Reassessing tectono-structural units and crustal thickness variations in southwest Cameroon using satellite gravity and seismic data
Bibliographic record
Abstract
Southwest Cameroon has long lacked detailed geological data, prompting an investigation into its geological and tectonic features through innovative edge detection techniques applied to gravity data. We employed various methods, including the Analytical Signal (AS), Tilt Angle (TDR), Total Horizontal Gradient of the Tilt Angle (TDR_THDR), and the Logistic Filter (IL) to delineate geological features that manifest as lineaments and contacts on gravity anomaly maps. Main findings of this study involve identification of key tectonic boundaries, notably the Kribi-Campo faults (KCF) and the Ebolowa pseudo-karstic circular network, the latter being mapped for the first time. This work reveals the continuity of the Ebolowa network and integrates connections such as the Mbilibekon and Mfuda caves, enhancing our understanding of the region's geological evolution. Additionally, we detailed the north-south extension of the KCF, which extends into both oceanic and terrestrial domains, aligning with the eastern margin of the Congo Craton. This finding suggests the fault system's role as a transition between the stable cratonic core and the dynamic mobile belts, necessitating a re-evaluation of regional tectonic models. To support our edge detection interpretations, we estimated Moho depths using gravity and seismic data, finding a Moho depth range between 34.5 km to 45.9 km, correlating with significant faulting and sedimentary features. This research enhances the understanding of subsurface structures in Southwestern Cameroon and provides a robust foundation for future geological and geophysical studies, emphasizing the critical interplay between surface and subsurface processes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".