1 SLC)

The paper has been prepared in the frame of the Horizon 2020 co-funded project GOLDENEYE, which has received funds through the GA 869398.

 

References:

[1] GEO (2023a). GEO Work Programme 2023-2025. Access 09 Jan. 2023. url:

 https://earthobservations.org/geo_wp_23_25.php 

[2] GEO (2023b). GEO WEEK 2022. Access 09 Jan. 2023. url: https://earthobservations.org 

How to cite: Gutierres, F., Matselyukh, T., Paavola, M., Franziskakis, F., De Salvo, P., and Carlos, F.: Discover the new approach to applications development with ‘Artificial Intelligence Knowledge Packs (AI KPs)’ in the GEO Knowledge Hub: Towards the Open and Reproducible Knowledge application for mine site monitoring, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9595, https://doi.org/10.5194/egusphere-egu23-9595, 2023.

11:30–11:40
|
EGU23-2597
|
GI6.7
|
Virtual presentation
Delira Hanelli, Andreas Knobloch, Jari Joutsenvaara, Julia Puputti, Ossi Kotavaara, Korab Tmava, Azem Rexhaj, and Ana Bautista Gascuena

The sulfidic sulfur contained in the host rocks and mining waste leads to strong acid mine drainage processes in the mining landscapes of Trepca, Kosovo and Pyhasalmi, Finnland. In the present, the water quality is usually monitored by discrete sampling and analysis of dissolved metal particles and other chemical parameters. Not only is this a cost- and time-consuming process, but the assessment takes place only on discrete locations.

The main aim of this application is to elaborate the suitability of multispectral remote sensing (R/S) data from different sensors for area-wide identification and quantitative mapping of Acid Mine Drainage (AMD) constituents such as dissolved iron concentration (Fe3+), pH value etc. in water bodies. The potential for mining waste to be subject to AMD processes is also being investigated through area-wide quantitative mapping of the sulfate content (SO₄2-) in solid ground.

In this framework, water and solid ground samples were collected to calibrate and validate the supervised machine learning algorithm of Artificial Neural networks (ANN), used for the identification of dependencies between the multispectral R/S data and the ground measurements. The ANNs of multilayer perceptron type (MLP) is implemented in the advangeo® 2D Prediction software from Beak Consultants GmbH (www.advangeo.com). The modelling and prediction software analyses complex non-linear relationships between a wide variety of spatial controlling parameters and natural complex processes or occurrences, by using methods of artificial intelligence within a Geographic Information System (GIS) environment.

In the mining landscapes of Artana 1 & 2 and Kelmend, AKG has allocated and analysed about 20 water samples and 15 soil samples between May – August 2022 in two field campaigns, whereas low pH values (3 – 4), dissolved iron concentrations up to 25 mg/L and sulfate contents up to 28474 mg/kg have been recorded. Because of the small-scale features in the mining landscapes, high-resolution multispectral images from Worldview-3 and time-series of drone-based acquisitions are used as controlling parameters for the modelling process.

In the tailing pond of Pyhasalmi and the surrounding water environment, the Oulu University has allocated and analysed about 60 water samples between June – October 2022 in two field campaigns. Low pH values (3 – 4), dissolved iron concentrations up to 1800 mg/L and sulfate contents up to 2200 mg/l have been recorded. In this case, medium-resolution multispectral images from Sentinel-2 (Level-1C TOA and Level-2A BOA products) and high-resolution images from Worldview-3 are used as controlling parameters for the modelling process.

In all scenarios, the imagery was acquired during similar time frames as the sampling, to ensure that the measured water / soil grounds parameters correspond to the surface reflectance information.

In the study, advantages and limitations of different multispectral imaging sensors are elaborated. The newly established dependencies from the ANN models can be used to perform area-wide monitoring of AMD processes in time-series, drastically reducing the need for terrestrial measurements in the future.

The paper has been prepared in the frame of the Horizon 2020 co-funded project GOLDENEYE, which has received funds through the Grant Agreement 869398.

How to cite: Hanelli, D., Knobloch, A., Joutsenvaara, J., Puputti, J., Kotavaara, O., Tmava, K., Rexhaj, A., and Bautista Gascuena, A.: AMD Monitoring using multispectral imaging from Worldview-3, Sentinel-2 and drone-based data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2597, https://doi.org/10.5194/egusphere-egu23-2597, 2023.

