most citedMLReal: Bridging the gap between training on synthetic data and real data applications in machine learning

1 citations · 1 across the 3 of their papers we have counts for

collaborators

3 papers

physics.geo-ph2025

Deep learning enhanced initial model prediction in elastic FWI: application to marine streamer data

Pavel Plotnitskii, Oleg Ovcharenko, Vladimir Kazei +2

Low-frequency data are essential to constrain the low-wavenumber model components in seismic full-waveform inversion (FWI). However, due to acquisition limitations and ambient nois…

physics.geo-ph2021★ 1 cited

MLReal: Bridging the gap between training on synthetic data and real data applications in machine learning

Tariq Alkhalifah, Hanchen Wang, Oleg Ovcharenko

Among the biggest challenges we face in utilizing neural networks trained on waveform data (i.e., seismic, electromagnetic, or ultrasound) is its application to real data. The requ…

cs.LG2021

Direct domain adaptation through reciprocal linear transformations

Tariq Alkhalifah, Oleg Ovcharenko

We propose a direct domain adaptation (DDA) approach to enrich the training of supervised neural networks on synthetic data by features from real-world data. The process involves a…