The hunt for mineral resources with quantum magnetometers
-
Ronny Stolz
,
Markus Schiffler
Abstract
Quantum sensing provides advanced technologies which significantly improve sensitivity and accuracy for sensing changes of motion, gravity, electric and magnetic field. Therein, quantum sensors for the detection of magnetic fields, so-called quantum magnetometers, are one of the most promising technological realizations. We firstly will provide a brief overview on methods in geophysical exploration benefitting from quantum magnetometers with resolution at the physical and technical limit. We will introduce recent developments on SQUID and OPM based sensors as specific implementations of a quantum magnetometer systems and application examples.
Funding source: NRSEC of Canada
Award Identifier / Grant number: 961966
Funding source: Federal Ministry of Economic Affairs and Climate Action BMWK
Award Identifier / Grant number: KK5031301
Funding source: Bundesministerium für Bildung und Forschung
Award Identifier / Grant number: 033R130
Award Identifier / Grant number: 033R385
Funding source: Fourth Framework Programme
Award Identifier / Grant number: 01QE1710
Award Identifier / Grant number: 033RU001B
About the author
Ronny Stolz studied physics at the Friedrich-Schiller-University in Jena/Germany and graduated in 2006. He is head of the Quantum Systems department at the Leibniz Institute of Photonic Technology in Jena/Germany and also affiliated with the Technical University in Ilmenau/Germany as Honorary professor with focus on Quantum engineering. His research topics include superconducting quantum circuits and their application for sensing (e.g. geoscience), metrology, and computation.
-
Research ethics: Not applicable.
-
Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission. Ronny Stolz – main author, data processing and interpretation, field application, Sensor & electronics development. Markus Schiffler, Michael Becken, Michael Schneider, Glenn Chubak – data processing and interpretation, instrument application in field testing.
-
Competing interests: The authors states no competing interests.
-
Research funding: The work was partly funded by the BMBF within the projects DESMEX II (no. 033R130) and DESMEX-Real (no. 033R385), European Union through QMag (01QE1710) as well as AMTEG (033RU001B), BMWK on the basis of a decision by German Bundestag under no. KK5031301 and NSERC of Canada under no. 961966 [CRSNG, numéro de référence] for the QAMT project.
-
Data availability: Not applicable.
References
[1] R. Schodde, Challenges of Exploring under Deep Cover (AMIRA International’s 11th Biennial Exploration Managers Conference), Healesville, Australia, 2017.Search in Google Scholar
[2] W. M. Telford. Geldart, L. P. and Sheriff, R. E. 1990. Applied Geophysics, 2nd ed. Cambridge, England, New York, Cambridge University Press.10.1017/CBO9781139167932Search in Google Scholar
[3] R. Boll and K. J. Overshott, Sensors: Magnetic Sensors (Sensors vol 5), Hoboken, Wiley VCH, 2008.Search in Google Scholar
[4] M. N. Nabighian, V. J. S. Grauch, R. O. Hansen, et al.., “The historical development of the magnetic method in exploration,” Geophysics, vol. 70, pp. 33–61, 2005. https://doi.org/10.1190/1.2133784.Search in Google Scholar
[5] A. Grosz, M. J. Haji-Sheikh, and S. C. Mukhopadhyay, Eds. High Sensitivity Magnetometers (Smart Sensors, Measurement and Instrumentation vol 19), Cham, s.l, Springer International Publishing, 2017.10.1007/978-3-319-34070-8Search in Google Scholar
[6] S. Tumanski, “Induction coil sensors—a review,” Meas. Sci. Technol., vol. 18, pp. R31–R46, 2007. https://doi.org/10.1088/0957-0233/18/3/r01.Search in Google Scholar
[7] D. Budker and D. F. Jackson Kimball, Eds. Optical Magnetometry, Cambridge, Cambridge University Press, 2013.10.1017/CBO9780511846380Search in Google Scholar
[8] A. Fabricant, I. Novikova, and G. Bison, “How to build a magnetometer with thermal atomic vapor: a tutorial,” New J. Phys., vol. 25, p. 025001, 2023. https://doi.org/10.1088/1367-2630/acb840.Search in Google Scholar
[9] J. Clarke and A. I. Braginski, The SQUID Handbook, Vol.1: Fundamentals and Technology of SQUIDs and SQUID Systems, Weinheim, Cambridge, Wiley VCH, 2004.10.1002/3527603646.ch1Search in Google Scholar
