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Titel Solving the problem of imaging resolution: stochastic multi-scale image fusion
VerfasserIn Marina Karsanina, Dirk Mallants, Dina Gilyazetdinova, Kiril Gerke
Konferenz EGU General Assembly 2016
Medientyp Artikel
Sprache Englisch
Digitales Dokument PDF
Erschienen In: GRA - Volume 18 (2016)
Datensatznummer 250121641
Publikation (Nr.) Volltext-Dokument vorhandenEGU/EGU2016-434.pdf
 
Zusammenfassung
Structural features of porous materials define the majority of its physical properties, including water infiltration and redistribution, multi-phase flow (e.g. simultaneous water/air flow, gas exchange between biologically active soil root zone and atmosphere, etc.) and solute transport. To characterize soil and rock microstructure X-ray microtomography is extremely useful. However, as any other imaging technique, this one also has a significant drawback – a trade-off between sample size and resolution. The latter is a significant problem for multi-scale complex structures, especially such as soils and carbonates. Other imaging techniques, for example, SEM/FIB-SEM or X-ray macrotomography can be helpful in obtaining higher resolution or wider field of view. The ultimate goal is to create a single dataset containing information from all scales or to characterize such multi-scale structure. In this contribution we demonstrate 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 representing macro, micro and nanoscale spatial information on porous media structure. 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. Potential practical applications of this method are abundant in soil science, hydrology and petroleum engineering, as well as other geosciences. This work was partially supported by RSF grant 14-17-00658 (X-ray microtomography study of shale rocks) and RFBR grant 15-34-20989 (data fusion). References: 1. Karsanina, M.V., Gerke, K.M., Skvortsova, E.B., Mallants, D. Universal spatial correlation functions for describing and reconstructing soil microstructure. PLoS ONE 10(5): e0126515 (2015). 2. Gerke, K.M., Karsanina, M.V., Mallants, D. Universal stochastic multiscale image fusion: an example application for shale rock. Scientific Reports 5: 15880 (2015). 3. Gerke, K.M., Karsanina, M.V., Vasilyev, R.V., Mallants, D. Improving pattern reconstruction using correlation functions computed in directions. Europhys. Lett. 106(6), 66002 (2014). 4. Gerke, K.M., Karsanina, M.V. Improving stochastic reconstructions by weighting correlation functions in an objective function. Europhys. Lett. 111, 56002 (2015).