Adewale Amosu

Adewale Amosu

Graduate Student

Rock physics, spectral decomposition, machine learning, deep learning, AI, data analysis, statistical methods in geosciences, uncertainty quantification in subsurface models

  Halbouty 59


My research addresses challenges encountered in the exploration of Earth’s resources using various computational, data analysis and artificial intelligence tools, in areas of interest such as petroleum geology, petrophysics, reservoir rock physics, and statistical and mathematical methods in geosciences.

Selected Publications

  • Amosu, A., and Y. Sun, 2021, Identification of thermally mature TOC-rich layers in shale formations using an effective machine learning approach: Interpretation, 9, no. 3, T735-T745.
  • Amosu, A., M. Imsalem, and Y. Sun, 2021, Effective machine learning identification of TOC-rich zones in the Eagle Ford Shale: Journal of Applied Geophysics, 104311.
  • Amosu, A., and Y. Sun, 2019, A quantitative probabilistic framework for estimating the critical moment in a petroleum system: AAPG Bulletin, 103, no. 1, 177-187.
  • Amosu, A., and Y. Sun, 2018, MinInversion: a program for petrophysical composition analysis of geophysical well log data: Geosciences, 8, no. 2, 65.
  • Amosu, A., and Y. Sun, 2017, WheelerLab: An interactive program for sequence stratigraphic analysis of seismic sections, outcrops and well sections and the generation of chronostratigraphic sections and dynamic chronostratigraphic sections: SoftwareX, 6, 19-24.


B.Sc. Physics (2006), Bowen University, Nigeria.

M.Sc. Earth System Physics (2009), International Center for Theoretical Physics

Additional Information

Advisor: Yuefeng Sun

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