41 citations · 44 across the 10 of their papers we have counts for
13 papers
Transfer learning for self-supervised, blind-spot seismic denoising
Claire Birnie, Tariq Alkhalifah
Noise in seismic data arises from numerous sources and is continually evolving. The use of supervised deep learning procedures for denoising of seismic datasets often results in po…
-FWI: Robust full-waveform inversion with Fourier-based metric
Muhammad Izzatullah, Tariq Alkhalifah
Full-waveform inversion is a cutting-edge methodology for recovering high-resolution subsurface models. However, one of the main conventional full-waveform optimization problems ch…
Time-lapse data matching using a recurrent neural network approach
Abdullah Alali, Vladimir Kazei, Bingbing Sun +1
Time-lapse seismic data acquisition is an essential tool to monitor changes in a reservoir due to fluid injection, such as CO injection. By acquiring multiple seismic surveys i…
Deep learning unflooding for robust subsalt waveform inversion
Abdullah Alali, Vladimir Kazei, Mahesh Kalita +1
Full-waveform inversion (FWI), a popular technique that promises high-resolution models, has helped in improving the salt definition in inverted velocity models. The success of the…
The potential of self-supervised networks for random noise suppression in seismic data
Claire Birnie, Matteo Ravasi, Tariq Alkhalifah +1
Noise suppression is an essential step in any seismic processing workflow. A portion of this noise, particularly in land datasets, presents itself as random noise. In recent years,…
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…