Reprocessing of Regional 2D Marine Seismic Data of Part of Taranaki Basin, New Zealand Using Latest Processing Techniques
Bibliographic record
Abstract
This study employed the use of various newly developed seismic data processing techniques which were unavailable as at the time (1986) of acquisition of the regional 2D marine seismic data (TRV 434) of part of Taranaki Basin, New Zealand, to reprocess the data in order to improve the volume as well as the quality of subsurface information derivable from the data which remain one of the vital sources of information for preliminary insight for petroleum prospect evaluation of the basin. The reprocessing operations attenuated various unwanted signals associated with the seismic data, F – K transform filter filtered out low frequency noise including swell noise while other noise types embedded in the seismic data were attenuated using Time Variant Omsby-Bandpass filters. Predictive deconvolution attenuated water bottom multiples as well as other periodic unwanted signals. True amplitude recovery technique restored lost reflection energies and made deeper reflections visible. Post and Pre-Stack Time Kirchhoff migration (PSTM) techniques appropriately repositioned dipping reflection events to their appropriate locations in time and space. Diffraction curves were collapsed to improve data resolution of both the shallow and deep reflection events. The reprocessing activities generally increased the illuminating strength of the TRV 434 marine seismic data to image the subsurface of the surveyed part of Taranaki Basin which presented complex subsurface geology in terms of structures and rock association.
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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 | 0.001 |
| 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".