Detecting and recovering critical mineral resource systems using broadband total-field airborne natural source audio frequency magnetotellurics measurements
Bibliographic record
Abstract
ABSTRACT Airborne geophysical methods offer a substantial advantage compared to ground-based techniques in exploring territories of different sizes, ranging from entire metallogenic provinces to the deposit scale, including those hosting critical minerals. An airborne method with measurements of natural magnetic field variations, known as audio frequency magnetotellurics (a passive field method), significantly increases the depth of investigation and expands the resistivity detection range compared with some controlled-source primary-field methods. We describe the technical solutions used in an airborne electromagnetic passive system with a mobile sensor of the total magnetic field variations and the stationary sensor of electric field variations, and its applications to recovering the complex geology of hydrothermal-magmatic systems often associated with critical minerals. The system’s ability to explore depths, typically beginning from the near-surface and down to 1–2 km, by recording responses in three orthogonal inductive coils over a broad bandwidth from 22 Hz to 21,000 Hz allows for mapping resistivities across a broad range. This capability is crucial for obtaining more comprehensive exploration models. Field case studies of the natural field system include application in exploring for unconformity uranium mineralization, along with other associated minerals, epithermal gold and polymetallic-bearing structures, and ferromanganese and polymetallic deposits formed in a continental rift valley. An extra case study involving kimberlites was incorporated as a proven example of the natural field system’s capability in conducting near-surface and deep investigations. The case histories illustrate the airborne natural electromagnetic field technology capabilities in recovering geoelectric models and their specific patterns.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".