5 papers
Coarse-to-fine multi-resolution hash encoding for implicit full waveform inversion
Linrong Wang, Shaowen Wang, Fan Min +1
Implicit full waveform inversion (IFWI) reparameterizes the velocity model with a neural network, but it suffers from spectral bias and requires many iterations to converge. Multi-…
SWEEP (Seismic Wave Equation Exploration Platform): A Unified Solver Framework for Differentiable Wave Physics
Shaowen Wang, Tariq Alkhalifah
SWEEP (Seismic Wave Equation Exploration Platform) is a unified and extensible wave equation solver library designed for wavefield modeling and inversion. It supports a wide range…
Target noise: A pre-training based neural network initialization for efficient high resolution learning
Shaowen Wang, Tariq Alkhalifah
Weight initialization plays a crucial role in the optimization behavior and convergence efficiency of neural networks. Most existing initialization methods, such as Xavier and Kaim…
High-resolution velocity model estimation with neural operator and the time-shift imaging condition
Xiao Ma, Shaowen Wang, Tariq Alkhalifah
Extracting subsurface velocity information from seismic data is mainly an undetermined problem that requires injecting a priori information to constrain the inversion process. Mach…
Inspired by machine learning optimization: can gradient-based optimizers solve cycle skipping in full waveform inversion given sufficient iterations?
Xinru Mu, Omar M. Saad, Shaowen Wang +1
Full waveform inversion (FWI) iteratively updates the velocity model by minimizing the difference between observed and simulated data. Due to the high computational cost and memory…