Geostatistical interpolation of estimated RQD values and its use in geomechanics design considerations – a case study
Bibliographic record
Abstract
Geostatistical interpolation techniques have proved its utility in all disciplines of geosciences and have potential to be used in geomechanics design applications. The principals of geostatistics are developed based on the fact that almost all geosciences data has some relationship with its location in space. Geostatistical tools decipher this relationship and exploit it to estimate the rock properties at an unsampled location. This work is an attempt to apply geostatistics in geomechanical design using nearest neighbour, inverse power and kriging interpolation of estimated Rock Quality Designation index (RQD) values from drill cores from part of one and four shear orebodies of Vale’s Garson Mine in Sudbury, Ontario, Canada. A comparative study of different methods and their correlation with mapped geology provided the most effective method for RQD interpolation. The part of orebody selected for this application has undergone intensive drilling with sufficient data volume to create an RQD block model with high degree of confidence. The RQD block model provided a basis for rock mass classification and creation of RQD maps along planned mining layouts, which served as an important tool for initial geomechanical assessment. This methodology has a potential to expand its application to other geomechanics parameters, such as RMR and Q values.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".