240 citations · 968 across the 42 of their papers we have counts for
28 papers · 1 filter
A new practical and effective source-independent full-waveform inversion with a velocity-distribution supported deep image prior: Applications to two real datasets
Chao Song, Tariq Alkhalifah, Umair Bin Waheed +2
Full-waveform inversion (FWI) is an advanced technique for reconstructing high-resolution subsurface physical parameters by progressively minimizing the discrepancy between observe…
Discovery of physically interpretable wave equations
Shijun Cheng, Tariq Alkhalifah
Using symbolic regression to discover physical laws from observed data is an emerging field. In previous work, we combined genetic algorithm (GA) and machine learning to present a…
A self-supervised learning framework for seismic low-frequency extrapolation
Shijun Cheng, Yi Wang, Qingchen Zhang +2
Full waveform inversion (FWI) is capable of generating high-resolution subsurface parameter models, but it is susceptible to cycle-skipping when the data lack low-frequency. Unfort…
A self-supervised scheme for ground roll suppression
Sixiu Liu, Claire Birnie, Andrey Bakulin +3
In recent years, self-supervised procedures have advanced the field of seismic noise attenuation, due to not requiring a massive amount of clean labeled data in the training stage,…
Robust data driven discovery of a seismic wave equation
Shijun Cheng, Tariq Alkhalifah
Despite the fact that our physical observations can often be described by derived physical laws, such as the wave equation, in many cases, we observe data that do not match the law…
Semi-blind-trace algorithm for self-supervised attenuation of trace-wise coherent noise
Mohammad Mahdi Abedi, David Pardo, Tariq Alkhalifah
Trace-wise noise is a type of noise often seen in seismic data, which is characterized by vertical coherency and horizontal incoherency. Using self-supervised deep learning to atte…
GaborPINN: Efficient physics informed neural networks using multiplicative filtered networks
Xinquan Huang, Tariq Alkhalifah
The computation of the seismic wavefield by solving the Helmholtz equation is crucial to many practical applications, e.g., full waveform inversion. Physics-informed neural network…
Learnable Gabor kernels in convolutional neural networks for seismic interpretation tasks
Fu Wang, Tariq Alkhalifah
The use of convolutional neural networks (CNNs) in seismic interpretation tasks, like facies classification, has garnered a lot of attention for its high accuracy. However, its dra…
Optimizing a Transformer-based network for a deep learning seismic processing workflow
Randy Harsuko, Tariq Alkhalifah
StorSeismic is a recently introduced model based on the Transformer to adapt to various seismic processing tasks through its pretraining and fine-tuning training strategy. In the o…
Gabor-based learnable sparse representation for self-supervised denoising
Sixiu Liu, Shijun Cheng, Tariq Alkhalifah
Traditional supervised denoising networks learn network weights through "black box" (pixel-oriented) training, which requires clean training labels. The uninterpretability nature o…
Meta-Processing: A robust framework for multi-tasks seismic processing
Shijun Cheng, Randy Harsuko, Tariq Alkhalifah
Machine learning-based seismic processing models are typically trained separately to perform specific seismic processing tasks (SPTs), and as a result, require plenty of training d…
Joint Microseismic Event Detection and Location with a Detection Transformer
Yuanyuan Yang, Claire Birnie, Tariq Alkhalifah
Microseismic event detection and location are two primary components in microseismic monitoring, which offers us invaluable insights into the subsurface during reservoir stimulatio…
A prior regularized full waveform inversion using generative diffusion models
Fu Wang, Xinquan Huang, Tariq Alkhalifah
Full waveform inversion (FWI) has the potential to provide high-resolution subsurface model estimations. However, due to limitations in observation, e.g., regional noise, limited s…
Physics reliable frugal uncertainty analysis for full waveform inversion
Muhammad Izzatullah, Matteo Ravasi, Tariq Alkhalifah
Full waveform inversion (FWI) enables us to obtain high-resolution velocity models of the subsurface. However, estimating the associated uncertainties in the process is not trivial…
PINNslope: seismic data interpolation and local slope estimation with physics informed neural networks
Francesco Brandolin, Matteo Ravasi, Tariq Alkhalifah
Interpolation of aliased seismic data constitutes a key step in a seismic processing workflow to obtain high quality velocity models and seismic images. Building on the idea of des…
Integrating U-nets into a Multi-scale Waveform Inversion for Salt Body Building
Abdullah Alali, Tariq Alkhalifah
In salt provinces, full-waveform inversion (FWI) is most likely to fail when starting with a poor initial model that lacks the salt information. Conventionally, salt bodies are inc…
Microseismic source imaging using physics-informed neural networks with hard constraints
Xinquan Huang, Tariq Alkhalifah
Microseismic source imaging plays a significant role in passive seismic monitoring. However, such a process is prone to failure due to aliasing when dealing with sparsely measured…
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…
Coherent noise suppression via a self-supervised blind-trace deep learning scheme
Sixiu Liu, Claire Birnie, Tariq Alkhalifah
Coherent noise regularly plagues seismic recordings, causing artefacts and uncertainties in products derived from down-the-line processing and imaging tasks. The outstanding capabi…
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…
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…