[10] V. Zakosarenko, L. Warzemann, J. Schambach, et al.., “Integrated LTS gradiometer SQUID systems for unshielded measurements in a disturbed environment,” Supercond. Sci. Technol., vol. 9, pp. A112–A115, 1996. https://doi.org/10.1088/0953-2048/9/4a/029.Search in Google Scholar
[11] D. Drung, “High-Tcand low-Tcdc SQUID electronics,” Supercond. Sci. Technol., vol. 16, pp. 1320–1336, 2003. https://doi.org/10.1088/0953-2048/16/12/002.Search in Google Scholar
[12] M. Schmelz, R. Stolz, V. Zakosarenko, et al.., “Field-stable SQUID magnetometer with sub-fT Hz(-1/2) resolution based on sub-micrometer cross-type Josephson tunnel junctions,” Supercond. Sci. Technol., vol. 24, p. 65009, 2011.10.1088/0953-2048/24/6/065009Search in Google Scholar
[13] J. McKay, J. Vrba, K. Betts, et al.., “Implementation of a multi-channel biomagnetic measurement system using DSP technology,” in Proceedings of Canadian Conference on Electrical and Computer Engineering Canadian Conference on Electrical and Computer Engineering (Vancouver, BC, Canada, 14–17 Sept. 1993), (IEEE), 1993, pp. 1090–1093.Search in Google Scholar
[14] E. Zimmermann, G. Brandenburg, U. Clemens, et al.., “HTS-SQUID magnetometer with digital feedback control for NDE applications,” in Review of Progress in Quantitative Nondestructive Evaluation, vol. 16A, D. O. Thompson and D. E. Chimenti, Eds., Boston, MA, US, Springer, 1997, pp. 2129–2135.10.1007/978-1-4615-5947-4_278Search in Google Scholar
[15] H. Larnier, G. Chubak, M. Schneider, M. Schiffler, and R. Stolz, “Three component SQUID-based system for airborne natural field electromagnetics,” in First International Meeting for Applied Geoscience & Energy Expanded Abstracts, 2021, pp. 1290–1294.10.1190/segam2021-3594781.1Search in Google Scholar
[16] V. Schultze, B. Schillig, R. IJsselsteijn, T. Scholtes, S. Woetzel, and R. Stolz, “An optically pumped magnetometer working in the light-shift dispersed Mz mode,” Sensors, vol. 17, no. 3, p. 17, 2017.10.3390/s17030561Search in Google Scholar PubMed PubMed Central
[17] T. Wolf, P. Neumann, K. Nakamura, et al.., “Subpicotesla diamond magnetometry,” Phys. Rev. X, vol. 5, p. 041001, 2015. https://doi.org/10.1103/physrevx.5.041001.Search in Google Scholar
[18] R. Stolz, M. Schmelz, V. Zakosarenko, et al.., “Superconducting sensors and methods in geophysical applications,” Supercond. Sci. Technol., vol. 34, pp. 033001–033034, 2021. https://doi.org/10.1088/1361-6668/abd7ce.Search in Google Scholar
[19] R. Stolz, M. Schiffler, M. Becken, et al.., “SQUIDs for magnetic and electromagnetic methods in mineral exploration,” Miner. Econ., vol. 35, pp. 467–494, 2022. https://doi.org/10.1007/s13563-022-00333-3.Search in Google Scholar
[20] J. B. Lee, D. L. Dart, R. J. Turner, et al.., “Airborne TEM surveying with a SQUID magnetometer sensor,” Geophysics, vol. 67, pp. 468–477, 2002. https://doi.org/10.1190/1.1468606.Search in Google Scholar
[21] C. L. LeRoux, R. Stolz, B. Du Plooy, and J. P. Smit, 11th SAGA Biennial Technical Meeting and Exhibition, 2009, pp. 50–54.Search in Google Scholar
[22] P. W. Schmidt and D. A. Clark, “The magnetic gradient tensor: its properties and uses in source characterization,” Leading Edge, vol. 25, pp. 75–78, 2006. https://doi.org/10.1190/1.2164759.Search in Google Scholar
[23] J. Rudd, G. Chubak, H. Larnier, et al.., “Commercial operation of a SQUID-based airborne magnetic gradiometer,” Leading Edge, vol. 41, pp. 486–492, 2022. https://doi.org/10.1190/tle41070486.1.Search in Google Scholar
[24] M. Bastani, M. Sadeghi, A. Malehmir, S. Luth, and P. Marsden, “3D Magnetic susceptibility model of a deep iron-oxide apatite-bearing orebody incorporating borehole data in Blötberget,” in SAGA Biennial Conference & Exhibition, Durban, South Africa, 2019.Search in Google Scholar
[25] M. S. Zhdanov, A. Gribenko, and G. Wilson, “Generalized joint inversion of multimodal geophysical data using Gramian constraints: three-dimensional joint inversion,” Geophys. Res. Lett., vol. 39, pp. L09301–L09308, 2012. https://doi.org/10.1029/2012gl051233.Search in Google Scholar
[26] D. W. Oldenburg and Y. Li, “5. Inversion for applied geophysics: A tutorial,” Near Surf. Geophys., pp. 89–150, 2012. https://doi.org/10.1190/1.9781560801719.ch5.Search in Google Scholar
[27] S. H. Ward, “AFMAG—airborne and ground,” Geophysics, vol. 24, pp. 761–787, 1959. https://doi.org/10.1190/1.1438657.Search in Google Scholar
