240 citations · 968 across the 42 of their papers we have counts for
9 papers · 1 filter
PINNup: Robust neural network wavefield solutions using frequency upscaling and neuron splitting
Xinquan Huang, Tariq Alkhalifah
Solving for the frequency-domain scattered wavefield via physics-informed neural network (PINN) has great potential in seismic modeling and inversion. However, when dealing with hi…
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…
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…
Solving the Eikonal equation for compressional and shear waves in anisotropic media using peridynamic differential operator
Ali Can Bekar, Erdogan Madenci, Ehsan Haghighat +2
The traveltime of compressional (P) and shear (S) waves have proven essential in many applications of earthquake and exploration seismology. An accurate and efficient traveltime co…
Wavefield solutions from machine learned functions
Tariq Alkhalifah, Chao Song, Umair bin Waheed +1
Solving the wave equation is one of the most (if not the most) fundamental problems we face as we try to illuminate the Earth using recorded seismic data. The Helmholtz equation pr…
Wavefield reconstruction inversion via physics-informed neural networks
Chao Song, Tariq Alkhalifah
Wavefield reconstruction inversion (WRI) formulates a PDE-constrained optimization problem to reduce cycle skipping in full-waveform inversion (FWI). WRI often requires expensive m…
PINNtomo: Seismic tomography using physics-informed neural networks
Umair bin Waheed, Tariq Alkhalifah, Ehsan Haghighat +2
Seismic traveltime tomography using transmission data is widely used to image the Earth's interior from global to local scales. In seismic imaging, it is used to obtain velocity mo…
A holistic approach to computing first-arrival traveltimes using neural networks
Umair bin Waheed, Tariq Alkhalifah, Ehsan Haghighat +1
Since the original algorithm by John Vidale in 1988 to numerically solve the isotropic eikonal equation, there has been tremendous progress on the topic addressing an array of chal…