Integrative Geophysical Approach for Enhanced Iron Ore Detection: Optimizing Geoelectrical and Geomagnetic Methods
Bibliographic record
Abstract
Geoelectrical and geomagnetic technologies are employed in the Ogololo Sub-District of Sojol District, Donggala Regency, Central Sulawesi, Indonesia, to offer more accurate subsurface information.This is done to rectify inherent deficiencies in the deposit model that impede mineral exploration.The selection of these methods relied on their strong complementarity and effectiveness.Geomagnetic surveys are very effective in identifying the magnetic properties of minerals with high iron levels.On the other hand, geoelectrical surveys are highly effective in distinguishing between ore and host rock by measuring resistivity.Our approach enhances previous methods for iron ore prospecting by using a unique combination of these strategies.To make the interpretation more precise, we employed meticulous modeling and techniques of validation, such as geochemical assays, two-line geoelectrical testing, and five-line geomagnetic observations.Our careful division of iron ore and granitoid bodies, which have resistivity values ranging from 170 to 1146Ωm and 685.7 to 7671.1Ωm, respectively, and geomagnetic anomalies ranging from -650nT to +1700nT, aligns with iron ore content between 61.09 to 97.12%.This clearly shows our significant advancement compared to previous techniques.This combination not only enhances the precision of subsurface condition analysis but also introduces a novel methodology for resource investigation, distinguishing our work in the geophysical exploration sector.Our findings underline the fact that places with substantial magnetic fields are likely electrically conducting structures, which could imply huge iron ore deposits.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".