[28] M. Becken, C. G. Nittinger, M. Smirnova, et al.., “DESMEX: a novel system development for semi-airborne electromagnetic exploration,” Geophysics, vol. 85, pp. E253–E267, 2020. https://doi.org/10.1190/geo2019-0336.1.Search in Google Scholar
[29] A. Steuer, M. Smirnova, M. Becken, et al.., “Comparison of novel semi-airborne electromagnetic data with multi-scale geophysical, petrophysical and geological data from Schleiz, Germany,” J. Appl. Geophy., vol. 182, p. 104172, 2020. https://doi.org/10.1016/j.jappgeo.2020.104172.Search in Google Scholar
[30] S. Yu and J. Ma, “Deep learning for geophysics: current and future trends,” Rev. Geophys., vol. 59, p. 6592, 2021. https://doi.org/10.1029/2021rg000742.Search in Google Scholar
[31] D. A. Pratt, K. B. McKenzie, and A. S. White, “An AI approach to automated magnetic formation mapping beneath cover,” ASEG Extended Abstracts, vol. 2019, pp. 1–9, 2019. https://doi.org/10.1080/22020586.2019.12073001.Search in Google Scholar
[32] A. Mentges and B. S. Rawal, “Magnetic dipole moment estimation from nearfield measurements using stochastic gradient descent AI model,” in 2022 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COM-IT-CON), 2022, pp. 327–332.10.1109/COM-IT-CON54601.2022.9850855Search in Google Scholar
[33] H. Deng, X. Hu, H. Cai, et al.., “3D inversion of magnetic gradient tensor data based on convolutional neural networks,” Minerals, vol. 12, p. 566, 2022. https://doi.org/10.3390/min12050566.Search in Google Scholar
[34] J. Sun and Y. Li, “Joint inversion of multiple geophysical and petrophysical data using generalized fuzzy clustering algorithms,” Geophys. J. Int., vol. 208, pp. 1201–1216, 2017. https://doi.org/10.1093/gji/ggw442.Search in Google Scholar
[35] Z. Hu, S. Liu, X. Hu, et al.., “Inversion of magnetic data using deep neural networks,” Phys. Earth Planet. Inter., vol. 311, p. 106653, 2021. https://doi.org/10.1016/j.pepi.2021.106653.Search in Google Scholar
[36] F. Nurindrawati and J. Sun, “Predicting magnetization directions using convolutional neural networks,” JGR Solid Earth, vol. 125, p. 1, 2020. https://doi.org/10.1029/2020jb019675.Search in Google Scholar
[37] Q. Li, Z. Li, Z. Shi, and H. Fan, “Magnetic object recognition with magnetic gradient tensor system heading-line surveys based on kernel extreme learning machine and sparrow search algorithm,” Measurement, vol. 203, p. 111967, 2022. https://doi.org/10.1016/j.measurement.2022.111967.Search in Google Scholar
© 2023 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
- Frontmatter
- Editorial
- Mit Intelligenz von der Messung zur Information – VDI-Zukunftsforum in Ettlingen –
- Research Articles
- Improving the performance of artificial neural networks trained on synthetic data in gas spectroscopy – a study on two sensing approaches
- Integrating metrological principles into the Internet of Things: a digital maturity model for sensor network metrology
- Review Article
- Quantentechnologie und Quantensensorik: Aktuelle Themen der Technologieentwicklung und Transfer in die Anwendung aus Sicht eines nationalen Metrologieinstituts
- Research Articles
- The hunt for mineral resources with quantum magnetometers
- Maschinenlesbares und maschineninterpretierbares digitales Kalibrierzertifikat (DCC) und sein Einsatz in der Praxis
- Eichung im Wandel – Veränderungen bei gesetzlich geregelten Messgeräten
Articles in the same Issue
- Frontmatter
- Editorial
- Mit Intelligenz von der Messung zur Information – VDI-Zukunftsforum in Ettlingen –
- Research Articles
- Improving the performance of artificial neural networks trained on synthetic data in gas spectroscopy – a study on two sensing approaches
- Integrating metrological principles into the Internet of Things: a digital maturity model for sensor network metrology
- Review Article
- Quantentechnologie und Quantensensorik: Aktuelle Themen der Technologieentwicklung und Transfer in die Anwendung aus Sicht eines nationalen Metrologieinstituts
- Research Articles
- The hunt for mineral resources with quantum magnetometers
- Maschinenlesbares und maschineninterpretierbares digitales Kalibrierzertifikat (DCC) und sein Einsatz in der Praxis
- Eichung im Wandel – Veränderungen bei gesetzlich geregelten Messgeräten