11:40–11:50
|
EGU23-8364
|
GI6.7
|
Virtual presentation
Guillem Domenech, Kamen Bogdanov, Daniel Nieto-Yll, and Azadeh Faridi

Interferometric Synthetic Aperture Radar (InSAR) has been applied using SAR images from the European Space Agency (ESA) Sentinel-1 constellation, in descending orbit, to obtain the terrain displacement by means of the Coherent Pixel Technique (CPT). This Persistent Scatter Interferometry (PSI) technique was developed in 2002 by the Remote Sensing Laboratory (RSLab) of the Universitat Politècnica de Catalunya, UPC (Lanari et al., 2004; Mora et al. 2002), and recently updated by the Dares Technology team.

ESA Sentinel-1 satellite constellation images were used with Single Look Complex (SLC) images and Interferometric Wide Swath (IW) acquisition mode to detect terrain displacements at Vlaykov Vruh and Tsar Assen porphyry-copper deposits (PCD) in the southern part of Panagyurishte ore district in Bulgaria.

The detected displacement magnitude in Vlaykov Vruh was from 500 to 4,000 m2 while for Tsar Assen PCD it ranges from 500 to 2,500 m2 where several spots of displacement were detected.

We conclude that in the waste pile area east of the Vlaykov Vruh slope instabilities occurred with a displacement of 3.5 cm. Due to a landslide along the fault structure, a slope displacement of about 4.0 cm for Tsar Assen PCD was detected.

The study is supported by the Horizon 2020 co-funded GOLDENEYE project, which has received funds through Grant Agreement 869398.

 

References:

Lanari, R.; Mora, O.; Manunta, M.; Mallorqui, J.J.; Berardino, P.; Sansosti, E. 2004. A small-baseline approach for investigating deformations on full-resolution differential SAR interferograms.’ IEEE Trans. Geosci. Remote Sens., 42, 1377–1386.

Mora, O.; Mallorqui, J.J.; Duro, J. 2002.Generation of deformation maps at low resolution using differential interferometric SAR data.’ Proceedings of 2002 IEEE International Geoscience and Remote Sensing Symposium, IGARSS ’02, Toronto, ON, Canada.

 

How to cite: Domenech, G., Bogdanov, K., Nieto-Yll, D., and Faridi, A.: Interferometric Synthetic Aperture Radar (InSAR) mapping in Vlaykov Vruh and Tsar Assen Cu-porphyry deposits, Panagyurishte ore region, Bulgaria, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8364, https://doi.org/10.5194/egusphere-egu23-8364, 2023.

11:50–12:00
|
EGU23-2669
|
GI6.7
|
On-site presentation
Kamen Bogdanov, Stefan Velev, and Ivan Krumov

Remote sensing UAV based technology combined with modern alteration mapping tools (SWIR, XRD Raman and XRF) for mineral detection has experienced great advances in the Cu-Au epithermal exploration targeting. We demonstrate quick and cost-efficient approach for epithermal gold exploration mapping and evaluation on examples of high-sulphidation epithermal Cu-Au deposits and prospects in the Panagyurishte ore district, Bulgaria.

The Panagyurishte ore district is part of the global Tethyan-Eurasian Cu-Au belt that developed during the Mesozoic as a copper-rich, andesite-dominated magmatic arc system characterized by obvious affiliation of porphyry-copper (PCD) and epithermal Cu-Au ore deposits with granodiorite and andesite dominated magmatic complexes. The target mapping has lacked high-resolution data to identify and prove the geometry of the alteration mineral assemblages and ore controlling fault structures. When distal sensing is combined with field mapping and proximal modern mineral detection methods such as SWIR (1300-2500nm) and Raman spectroscopy, XRD and ore petrography is more efficient tool for detection of hydrothermally altered minerals and zones by their diagnostic spectral signatures.

Drone based photogrammetry approach was applied for hydrothermal alterations mapping and targeting for Cu-Au epithermal deposits exploration in the Panagyurishte ore district, Bulgaria. Mineral alterations maps for Pesovets, Petelovo and Krassen Au-epithermal deposits was assembled using orthophoto model and TIR- 3D mapping to utilize the time and cost efficiency of the subsequent geological exploration field work. For classification and verification of drone orthophoto mosaic geological mapping and rock sampling was carried out in addition to XRF and XRD mapping and stream sediments sampling. UAV- based mapping with selected light bands was used to recognize different hydrothermal alterations styles such as advanced argillic (AAA), argillic (AA) propylitic (Prop) and phyllic (Phy) alteration styles that are overprinting andesitic volcanic sequences in the central part of Panagyurishte ore district. Radial and concentric fault structures and regional strike-slip fault zones have also been proved by UAV-based mapping. Domains of proximal hypogene AAA and AA and more distal propylitic halo as possible host of HS gold mineralization were clearly outlined. XRF mapping of the Pesovets lithocap indicate increasing of As (20-50ppm) and Ti (570-2400ppm) concentrations when approaching AA and AAA alteration domains and could also provide effective vectoring tool for targeting of epithermal Cu-Au mineralization.

