I am a PhD Candidate at King Abdullah University of Science and Technology (KAUST) in Seismic Modeling and Inversion group run by Prof. Daniel Peter. I am broadly interested in numerical methods in geophysics, which includes but not limited to Machine Learning, Seismic Imaging and HPC. My PhD research is focused on developing ML-based methods to improve seismic inversion.


Recent research

  • Deep Learning for Seismic Data Reconstruction: Opportunities and Challenges

    Research project from my internship in CGG, Crawley, UK. Under supervision of Song Hou I explored benefits and pitfalls of using GANs for seismic data reconstruction. In the nutsell, high perceptual realism of reconstructed seismic data is not sufficient for real-world application. Physics also should be constrained. First EAGE Digitalization Conference and Exhibition, Nov 2020

  • Extrapolating low-frequency prestack land data with deep learning

    In synthetic framework we explore capability of a simple neural network to reconstruct low-frequency components of land data. SEG Technical Program Expanded Abstracts 2020

  • Style transfer for generation of realistically textured subsurface models

    We apply an iterative style transfer approach from image processing to produce realistically textured subsurface models based on synthetic prior models. These realistically textured models to be used in training datasets for machine learning applications in geophysics. Presented in Sep 2019, on SEG Annual meeting in San Antonio, USA

  • Transfer learning for low frequency extrapolation from shot gathers for FWI applications

    Here, we propose and utilize transfer learning to reduce the computational efforts for optimal architecture search and initial network training. We re-train the light-weight MobileNet convolutional network to infer low-frequency data from a frequency-domain representation of individual shot-gathers, which leads to an efficient, yet accurate inference of low fre- quencies according to wavenumber theory. Presented in Jun 2019, on EAGE Annual meeting in London, UK


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