Abstract
Spatial data captured with sensors of different resolution would provide a maximum degree of information if the data were to be merged into a single image representing all scales. We develop a general solution for merging multiscale categorical spatial data into a single dataset using stochastic reconstructions with rescaled correlation functions. The versatility of the method is demonstrated by merging three images of shale rock representing macro, micro and nanoscale spatial information on mineral, organic matter and porosity distribution. Merging multiscale images of shale rock is pivotal to quantify more reliably petrophysical properties needed for production optimization and environmental impacts minimization. Images obtained by X-ray microtomography and scanning electron microscopy were fused into a single image with predefined resolution. The methodology is sufficiently generic for implementation of other stochastic reconstruction techniques, any number of scales, any number of material phases, and any number of images for a given scale. The methodology can be further used to assess effective properties of fused porous media images or to compress voluminous spatial datasets for efficient data storage. Practical applications are not limited to petroleum engineering or more broadly geosciences, but will also find their way in material sciences, climatology, and remote sensing.
Highlights
Assembling spatial information across a wide range of scales is a crucial component in almost any type of industrial or scientific activity[1]
A slightly different Bayesian based approach was reported by Mohebi et al.21. to reconstruct double porosity features of different artificial and natural porous media samples using coarse and high resolution 2D magnetic resonance images
The backbone of our method is based on stochastic reconstructions using rescaled correlation functions
Summary
Assembling spatial information across a wide range of scales is a crucial component in almost any type of industrial or scientific activity[1]. Electron microscopes (SEM) typically have a 250–300 times higher resolution than optical microscopes, while the highest spatial resolution of aerial imagery is up to 2.5 cm compared to 50 cm for satellite images These differences in resolution by no means suggest that lower-resolution methods need to be abandoned; one of their advantages is their much wider field of view or measurement support and, their ability to resolve larger objects than the more recent high-resolution devices. The methodology will be tested using structural images from shale rocks, for which data fusion will be performed using images at three spatial scales representing different spatial information on the distribution of mineral, organic matter and pore phases. Our results are expected to have practical implications in numerous disciplines, e.g., petroleum engineering, geosciences, material sciences, hydrology, soil science, biology, climatology/ecology, and remote sensing
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