 The recent study demonstrates UAV-based mineral mapping approach that will help to improve the exploration targeting and decision making in and eestimation of the Cu-Au mineral potential in cost-efficient manner.

The study is supported by the Horizon 2020 co-funded GOLDENEYE project, which has received funds through the Grant Agreement 869398.

How to cite: Bogdanov, K., Velev, S., and Krumov, I.: Remote-sensing applications for Au-epithermal deposits mapping and exploration targeting in Panagyurishte ore district, Bulgaria, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2669, https://doi.org/10.5194/egusphere-egu23-2669, 2023.

12:00–12:10
|
EGU23-4901
|
GI6.7
|
ECS
|
On-site presentation
Jari Havisto, Martin Köhler, Sanna Uusitalo, Marko Paavola, Andreas Knobloch, and Katariina Rahkamaa-Tolonen

The Goldeneye project combines remote sensing and positioning technologies with proximal sensing to produce reference calibrated mineralogical maps through data fusion. The project brings together optical satellite sensor data, drone sensor aerial data both optical and electromagnetic as well as spectral ground sensor data. Satellite data can offer spectral signatures of large areas but suffers from limited spatial resolution and blind spots where the higher resolution satellite data is not available. Drone data can offer more variety in spectral wavelengths with higher resolution but there are some drawbacks as well. Namely, NIR vibrational spectroscopy requires background information for successful mineralogical analysis. In addition, the most interesting SWIR range is challenging due to large and very expensive sensors. To cope with these challenges of aerial data, proximal sensing can be applied in locations where satellite imagery is not available, and it can also produce reference information for the calibration of the spaceborne and airborne instruments. The conventionally used analyses for producing mineralogical information from field collected samples are the mineral liberation analysis (MLA) and X-Ray diffraction (XRD) which require extensive sample preparations and are laborious and slow. Goldeneye-project has studied the use of time-gated Raman for easier and more practical production of reference data at the field sites as well as from field collected rock samples. The benefit of Raman is an accurate characteristic spectral fingerprint and an ability to distinguish small mineralogical features as the detection spot is in the range of hundreds of microns. There are continuous wave Raman spectrometers, which are already field deployable. However, conventional Raman suffers from the auto-fluorescence emission triggered by the laser illumination, especially in light-colored rock samples. Time-gated (TG) Raman has the benefit of time-resolved sensing, where the Raman scattering is recorded before the fluorescence signal is over-powering the weaker scattered signal. TG-Raman can thus offer information from a wider variety of geological specimen than the conventional Raman. In Goldeneye-project, TG-Raman spectra were collected with custom sampling solution from mineral samples and drill cores from Erzgebirge exploration site in Germany. The data was analysed together with pXRF reference data to assess the benefit of the data fusion.

The paper has been prepared in the frame of the Horizon 2020 co-funded project GOLDENEYE, which has received funds through the Grant Agreement 869398.

How to cite: Havisto, J., Köhler, M., Uusitalo, S., Paavola, M., Knobloch, A., and Rahkamaa-Tolonen, K.: Development of time-gated Raman proximal sensing for an earth observation platform, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4901, https://doi.org/10.5194/egusphere-egu23-4901, 2023.

12:10–12:20
|
EGU23-11469
|
GI6.7
|
Virtual presentation
Markku Pirttijärvi and Pekka Korkeakangas

During the last few years, the number of applications utilizing unoccupied aerial vehicles (UAVs), or drones, has increased rapidly in geophysics. The main benefits of airborne surveys are the ability to avoid terrain obstacles such as lakes, rivers, swamps, and ravines and the ability to collect evenly sampled data over large areas quickly. Drone surveys are safer and more cost-effective than ground surveys, especially in rough terrain. Compared to manned aircrafts, drones are cheaper to acquire and to operate. Drones are also versatile, fast to deploy, and ecologically more friendly.

