Introduction to Seismic Imaging
Bibliographic record
Abstract
Intensive seismic exploration for naturally occurring gas hydrate probably started in the 1970s with the first discovery of bottom-simulating reflectors (BSRs) in marine seismic profiles (Markl et al., 1970; Shipley et al., 1979). The BSR is inferred to mark the base of the gas-hydrate stability field and represents an acoustic impedance contrast that is interpreted to correspond to gas-hydrate-bearing sediments (high impedance) above the interface underlain by free-gas-bearing sediments (lower impedance). Various attempts were made to infer gas hydrate and/or free-gas concentrations from the reflection amplitude of the BSR, but considerable ambiguity exists in the interpretation whether the reflection amplitude can be related solely to the gas-hydrate content above or free gas below (e.g., Fink and Spence, 1999), despite various attempts to exploit advance seismic processing techniques such as amplitude-variation-with-offset (AVO) modeling and inversion (Hyndman and Spence, 1992; Ecker et al., 1998; Chen et al., 2007), full waveform inversion (Minshull et al., 1994; Singh and Minshull, 1994; Yuan et al., 1996; Yuan et al., 1999) or impedance inversion (e.g., Grevemeyer et al., 2000). A common problem in imaging the BSR is the varying response of this complex interface with seismic frequency, as demonstrated by various authors (e.g., Chapman et al., 2002). The latest deep-drilling expeditions carried out on active as well as passive continental margins have also shown that there is considerable complexity in the gas-hydrate content and distribution and that the BSR cannot easily be related to gas-hydrate concentrations above the base of gas-hydrate stability (Riedel et al., 2006; Collett et al., 2008). Despite this complexity and ambiguity, the presence of a BSR is a first indicator in seismic data for the potential presence of gas hydrate in the sedimentary section.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.059 | 0.035 |
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".