Mapping local singularities using magnetic data to investigate the volcanic rocks of the Qikou depression, Dagang oilfield, eastern China
Bibliographic record
Abstract
Abstract. The spatial structural characteristics of geological anomaly, including singularity and self-similarity, can be analysed using fractal or multifractal modelling. Here we apply the multifractal methods to potential fields to demonstrate that singularities can characterise geological bodies, including rock density and magnetic susceptibility. In addition to enhancing weak gravity and magnetic anomalies with respect to either strong or weak background levels, the local singularity index (α ≈ 2) can be used to delineate the edges of geological bodies. Two models were established to evaluate the effectiveness of mapping singularities for extracting weak anomalies and delineating edges of buried geological bodies. The Qikou depression of the Dagang oilfield in eastern China has been chosen as a study area for demonstrating the extraction of weak anomalies of volcanic rocks, using the singularity mapping technique to analyse complex magnetic anomalies caused by complex geological background. The results have shown that the singularities of magnetic data mapped in the paper are associated with buried volcanic rocks, which have been verified by both drilling and seismic survey, and the S–N and E–W faults in the region. The targets delineated for deeply seated faults and volcanic rocks in the Qikou depression should be further investigated for the potential application in undiscovered oil and gas reservoirs exploration.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".