Vladimir Kazei
Research Geophysicist at Aramco
Houston, Texas, United States
Summary
👤
Senior🎓
Top SchoolVladimir Kazei is a research geophysicist with a decade of experience applying full-waveform inversion, machine learning, and Distributed Acoustic Sensing to seismic inverse problems. Based in Houston and currently at Aramco, he blends advanced ML techniques with domain expertise to push practical seismic imaging and time-lapse monitoring. His background spans academic and industry research roles at KAUST, University of Hamburg, and Saint Petersburg State University, where he developed pseudo-spectral FWI tools and anisotropic modeling. Vladimir holds a PhD in Geophysics and Seismology and augmented his skill set with a Deep Learning Nanodegree, enabling him to bridge computational seismology and modern ML workflows. He is comfortable coding scientific workflows (Matlab, Fortran, Bash) and translating wavefield simulation insights into production-ready inversion pipelines. Notably, he brings a rare combination of deep theoretical grounding and hands-on implementation experience in DAS-informed seismic inversion.
10 years of coding experience
8 years of employment as a software developer
Doctor of Philosophy (PhD), Geophysics and Seismology, Doctor of Philosophy (PhD), Geophysics and Seismology at Saint Petersburg State UniversityBachelor of Science (B.Sc.), Mathematical Physics, excellent with distinction, Bachelor of Science (B.Sc.), Mathematical Physics, excellent with distinction at Санкт-Петербургский Государственный Университет / Saint Petersburg State UniversityDeep Learning Nanodegree, Computer Science, Deep Learning Nanodegree, Computer Science at Udacity
Stackoverflow
1reputation
0reached
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Github Skills (43)
frequency-domain10
regularization10
inversion9
matlab9
quaternions9
wave9
geodesy9
deep-learning9
geophysics9
elastic8
bag8
modeling8
waveform8
deep-neural-networks8
reproducible-science7
Programming languages (7)
Github contributions (5)
vkazei/fastFWI
Apr 2019 - Nov 2020
Simple frequency domain full-waveform inversion (FWI) regularized by Sobolev space norm
Contributions:12 commits, 1 push, 1 comment in 1 year 7 months
frequency-domainfull-waveform-inversionhelmholtz-equationregularizationsobolev-space-norm
vkazei/deeplogs
Jun 2019 - Sep 2020
Velocity model building by deep learning. Multi-CMP gathers are mapped into velocity logs.
Contributions:2 releases, 37 commits, 15 pushes in 1 year 2 months
deep-learningdeep-neural-networksseismic-imagingseismic-inversionfull-waveform-inversion
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