Presently, drones are commonly used for magnetic surveying, and in addition to normal photogrammetry, drones are also used for multispectral and thermal imaging. Electromagnetic (EM), radiometric and gravity applications have been scarce, because the instruments are heavy compared to the modest payload of reasonable priced drones. Special adaptation or completely new instrumentation is needed to enable more drone applications.

Radai is a private Finnish company specialized in drone-based geophysical and environmental surveys. For the last five years Radai have been developing Louhi – a frequency-domain electro­magnetic (EM) system that is lightweight enough to be operated by drones. Presently, Louhi is operated using a large (Ø 100 m) ground loop as the EM source and a standalone 3-component EM receiver is towed by a VTOL (vertical take-off and landing) drone. Radai also develops a fully airborne system where a smaller transmitter loop (Ø 1 m) is fixed to the drone and receiver is towed either by the same drone or by a second drone that flies in tandem with the first one. The applications of the new EM system include geological mapping, mineral exploration, groundwater and geotechnical investigations and environmental monitoring. This paper gives details of the drone-based Louhi EM system and shows results from the first environmental survey made over a tailings pond dam of closed Pyhäsalmi Zn-Cu mine in Finland. The work is made as a part of EU Horizon 2020 funded Goldeneye project.

How to cite: Pirttijärvi, M. and Korkeakangas, P.: Drone-based electromagnetic survey system for environmental applications, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11469, https://doi.org/10.5194/egusphere-egu23-11469, 2023.

12:20–12:30
|
EGU23-11816
|
GI6.7
|
ECS
|
On-site presentation
Marton Magyar, Julia Puputti, Ossi Kotavaara, Jari Joutsenvaara, Eli Ariel, and Tuomas Koivurova

Global navigation satellite systems (GNSS)-based navigation services are widely used above ground in open-pit mining operations for safety management, process optimalization and fleet management. Extending these location-based services (LBS) to underground operations may increase efficiency and safety in mining, underground research and development, as well as, in mine reuse projects. Numerous different methods and technologies have been proposed and utilized for positioning and navigation in indoor areas and underground tunnels. Depending on the detection technology, there have been four main categories of LBS with varying levels of complexity and accuracy: 1) inertial navigation systems, 2) radio frequency (RF) based positioning, 3) multi-sensor (hybrid) navigation and 4) pseudolite-based positioning. The main motivation for deploying GNSS technology in underground conditions is to utilize the already existing, robust infrastructure, with simple off-the-shelf receiver devices. The tested system has high potential to enable high accuracy positioning in traditional GNSS-denied areas. 

The simulated underground GNSS approach is tested in a 400-meter-deep tunnel section in the Pyhäsalmi mine located in Northern Finland. At the test site, 17 signal emulators have been installed in a 200-meter-long mine tunnel to provide GNSS access. The goal is to test the simulated underground GNSS and its ability to support a wide range of common above-ground GNSS end-user devices and services. These may include applications for worker safety, mine environment monitoring and operational efficiency. The accuracy, reliability and coverage of the tested system will affect its usability significantly. In this paper, we measure positioning accuracy in different underground conditions and environments, assess applicability of a hybrid positioning approach using WLAN supported services, and test functionality of the system with common GNSS devices. The collected positioning data is analyzed with spatial analyses and statistics in geographic information systems. Results of the study will indicate how GNSS emulation techniques could be adopted to deep underground spaces and what are the possible development needs of the technology. 

This project received funding from the European Union's Horizon 2020 innovation programme under grant agreement number: 839398.

How to cite: Magyar, M., Puputti, J., Kotavaara, O., Joutsenvaara, J., Ariel, E., and Koivurova, T.: Piloting Simulated GNSS in Underground Spaces, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11816, https://doi.org/10.5194/egusphere-egu23-11816, 2023.

Posters on site: Mon, 24 Apr, 14:00–15:45 | Hall X4

Chairpersons: Kamen Bogdanov, Julia Puputti
X4.199
|
EGU23-2781
|
GI6.7
|
ECS
|
Paul Bohlender, Andreas Brosig, Roberto De La Rosa, Andreas Knobloch, and Andreas Barth

To ensure Europe increases its domestic production of high quality and responsibly produced raw materials, the development of innovative technologies for 3D geological modeling in mineral exploration is paramount. The Erzgebirge in Germany provides an excellent framework to showcase the application of artificial intelligence and in particular Artificial Neural Networks (ANN) for 3D mineral prospectivity mapping. The Erzgebirge belongs to the Variscan Belt, withholding 800 years of mining history and it is also famous for Ag, Sn, W, Fe, Cu, Li mineralizations among others. The Bockau deposit is located at the western section of the Erzgebirge. The target area is a Paleozoic metasediment body that was formed during the Variscan orogeny. The metasediment body consists primarily of alternating micaschist, phyllite and quartzite and dips mostly 25° to 240° SW. The metasediment is surrounded by Late Variscan plutons which partly led to contact metamorphic zones. In addition there is a large Quartzite body which was mined near to the surface in the 17th century for Sn, following a stratiform tin anomaly which can reach up to 4000 ppm Sn.

