Data processing and interpretation schemes for a deep-towed high-frequency seismic system for gas and hydrate exploration
Bibliographic record
Abstract
The deep-towed Autonomous Cable Seismic (ACS) system is a high-resolution acoustic survey system designed for use in deep-water environments. This system uses a high-frequency acoustic source and a multichannel receiver cable. A common problem in the analysis of deep-towed ACS data is the unstable positioning of the source and receivers due to ocean currents and seafloor bathymetry. Since the data acquisition using high-frequency source with unstable source–receiver positions causes destructive interference on the final stack profile, correction of the unstable source-receiver is a crucial issue. In this study, we propose a method to solve the unstable source–receiver position problem and thus to construct an accurate final stack profile. We used deep-towed ACS data acquired in the Joetsu Basin in Niigata, Japan, where hydrocarbon features in the form of gas chimneys, gas hydrate, and free gas have been observed. Because sidelobes in the ACS source signature defocus the source wavelet and decrease the bandwidth frequency content, we designed a filter to focus the source signature. Our proposed approach considerably improved the quality of the final stack profile. Even though depth information was not available for all receivers, the velocity spectra in the velocity analysis were well focused. Furthermore, shaping the source wavelet considerably increased the bandwidth frequency of the source signature. We applied seismic attribute analysis to the post-stack profile to identify the distributions of free gas and hydrate. Our seismic attribute analyses for the high-frequency ACS data demonstrated that free gas accumulations are characterized by low reflection amplitude and an unstable frequency component, and that hydrate close to the seafloor can be identified by its high reflection amplitude.
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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 | 0.001 | 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 | 0.002 |
| 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".