Minimum‐structure borehole gravity inversion
Bibliographic record
Abstract
The borehole gravity technique has been well established in hydrocarbon exploration geophysics since the 1970's. The concept behind borehole gravity is simply to measure the variation in the Earth's gravitational field while traveling along a borehole. Densities both close to and far from the borehole can be derived from such measurements. However, the borehole gravity technique has not yet been routinely used for mineral exploration because gravimeters that fit in the narrower diameter holes used in mineral exploration have not existed. Such gravimeters are now being developed. Complementary investigation and development of interpretation procedures for borehole gravity data in a mineral exploration context are required. Here, preliminary results are presented of a study inverting synthetic borehole gravity data for three‐dimensional, mineral exploration relevant Earth models. The forward‐modelling on which the inversion is based is a finite‐difference solution of Poisson's equation. The inversion is performed using a standard minimum‐structure algorithm. The intention is to demonstrate what we can expect to determine about the density variation around and between boreholes given varying amounts and locations of down‐hole and surface data.
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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".