Technical Paper Geophysics- More Than Numbers Processing and Presentation of Geophysical Data
Bibliographic record
Abstract
Geophysical techniques are now routinely applied to groundwater and environmental studies and geologists are forced to deal with the ever increasing volumes of numbers that are generated. Geophysical data can and should be processed to extract the maximum amount of useful information and the results presented to effectively convey the meaning of the data to others, who are often less specialized. With common low-cost computers we are able to process and present geophysical data in many more useful, creative and revealing ways than the traditional contour map. Geophysical surveys are sensitive to instrument accuracy, survey methodology, cultural noise, geologic noise (the geology that we are NOT interested in) and the geologic model that we are studying. The intelligent application of filters can be used to both remove the effects of noise and to enhance those components of the data that are of interest. It is important to understand the relationship of filters, gridding methods, sample density and noise characteristics in order to use processed data effectively. This paper will use a number of practical examples to illustrate the use of filters in processing, as well as the use of colour, shading and perspective to help visualize and enhance the results of geophysical studies. The examples include conductivity data from a waste disposal site in Novo Horizontal in Brazil, VLF data from a land-fill site near North Bay, Ontario, radar data from an overburden study at Chalk River, Ontario and magnetometer data from a study to locate abandoned water wells at the Rocky Mountain Arsenal in Colorado.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.110 | 0.094 |
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".