Adaptive filtering of random noise in 2-D geophysical data
Bibliographic record
Abstract
Abstract Random noise is often a problem in geophysical data visualization because it obscures fine details and complicates identification of image features. Adaptive filters have recently been used to suppress speckle (random) noise in synthetic aperture radar (SAR) images. SAR data are similar to seismic reflection data, both in their data acquisition approach and in their final data processed format. The nature of the random noise associated is also very similar, and adaptive filters can be applied to reduce random noise in both types of data sets. In this paper several popular adaptive filters—the Lee filter, the Frost filter, and the Kuan filter, which have been used frequently for speckle reduction in SAR data—are tested on Lithoprobe (AGT) deep seismic reflection data and on one set of oil industry shallow seismic reflection data. In addition, a standard band-pass filter, which is common in many seismic data processing packages, is tested with the oil industry test data. Performance of the adaptive filters is also tested on Radarsat SAR data. The random (speckle) noise in both the Lithoprobe and the Radarsat (SAR) data sets is statistically very similar, and the adaptive filters tested successfully suppressed random noise while minimizing blurring. Among the tested filters the enhanced Lee filter performed best, closely followed by the enhanced Frost filter and the Kuan filter. The background noise in the oil industry seismic data is statistically quite different from the above two data sets; the results obtained were less than satifactory, although they were still encouraging. With the oil industry data, the enhanced Frost and Kuan filters performed better than the enhanced Lee filter. The commonly used band-pass filter successfully removed the background (random) noise, but it also suppressed the reflection events, making the final result less desirable.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".