ArjunAir: Updating and parallelizing an existing time domain electromagnetic inversion program
Bibliographic record
Abstract
Summary Results from ongoing work to parallelize the existing 2.5D airborne electromagnetic inversion program ArjunAir are presented here. ArjunAir is the only code known to the authors to see extended use in the mineral exploration industry for the rigorous inversion of time domain airborne electromagnetic (EM) data using a two-dimensional (2D) conductivity model. This study sought to increase the efficiency of the code by re-implementing the most computationally expensive calculations with modern high-performance routines, employing parallel algorithms wherever possible. Distributed memory and shared memory versions of the ArjunAir forward solver have been developed. Speedups as high as 23.7 for the distributed memory code and 15 for the shared memory workstation version, relative to the original code, have been achieved. Ongoing work is focused on developing a hybrid MPI/OpenMP forward solver, and on building a minimum structure inversion code using the new implementation of the forward solver. This will replace the existing inversion algorithm, which is based on a non-linear damped least-squares fit to the data.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".