Application of wide-field electromagnetic method for skarn-type polymetallic deposits’ exploration in the Yemaquan, Qinghai Province, China
Bibliographic record
Abstract
Abstract The Qimantag region in the East Kunlun Mountains is a significant skarn-type polymetallic metallogenic belt in China. With the exhaustion of shallow deposits due to extensive geological and exploration work, there is a pressing need to explore deeper buried ore bodies. The desert soil cover limits the effectiveness of geological and geochemical surveys. Traditional magnetic and gravity surveys have been the primary methods for early exploration but are inadequate for deep exploration. This study applies the Wide-field Electromagnetic Method (WFEM) to mineral exploration in the Yemaquan area of Qimantag region. Developed from the Controlled Source Audio-frequency Magnetotellurics (CSAMT), WFEM uses a vertical or horizontal dipole source to generate electromagnetic responses. It calculates apparent resistivity from a single observed parameter, significantly reducing data acquisition costs. The method is especially effective for identifying deep metal deposits under thick cover. WFEM data were recorded and then processed using the Gauss–Newton method for 2D inversion, followed by 3D kriging interpolation to generate a resistivity model at a depth of 1000 meters in the study area. The results revealed the distribution and contact relationships of sedimentary strata and rock bodies, correlating well with existing geological and geophysical data. Drilling verified the presence of iron, copper, and other polymetallic ore bodies, demonstrating the potential of WFEM for mineral exploration in areas with weak magnetic anomalies. This study validates the effectiveness of WFEM in detecting deep polymetallic deposits in the Qimantag area and provides valuable reference for future exploration in similar geological environments.
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 | 0.001 |
| 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) | 0.000 | 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".