Study of the Moho interface and its controlling mechanism on petroleum-rich sag in Beibu Gulf Basin by satellite potential field data
Bibliographic record
Abstract
The Beibu-Gulf Basin is one of the important petroleum-bearing basins in offshore South China Sea. Decades of exploration has found great petroleum resource potential in it, but the overall petroleum geological reserves level is not very high when it comes to specific structure unit. Traditional petroleum exploration was concentrated on the shallower sediment geological conditions, however some studies have shown that there is a close relationship between petroleum resources and deep earth structures, especially the Moho interface or the crust. In this abstract we calculated the depth of Moho interface in Beibu Gulf Basin by dual-interface fast inversion algorithm and the thickness of crust with satellite potential field data. It shows that the depth of Moho shallows from the land to sea area and reaches its highest value up to 46.5 km in the northwest land area, while there is an obviously uplift in the southwest Yinggehai Basin in which the depth only comes to 12.7 km, and ranges greatly from different sags in Beibu Gulf Basin. Based on these results, we researched the quantitative relationship between the distribution of petroleum-rich sags and the fluctuation deviation of Moho depth and its horizontal gradient, together with the stretch factor of crust. We also found that there is a strong correlation among the uplift zone of the Moho or the thinning area of crust (stretch factor>1.0) and the oil and gas sources or gathering places, which will produce a beneficial temperature, pressure, chemistry as well as structure condition for organic matter to form oil and gas. So this research will offer a perspective about the controlling mechanism of the differential distribution in petroleum-rich sags due to the deep earth structure, and help for the further selection of target areas in Beibu Gulf Basin.
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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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".