3D hybrid imaging based on gravity migration and regularized focusing inversion to predict the Poyang Basin interface
Bibliographic record
Abstract
ABSTRACT Describing and understanding the basement relief of sedimentary basins is vital for oil and gas exploration. The traditional method to map an interface in each spatial direction is based on 3D modeling of gravity Bouguer anomalies with variable lateral and vertical density contrasts using a priori information derived from other types of geoscience data sets as constraints (e.g., well and/or seismic data). However, in the preexploration stage, vertical gravity gz, which is sometimes the only available geophysical data, is typically used to recover smooth density contrast distributions under a generic set of constraints. Apparently, the use of the gz component is not sufficient to produce geologically reasonable interpretations with high resolution. To address this, we have developed a novel process of hybrid inversion, combining gravity migration and inversion using the same gz data set, to distinguish the complicated interface between basement and sedimentary basin rocks from a full-space inverted density distribution volume. First, a 3D-migrated model delineating the basic sedimentary basin structure is derived using a focusing gravity iterative migration method, where a priori information is not necessary. Subsequently, under the framework of the regularized focusing conjugate inversion algorithm, a high-resolution density contrast model is inverted for delineation of the basement boundary by integrating the 3D-migrated density model as a priori information. We examine the method using one synthetic example and a field data case, of which a transformed resolution density matrix is developed from logarithmic space to qualitatively evaluate the practical resolutions. The high resolution of the density distribution of the Cretaceous basement with a clear interface is achieved and verified by limited seismic data and strata markers in limited wells.
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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 | 0.000 |
| 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".