Extraction of reflected events from sonic-log waveforms using the Karhunen-Loève transform
Bibliographic record
Abstract
ABSTRACT Sonic-reflection logging, a recently developed borehole geophysical scheme, is in principle capable of providing a clear view of outside the well bore. In this type of acoustic well logging, a key technical obstacle is that the reflected wave signal is almost entirely obscured by the directly arriving P-, S-, and Stoneley wave modes. Effective extraction of these reflection signals from the full acoustic waveforms is therefore a critical data-processing step. We have examined the use of the Karhunen-Loève (KL) transform, combined with a band-limiting filter, as a technique for the extraction of reflections of interest from a mixture with directly arriving wave modes of much higher amplitude. Under the assumption that large energy (squared-amplitude) differences exist between each wave component, the direct Stoneley wave, S-wave, and the P-wave are eliminated sequentially by subtracting the most significant principal components, after which the remaining signal is seen to be dominated by reflected events. Thereafter, the extracted reflections can be used in migration to provide interpretable images of the structures outside the borehole. Synthetic data are used to develop and justify our procedure for subtraction of appropriate KL principal components. Laboratory data are used to demonstrate in detail the suppression of unwanted modes. For comparison, the multiscale slowness-time-coherence method is applied to extract reflections from the same data set. The procedure is exemplified on a field data case with attention paid in particular to the consequences to imaging of near-borehole structures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".