Thanks to the long mining history, the Bockau deposit condenses a large amount of geological, geochemical, geophysical and mineral data. To increase mineralogical knowledge of the deposit and to help identify drilling targets, a hybrid approach for 3D mineral predictivity mapping is implemented. Potentially mineralisation-controlling factors are identified in knowledge-driven genetic exploration models, taking into account the borehole data, major faults, electromagnetic data, intrusive bodies, contact metamorphic zones and lithological borders, followed by data-driven weighted ANN predictive modelling implemented in the in-house developed advangeo® 3D Prediction Software. The predictive model is guided by structural variables such as the euclidean distance to fault planes, lithological surfaces and to metamorphic contact zones. The model is also constrained by geophysical data by a magnetic susceptibility model obtained from an airborne magnetic data inversion. Finally, Sn anomaly data from boreholes is implemented as training data for the prediction.

The results show the probability distribution of Sn mineralisation occurrence in 3D over a voxel model formed by blocks of approximately 684 m3 13(x), 13.5(y) and 4(z), increasing the mineralogical knowledge of the deposit and guiding exploration efforts complementing the decision making process for drilling new targets. The results are validated by iteratively implementing the jackknife method, splitting the training data into validation and training subsets. The first prediction iteration is performed with a subset containing 77 % of the Sn content data from boreholes as training data, followed by 50 and 30 % subsets. Thus, allowing at each iteration to perform a quantitative evaluation of the prediction by comparing the validation subset with the Sn content of the borehole that was not used for the prediction.

The paper has been prepared in the frame of the Horizon 2020 co-funded project GOLDENEYE, which has received funds through the Grant Agreement 869398.

How to cite: Bohlender, P., Brosig, A., De La Rosa, R., Knobloch, A., and Barth, A.: 3D mineral prospectivity mapping of the Bockau tin deposit, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2781, https://doi.org/10.5194/egusphere-egu23-2781, 2023.

X4.200
|
EGU23-3168
|
GI6.7
Stefan Velev, Kamen Bogdanov, and Ivan Krumov

The Panagyurishte ore district, Bulgaria is situated in the Srednogorie zone situated in the western part of the global Cu-Au dominant Tethyan-Eurasian Cu-Au metallogenic belt. Significant examples of Late Cretaceous volcano-plutonic structures with porphyry-copper deposits (Elatsite, Medet, Asarel, Tsar Assen, Vlaykov Vruh), connected to subvolcanic granodiorite porphyry intrusions occur in the Panagyurishte ore region, Bulgaria. The PSD are closely associated with high-sulphidation type Cu-Au epithermal deposits (Chelopech, Krasen, Radka, Elshitsa) that are related to andesite - dacite magmatic activity. 3D UAV-supported alteration mapping of Vlaykov Vruh and Tsar Assen PCD have been performed to identify and prove the geometry of the alteration mineral assemblages and ore controlling structures. Domains consisting of phyllic, argillic, propylitic and K-silicate alteration zones associated with and porphyry-copper style of mineralization in Vlaykov Vruh and Tsar Assen deposits were outlined. 3D modelling of Popovo Dere PCD by means of Leapfrog Geo and mineral alteration mapping study outlined fault controlled proximal K-silicate domain and more distal propylitic domain as a potential Cu-porphyry deposit target for further mineral exploration and evaluation

Two types of K-silicate alterations, one with magnetite and another without magnetite that hosted Cu-porphyry mineralization have been distinguished within the proximal Cu-rich zone. More distal propylitic domain has also been outlined by 3D modelling. Strike-slip fault control within the K-silicate alteration domain outlined the cone shaped Cu-porphyry ore body. The UAV-Multispectral and TIR mapping in addition to XRD study confirmed the geometry of phyllic alteration domain hosted in andesitic and dacitic volcanic rocks. The propylitic alteration zone is developed in the granodiorite porphyry intrusion in Vlaykov Vruh PCD and with K-silicate domains hosts Cu-Mo mineralization. Fe-oxide and malachite rich domains have been traced in Tsar Assen PCD in addition to Cu-rich zone in the western part of the open pit.

