Getting a better control of IP acquisitions with GDD’s new IP Post-Processing software
Bibliographic record
Abstract
There was a time when an entire day of Resistivity / Induced Polarization (IP) acquisition would have to be re-surveyed because wrong survey parameters had been set, timing errors had occurred, wind or external noise had prevented acceptable repeatability of readings, etc. This frustrating and costly outcome was due to the absence of full wave data available for the geophysicist to process. For both ground and borehole EM and IP surveys, the lack of data for post-processing and post-processing capabilities remained for a long time, until more recently some manufacturers started offering access to time series along with software to visualise and process the data.Instrumentation GDD, a Canadian manufacturer of geophysical instruments since 1976, is one of them. The GDD IP receivers’ full wave data were accessible since 2009 but users can now use the IP post-processing software. This paper will include many examples of real data collected in different part of the world for which it has been possible to: validate the nature of external noise to adjust acquisition parameters and fix final survey results, correct synchronization offset between the transmitter and the receiver, manually discard noisy half-cycles to recover data in specific cases for which the receiver algorithm did not yield satisfactory results, modify the secondary voltage (Vs) decay windows scheme in order to fine-tune chargeability responses in specific geological environments, and more.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.038 | 0.020 |
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".