SQUIDs for magnetic and electromagnetic methods in mineral exploration
Bibliographic record
Abstract
Abstract Research on quantum sensors for the detection of magnetic fields (quantum magnetometers) is one of the fast-moving areas of Quantum Technologies. Since there exist expectations about their use in geophysics, this work will provide a brief overview on the various developing quantum technologies and their individual state of the art for implementing quantum magnetometers. As one example, the developments on superconducting quantum interference devices, so-called SQUIDs as a specific implementation of a quantum magnetometer, are presented. In the course of this, SQUID instrument implementations and associated demonstrations and case studies will be presented. An airborne vector magnetometer with ultra-low noise ( $$<10 \mathrm{fT}/\sqrt{\mathrm{Hz}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo><</mml:mo> <mml:mn>10</mml:mn> <mml:mi>fT</mml:mi> <mml:mo>/</mml:mo> <mml:msqrt> <mml:mi>Hz</mml:mi> </mml:msqrt> </mml:mrow> </mml:math> ) and high dynamic range of $$>32 \mathrm{bit}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>></mml:mo> <mml:mn>32</mml:mn> <mml:mi>bit</mml:mi> </mml:mrow> </mml:math> will be introduced which has the prospect to be applied for the magnetic method in parallel with electromagnetic methods such as passive audio-frequency magnetics or semi-airborne methods using active transmitters such as elongated grounded dipole sources. The according signals are separated in the frequency domain. A second implementation is an airborne full tensor gradiometer instrument will be discussed which has shown already a number of successful case studies and which turned into commercial operation in the past years. Besides the airborne instrument, a very successful implementation of quantum magnetometers is the SQUID-based receiver for the ground-based transient electromagnetic method. Today it is a mature technology which has been in commercial use for more than a decade and has led to a number of discoveries of conductive ore bodies. One case study will be presented which demonstrates the performance of this instrument. Finally, future prospects of using quantum magnetometers, including SQUIDs and new optically pumped magnetometers, in geophysical exploration will be discussed. Particular applications for both sensor types will be introduced.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".