The recent study demonstrates that combined UAV-supported remote sensing and mineral alteration field and XRD mapping could provide an effective vectoring and exploration targeting tool toward PCD mineralization

This study is supported by the Horizon 2020 co-funded GOLDENEYE project through the Grant Agreement 869398.

How to cite: Velev, S., Bogdanov, K., and Krumov, I.: 3D alteration mapping and remote-sensing applications for porphyry -copper deposits (PCD) exploration, in Panagyurishte ore district, Bulgaria, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3168, https://doi.org/10.5194/egusphere-egu23-3168, 2023.

X4.201
|
EGU23-5181
|
GI6.7
Kateryna Sergieieva, Olena Kavats, and Dmitriy Khramov

Monitoring and mapping open-pit mining activity is essential to identify operation sites and unaffected surfaces of mining areas. Vertical displacements of the earth's surface associated with open pit mining can be detected using high spatial resolution Digital Surface Model (DSM) data or based on all-weather Synthetic Aperture Radar (SAR) Single Look Complex (SLC) satellite images using Differential Interferometry Synthetic Aperture Radar (DInSAR) technique. In some cases, activity in an open pit may not be accompanied by changes in terrain heights but cause violations of land cover integrity accompanied by earth's surface texture changes (for example, deforestation or recultivation, violation of quarries and dump slope integrity, changes in surface conditions, hydrological disturbances, etc.) and can be detected using coherence maps generated from SAR SLC data.

Coherence is the modulus of the complex correlation coefficient between two SLC images containing information about the amplitude and phase of the radar signal. If there is no surface change between the two survey dates, the coherence values are close to 1. Mining activities change the surface texture, so the coherence decreases to values close to 0. The frequency approach estimates the total changes in coherence over the season. For example, the Temporal Activity Index (TAI) is a relative coherence frequency below a given threshold across the time series of SAR images. In the case of monitoring open pit mining, activity areas with consistently low coherence over a time series of observations are of primary interest.

The study area is an open-pit mining area of the Pyhäsalmi Mine located in the Pohjois-Pohjanmaa region, Finland. It includes an old open pit, a backfill open pit, and several waste dumps [1]. Time series of Sentinel-1 SLC Interferometric Wide (IW) images were used to detect active areas in operation for the study area. Images were collected every 12 days from May to  September 2020-2022 and provided by the GOLDEN-AI platform [2].

For each observation year, a time series of Sentinel-1 SLC coherence was generated for the Pyhäsalmi mine. Active areas in operation were identified for open pits and waste dumps based on TAI maps (Fig. 1), providing information about the intensity of surface changes during the observation periods.

Figure 1. Temporal Activity Index maps for the Pyhäsalmi Mine area.

Funding. This work was funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 869398 “Earth observation and Earth GNSS data acquisition and processing platform for safe, sustainable and cost-efficient mining operations” (Goldeneye).

Acknowledgments. The authors gratefully acknowledge Maria Hänninen, Environmental Manager at Pyhäsalmi Mine Oy for specification locations for measurements and study planning, and the OPT/NET BV company (opt-net.eu) and GOLDEN-AI platform for supplying Sentinel-1 data. The authors would like to thank the European Commission, the European Space Agency, and the Copernicus Program for providing Sentinel-1 data.

References:

[1] Siikanen, S., Savolainen, M., Karinen, A., Puputti, J., Kauppinen, T., Uusitalo, S., & Paavola, M., 2022. Drone-based near-infrared multispectral and hyperspectral imaging in monitoring structural changes in mine tailing ponds. Thermal Infrared Applications XLIV, Vol. 12109, pp. 58-64). https://doi.org/10.1117/12.2618294

[2] Havisto, J., Matselyukh, T., Paavola, M., Uusitalo, S., Savolainen, M., González, A. S., Knobloch, A. & Bogdanov, K., 2021. Golden AI Data Acquisition and Processing Platform for Safe, Sustainable and Cost-Efficient Mining Operations. 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, pp. 5775-5778. https://ieeexplore.ieee.org/document/9554181

 

How to cite: Sergieieva, K., Kavats, O., and Khramov, D.: Monitoring Active Mining Areas in Operation using Sentinel-1 Coherence Time Series, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5181, https://doi.org/10.5194/egusphere-egu23-5181, 2023.

