Deep Electrical Resistivity Tomography (DERT): a versatile method to investigate fluid migration systems and to identify ore deposits
Bibliographic record
Abstract
Deep Electrical Resistivity Tomography (DERT) has proven to be a versatile geophysical method to investigate fluid migration systems and to explore metalliferous deposits. We present the survey conducted at Calamita iron skarn deposit, located in Tuscany, Italy. The site, though no longer actively exploited, presents unique opportunities to evaluate the application of DERT in mineral exploration. The goal is to detect causative magmatic intrusions and map the associated mineralized structures at depth.The DERT survey conducted at Calamita allowed us identifying several features that are part of a complex paleo-geothermal system. The anomaly beneath the Vallone skarn at a depth of -150 m.a.s.l is interpreted as either an altered granitic body or a pathway for the migration of magmatic fluids that are linked to mineralized surface zones. The resistivity model also highlights the presence of extension faults and a southward dip of the deposits which is consistent with the hypothesized location of the magmatic intrusion. Induced Polarization (IP) measurements further indicate widespread pyritization of schists, following their epidotization, at depths ranging from -50 to -200 m.a.s.l. Integrated resistivity and chargeability data illustrate the potential of DERT to investigate skarn deposits and support decision-making in mineral exploration. This approach is particularly effective to define hidden ore bodies and to understand the tectonic control on mineralization and could significantly reduce the uncertainty associated with drilling programs.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".