@conference {cirakman2026IMAGEwwp,
	title = {Wavelet whitened patch-based diffusion prior for velocity models},
	booktitle = {International Meeting for Applied Geoscience and Energy},
	year = {2026},
	month = {8},
	abstract = {Many physical properties, such as subsurface velocity models, exhibit fractal-like 1/ f^α power spectra with α > 1, implying a power-law decay of spectral energy with frequency, where low-frequency components dominate and high-frequency con- tributions decay polynomially. This induces long-range spatial correlations and complicates the estimation of statistical quan- tities, most notably yielding ill-conditioned score functions central to score-based generative models. Recent patch-based training strategies have been introduced to mitigate the chal- lenges of high-dimensional learning; however, for data with strong long-range correlations, patching further exacerbates ill-posedness in score estimation. To address this challenge, we propose a scale- and orientation-normalized invertible wavelet transform tailored for patch-based training of score-based gener- ative models. Additionally, we introduce a sampling correction that compensates for numerical inconsistencies induced by the multiscale transform during inference. Empirical results show that, relative to baseline patch-based models, the proposed ap- proach achieves an average improvement of approximately 20\% in power spectral fidelity, while reducing training and inference time by 20\% and 50\%, respectively.},
	keywords = {Bayesian inference, deep learning, generative model, IMAGE, Imaging, Inverse problems, RTM, SEG, Summary Statistics, Uncertainty quantification, wavelet transform},
	url2 = {https://slim.gatech.edu/Publications/Public/Submitted/2026/cirakman2026IMAGEwwp/IMAGE_Ege.pdf},
	author = {Ege Cirakman and Huseyin Tuna Erdinc and Felix J. Herrmann}
}
