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Sankore

Named after the University of Sankore in Timbuktu — one of the world’s oldest centres of learning. Every figure here is computed from work-level records in our own graph, never copied from a source’s summary count.

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Coverage is partial and says so. A country appears with its research output only once its works have been swept; until then the portal shows whatever the graph happens to hold for it and labels it as exactly that.

Improving subsurface characterization in tropical granitic terrains using machine learning and deep learning-enhanced resistivity–chargeability modeling

2026Scientific ReportsJournalOpen access

10.1038/s41598-026-62674-1 Full text0 citations

Authors

7 authors across 10 institutions in 6 countries.

Adedibu Sunny AkingboyeFirst authorCorresponding
Andy Anderson Bery
Hui Tang
Ayokunle Olalekan Ige
Obinna Chigoziem Akakuru
- Federal University of Technology Owerri (NG)
Gabriel Abraham Bala
Mbuotidem David DickSenior author
- Akwa Ibom State University (NG)
- Akwa Ibom State Polytechnic (NG)

Fields

Assignment is probabilistic — a work belongs to several fields in proportions.

Geophysical and Geoelectrical Methodslevel 3 · 0.97
Seismic Waves and Analysislevel 3 · 0.01
Groundwater and Watershed Analysislevel 3 · 0.01

Sources

Several sources describing one work is the point — it means the record rests on more than one authority.

openalexseen September 9, 2026
2026 · Scientific Reports · 0 citations
https://openalex.org/W7203637613

Improving subsurface characterization in tropical granitic terrains using machine learning and deep learning-enhanced resistivity–chargeability modeling · Sankore