Generating fractal rough surfaces with the spectral representation method

Wang, Yuechang; Azam, Abdullah; Wilson, Mark C. T.; Neville, Anne; Morina, Ardian

doi:10.1177/13506501211049624

Cited by 7 publications

(2 citation statements)

References 48 publications

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“…To generate the random fracture topography, the spectral representation method has been used (Wang et al, 2021;Wu, 2000). This method is based on the fact that self-affine surfaces have the following Power Spectral Density (PSD):…”

Section: Appendix A: Generation Of Random Fracture Surfacesmentioning

confidence: 99%

Significance of Low‐Velocity Zones on Solute Retention in Rough Fractures

Sanglas,

Trinchero,

Painter

et al. 2024

Water Resources Research

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Natural fractures are characterized by high internal heterogeneity. This internal variability is the cause of flow channeling, which in turn leads to contaminant transport taking place primarily along the high‐velocity channels. Mass exchange between the high‐velocity channels and the low‐velocity zones has the potential to enhance contaminant retention, due to solute diffusion into the low‐velocity zones and subsequent exposure to additional surface area for diffusion into the bordering rock matrix. Here, we derive a random walk particle tracking method for heterogeneous fractures, which includes an additional term to account for the aperture gradient. The method takes into account advection, diffusion in the fracture and matrix diffusion. The developed numerical framework is applied to assess the effect of low‐velocity zones in rough self‐affine fractures. The results show that diffusion into low‐velocity zones has a visible but modest impact on contaminant retention. The magnitude of this impact does not change considerably, regardless of whether diffusion into the rock matrix is considered in the model, and increases for a decreasing average Péclet number of the fracture.

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Section: Appendix A: Generation Of Random Fracture Surfacesmentioning

confidence: 99%

Significance of Low‐Velocity Zones on Solute Retention in Rough Fractures

Sanglas,

Trinchero,

Painter

et al. 2024

Water Resources Research

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“…Non-Gaussianities are expected in actual atmospheric data, and thus, the spectral density may not contain all relevant information. Nonetheless, our scintillation model could be combined with iterative techniques like the Johnson translator system (see, e.g.,Wang et al 2018Wang et al , 2021 to simulate atmospheric series with a given skewness and kurtosis.…”

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confidence: 99%

Time-domain Deep-learning Filtering of Structured Atmospheric Noise for Ground-based Millimeter Astronomy

Alejandra¹,

Rodríguez‐Montoya²,

Sánchez-Argüelles³

et al. 2022

ApJS

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The complex physics involved in atmospheric turbulence makes it very difficult for ground-based astronomy to build accurate scintillation models and develop efficient methodologies to remove this highly structured noise from valuable astronomical observations. We argue that a deep-learning approach can bring a significant advance to treat this problem because of deep neural networks’ inherent ability to abstract nonlinear patterns over a broad scale range. We propose an architecture composed of long short-term memory cells and an incremental training strategy inspired by transfer and curriculum learning. We develop a scintillation model and employ an empirical method to generate a vast catalog of atmospheric-noise realizations and train the network with representative data. We face two complexity axes: the signal-to-noise ratio (S/N) and the degree of structure in the noise. Hence, we train our recurrent network to recognize simulated astrophysical pointlike sources embedded in three structured-noise levels, with a raw-data S/N ranging from 3 to 0.1. We find that a slow and repetitive increase in complexity is crucial during training to obtain a robust and stable learning rate that can transfer information through different data contexts. We probe our recurrent model with synthetic observational data, designing alongside a calibration methodology for flux measurements. Furthermore, we implement traditional matched filtering (MF) to compare its performance with our neural network, finding that our final trained network can successfully clean structured noise and significantly enhance the S/N compared to raw data and in a more robust way than traditional MF.

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