Development of Sustainable Test Sites for Mineral Exploration and Knowledge Spillover for Industry

Keßelring, Michaela; Wagner, Frank; Kirsch, Moritz; Ajjabou, Leila; Gloaguen, Richard

doi:10.3390/su12052016

Cited by 12 publications

(13 citation statements)

References 66 publications

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“…There is a growing demand for mineral resources worldwide, and yet the industry is facing increasing obstacles in obtaining public acceptance for new exploration and mining projects. Numerous recent citizen protests highlight the public perception of 'destructive' mining projects and increase the reluctance of investors to finance exploration and mine projects [4]. Social scientists attempted to provide a framework to improve the sustainability of metal sourcing.…”

Section: Goalsmentioning

confidence: 99%

The Potential of Machine Learning for a More Responsible Sourcing of Critical Raw Materials

Ghamisi¹,

Shahi²,

Duan³

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The digitization and automation of the raw material sector is required to attain the targets set by the Paris Agreements and support the sustainable development goals defined by the United Nations. While many aspects of the industry will be affected, most of the technological innovations will require smart imaging sensors. In this review, we assess the relevant recent developments of Machine Learning for the processing of imaging sensor data. We first describe the main imagers and the acquired data types as well as the platforms on which they can be installed. We briefly describe radiometric and geometric corrections as these procedures have been already described extensively in previous works. We focus on the description of innovative processing workflows and illustrate the most prominent approaches with examples. We also provide a list of available resources, codes, and libraries for researchers at different levels, from students to senior researchers, willing to explore novel methodologies on the challenging topics of raw material extraction, classification, and process automatization.

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Section: Goalsmentioning

confidence: 99%

The Potential of Machine Learning for a More Responsible Sourcing of Critical Raw Materials

Ghamisi¹,

Shahi²,

Duan³

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…For both exploratory and exploitative innovation strategy, we identified five research items from Jansen et al (2006). addition, benefits to the creative economy of ERShosting regions have already been explored (Kesselring et al, 2020).…”

Section: Case Selectionmentioning

confidence: 99%

“…However, demonstrating the performance of technologies such as sensors comes with several requirements. Requirements include detailed knowledge of the test sites, testing, and land access permits, and compliance with socioeconomic and ecologic conditions of the host country (Kesselring et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Integrated Trust Sites for Innovative Ecosystems

Keßelring¹,

Ruiz-Coupeau²,

Kirsch³

et al. 2021

TIM Review

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“…The Sakatti deposit is located 15 km north of Sodankylä, Finland; it is rich in Cu-Ni-PGE (Platinum Group Elements) minerals [55] and has been selected as one of the test sites of the EU-funded project INFACT aiming at the development of cutting-edge technologies for mineral exploration [56]. Within the framework of the INFACT project, time-domain electromagnetic data have been acquired by Geotech using a VTEM system [16,17].…”

Section: Field Examplementioning

confidence: 99%

(Quasi-)Real-Time Inversion of Airborne Time-Domain Electromagnetic Data via Artificial Neural Network

et al. 2020

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The possibility to have results very quickly after, or even during, the collection of electromagnetic data would be important, not only for quality check purposes, but also for adjusting the location of the proposed flight lines during an airborne time-domain acquisition. This kind of readiness could have a large impact in terms of optimization of the Value of Information of the measurements to be acquired. In addition, the importance of having fast tools for retrieving resistivity models from airborne time-domain data is demonstrated by the fact that Conductivity-Depth Imaging methodologies are still the standard in mineral exploration. In fact, they are extremely computationally efficient, and, at the same time, they preserve a very high lateral resolution. For these reasons, they are often preferred to inversion strategies even if the latter approaches are generally more accurate in terms of proper reconstruction of the depth of the targets and of reliable retrieval of true resistivity values of the subsurface. In this research, we discuss a novel approach, based on neural network techniques, capable of retrieving resistivity models with a quality comparable with the inversion strategy, but in a fraction of the time. We demonstrate the advantages of the proposed novel approach on synthetic and field datasets.

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Development of Sustainable Test Sites for Mineral Exploration and Knowledge Spillover for Industry

Cited by 12 publications

References 66 publications

The Potential of Machine Learning for a More Responsible Sourcing of Critical Raw Materials

The Potential of Machine Learning for a More Responsible Sourcing of Critical Raw Materials

Integrated Trust Sites for Innovative Ecosystems

(Quasi-)Real-Time Inversion of Airborne Time-Domain Electromagnetic Data via Artificial Neural Network

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