X4.202
|
EGU23-6208
|
GI6.7
Ossi Kotavaara, Guillem Domenech, Sandra Mingot, Jari Joutsenvaara, Julia Puputti, Daniel Nieto Yll, Zarina Acero, and Terhi Ala-Hulkko

Monitoring the stability of mine structures, such as tailing ponds and open-pits, is crucial for ensuring the safety of personnel on-site and for preventing environmental accidents. This must be done, not only during the active operation of a mine, but also during possible reuse phases, and even after closure. Currently, monitoring the structural stability of the mining area relies heavily on manually conducted RTK-GNSS-based measurements of established control points. While this is a precise and relatively simple technique, it does pose a limit to how many control points can feasibly be monitored, as using tens or even hundreds of control points is time-intensive and laborious. Consequently, monitoring larger areas and areas requiring frequent measuring can be challenging. A remote monitoring option would also remove the element of danger that comes from having to reach control points in possibly unstable areas. InSAR appears to be an alternative for measuring terrain displacements in large, mining areas. Some limitations remain, as terrain coverage and weather conditions in northern latitudes can hinder InSAR analysis. 

 

The Callio Lab research centre at the Pyhäsalmi mine in Finland has been chosen as a test site for InSAR measurements conducted during the EU H2020-funded GoldenEye project. InSAR is used to measure terrain displacement as a result of geomorphologic changes during the summer and autumn of 2022. Additionally, InSAR analysis will be carried out using a network of corner reflectors during winter 2023. InSAR measurements will be evaluated and compared to drone imagery-based photogrammetric Digital Elevation Models (DEM) and field observations. Supplementary RTK-GNSS measurements are planned to be used to control the stability of selected control points. Results will provide valuable insight about InSAR usability for long-term monitoring in northern latitudes in mine environments, as well as, knowledge related to weather and terrain conditions required for obtaining reliable InSAR. Results will also touch on the main challenges faced when using InSAR in such an environment.

 

This work has been supported by project Earth observation and Earth GNSS data acquisition and processing platform for safe, sustainable and cost-efficient mining operations (Goldeneye) ID: 869398, Horizon 2020.

How to cite: Kotavaara, O., Domenech, G., Mingot, S., Joutsenvaara, J., Puputti, J., Nieto Yll, D., Acero, Z., and Ala-Hulkko, T.: Interferometric Synthetic Aperture Radar (InSAR)-based measurements of displacements due to geomorphologic changes in northern mining environments – testing and validating InSAR in open pit and tailings of Pyhäsalmi Mine, Finland, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6208, https://doi.org/10.5194/egusphere-egu23-6208, 2023.

X4.203
|
EGU23-8355
|
GI6.7
|
ECS
Julia Puputti, Marko Holma, Ossi Kotavaara, Jari Joutsenvaara, and Marton Magyar

Callio Lab is a multidisciplinary research centre operating at the Pyhäsalmi mine in Finland and it is coordinated by the Kerttu Saalasti Institute of the University of Oulu.  The Callio Lab team is responsible for hosting, facilitating, and supporting field trials conducted at the Pyhäsalmi site during the EU funded H2020 project GoldenEye. They are also involved in evaluating the piloted techniques, which includes providing ground truths and other comparative data that can be used for validation. The field trials include pilots such as monitoring the stability of tailing ponds and the deployment of an underground simulated GNSS system. 

The Pyhäsalmi mine is a prime location for testing remote sensing and positioning technologies in a real-world mining setting, as the environment encompasses many key elements that can be found in mines around the world: active and closed open pits of various steepness, ore and waste rock piles, tailing ponds in various states of use, and a multifaceted landscape. Callio Lab and its predecessor CUPP (the Centre for Underground Physics in Pyhäsalmi) have a long-standing history of cooperation with the mining company, which affords easy access to the area and the possibility of using historical datasets spanning decades. We will be presenting how the Callio Lab environment at the Pyhäsalmi mine can serve as a field trial site in projects such as GoldenEye.  

This work has been supported by the project Earth observation and Earth GNSS data acquisition and processing platform for safe, sustainable and cost-efficient mining operations (Goldeneye) ID: 869398, Horizon 2020.

How to cite: Puputti, J., Holma, M., Kotavaara, O., Joutsenvaara, J., and Magyar, M.: Callio Lab – a GoldenEye field trial site at the Pyhäsalmi mine in Finland, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8355, https://doi.org/10.5194/egusphere-egu23-8355, 2023.

