Imaging fault-controlled hydrothermal dolomite bodies in the subsurface: insights from 3D seismic modelling of the Hammam Faraun Fault system, Suez Rift, Egypt
Bibliographic record
Abstract
Constraining and detecting fault-controlled hydrothermal dolomite bodies in the subsurface generally depends on the use of seismic reflection data, but the practical understanding of how dolomite presence affects seismic images is limited. Using 3D seismic modelling, we here attempt to bridge this gap in understanding by linking outcrop data from fault-controlled hydrothermal dolomite to their response in seismic images. We investigate nine seismic models that contain fault-controlled stratabound and non-stratabound dolomite bodies, testing the sensitivity of seismic images to dolomite–limestone acoustic impedance, seismic wavelet selection and noise. The results show that (1) massive dolomite bodies are generally well imaged albeit transparent and non-reflective, (2) the presence and restriction of stratabound dolomite bodies to certain stratigraphic intervals can be interpreted or inferred, but that (3) individual stratabound dolomite bodies cannot be reliably detected or identified. Furthermore, (4) seismic images elucidate dolomite trends along and across bounding faults, and (5) stratigraphic trends in dolomite presence are clearly distinguishable in the models. The findings are important for understanding seismic imaging of dolomite in the subsurface, which in turn has implications for understanding fluid flow, for instance associated with groundwater management, geothermal energy and petroleum, as well as in underground storage of CO 2 and hydrogen.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".