Finite element modelling of IP anomalous effect from bodies of any shape located in rugged relief area
Bibliographic record
Abstract
Finite element modelling of sections is carried out in two cases: (a) Ore bodies with a massive texture and an electrical resistivity in contrast with the resistivity of the surrounding rocks, where 2.5D modelling is applied; (b) Ore bodies with a disseminated texture having no contrast of resistivity with the surrounding rocks, where 3D models are under use. Two parameters characterize the constructed model - the apparent resisitivity and the induced polarization. The effect of the relief as well as of the global geological structure is taken into account. Case studies shown demonstrate different effects and usability of the modelling by finite elements. The results of such a modelling are presented according to a new method of geoelectrical real section, proposed by Perparim Alikaj, and developed recently in QUANTEQ IP Inc., Canada. They are a synthesis of many year's works of the authors in collaboration with this company in a number of projects. INTRDUCTION Resolving geophysical problems means a finite iteration of the couple interpretation ↔ modelling. Theoretical models exist for a number of ideal cases, rarely found in nature. The problem becomes more complicated when the depth of investigation increases, together with an increase in the secondary effects caused by the relief and the geological inhomogeneity in depth. In this paper the problem of modelling real sections is treated using finite elements to solve elliptic equations in a heterogeneous medium related to complex geological situations and rugged relief. This procedure is used both for resistivity and IP modelling.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".