Posters virtual: Mon, 24 Apr, 14:00–15:45 | vHall ESSI/GI/NP

Chairpersons: Kamen Bogdanov, Julia Puputti
vEGN.24
|
EGU23-2574
|
GI6.7
|
ECS
Martin Köhler, Nailia Rizatdinova, Andreas Knobloch, and Roberto de la Rosa

The Goldeneye project combines different sensing technologies with proximal sensing to produce reference calibrated mineralogical maps through data fusion. In order to develop mineral detection applications, rock specimens, taken from an outcrop in Bockau (Erzgebirge, Germany), are analyzed with active hyperspectral scanning (AHS) as well as portable X-ray fluorescence (pXRF) devices. The received data is analyzed by means of artificial intelligence in order to develop an approach to automatically map the minerals with the samples. The analysis is carried out in advangeo® 2D Prediction, developed by Beak Consultants GmbH. Tin concentrations derived from pXRF measurements and AHS data from 2/3 of the specimen surface serve as training and validation data of the artificial intelligence algorithm (artificial neural networks). As a result, we developed a prediction model for the distribution of tin and its associated mineral cassiterite throughout the rock specimen, which allows to detect the mineral potential of hand specimens and larger outcrops in a fast and reliable manner.

The paper has been prepared in the frame of the Horizon 2020 co-funded project GOLDENEYE, which has received funds through the Grant Agreement 869398.

How to cite: Köhler, M., Rizatdinova, N., Knobloch, A., and de la Rosa, R.: 2D mineral prospectivity mapping of hand specimen and outcrop walls using AHS and pXRF data in Bockau, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2574, https://doi.org/10.5194/egusphere-egu23-2574, 2023.

vEGN.25
|
EGU23-2625
|
GI6.7
Enis Sterjo, Andreas Knobloch, Martin Köhler, and Andreas Brosig

From the middle of the 17th to the beginning of the 19th century, tin mining was carried out near Bockau and Aue in the Westerzgebirge. The object of the mining was stratiform mineralization. Near Bockau, (underground) tin mining was first mentioned in documents in 1663 and was active, with interruptions, until the beginning of the 19th century. On an area of more than 2 km², hundreds of dumps and pits, numerous abandoned mines, and historically very remarkable underground objects are known. Mining was preferably carried out near the surface. Despite favourable morphological conditions, hardly any deep adits were built and depths of more than 20 m were rarely reached. In this context, Pingen are abandoned ore pits or prospecting sites, where ores and other mineral resources were mined. Geometrically Pingen resemble round depressions created by the collapse of a mine workings (shaft, adit, underground drift), collapsed due to its age, leaving this relic usually funnel-shaped (down-facing cone), often surrounded by an annular dump (0.5 to 3 m) caused by the lowered surface.

The main aim of this application is to identify mining relics (Pingen) using a UAV equipped with LiDAR technology. The LiDAR technology allows to obtain a high-resolution Digital Elevation Model (DEM) and Point cloud of the surveyed area. The DEM is the digital representation of topographic and manmade features located on the surface of the earth.

In this framework, a LiDAR survey was conducted in a flight area of about 1,6 km² within the “Bockau” area during August 2022. The surveyed features include the elevations, colorized Point Cloud (RGB values), transparency levels, reflectance values, and number of returns (significant for the penetration of the vegetation). This information was used to derive the final products: DEM and classified Point cloud. Various spatial analyses were conducted and tested on the DEM and Point Cloud to automatically identify the mining relics. Hydrogeological analysis showed to be the best approach for the automatic identification of Pingen. As a result, the ground depressions were identified and nested surfaces were delineated.

The automatically identified features were validated by examination of randomly selected samples on the surveyed point cloud, comparison to identified features based on the National dataset of the LiDAR Database and field verification. The validation revealed, that around 90% of the Pingen in the study area were successfully identified with the developed workflow. Other features of interests couldn’t be identified due to the similarity of geometric properties with other topographical features, dense vegetation, erosion etc.

The paper has been prepared in the frame of the Horizon 2020 co-funded project GOLDENEYE, which has received funds through the Grant Agreement 869398.

How to cite: Sterjo, E., Knobloch, A., Köhler, M., and Brosig, A.: Detection of mining relics (Pingen) using LiDAR technology, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2625, https://doi.org/10.5194/egusphere-egu23-2625, 2023.