Open-source geophysical software development for groundwater applications
Bibliographic record
Abstract
The work we present was motivated by our Geoscientists Without Borders (GWB) Project, which builds capability for the community in Myanmar to acquire and interpret direct current (DC) resistivity data to find groundwater resources. For this, we improved the DC portion of the existing open-source geophysical software module, SimPEG-DC. Our main goal was to generate 1D and 2D inversion software that local engineers and students could readily run within a reasonable amount of time (less than 5 minutes) using their own computers. Three main improvements were: (a) the development of the 1D layered-earth solution for DC, (b) the implementation of semi-structured meshes in 2D and 3D, and (c) a novel projection methodology which reduces any electrode configuration to unique pole-pole source-receiver pairs within a DC survey for forward modelling and sensitivity calculations. To highlight the impact of our improvements, we applied 1D, 2D, and 3D inversions to field DC data sets obtained at Kawpiphtaw Village Mon State, Myanmar, and interpreted hydrostratigraphy of the region. For both the 2D and 3D codes, the reduction in runtime was at least a factor of 10 and memory usage was reduced; this allowed local users to run both 2D and 3D inversions within several minutes. Presentation Date: Wednesday, October 14, 2020 Session Start Time: 9:20 AM Presentation Time: 9:20 AM Location: Poster Station 5 Presentation Type: Poster
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.046 | 0.030 |
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".