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Didkovskyi Oleksandr Ivanov Vladislav Radice Alessio Papini Monica Longoni Laura Menafoglio Alessandra 《Mathematical Geosciences》2022,54(3):467-506
Mathematical Geosciences - The problem of providing data-driven models for sediment transport in a pre-Alpine stream in Italy is addressed. This study is based on a large set of measurements... 相似文献
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Bernardi Mara S. Africa Pasquale C. de Falco Carlo Formaggia Luca Menafoglio Alessandra Vantini Simone 《Mathematical Geosciences》2021,53(8):1781-1812
Mathematical Geosciences - Recent advances in satellite technologies, statistical and mathematical models, and computational resources have paved the way for operational use of satellite data in... 相似文献
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Ognjen?GrujicEmail authorView author&#;s OrcID profile Alessandra?Menafoglio Guang?Yang Jef?Caers 《Stochastic Environmental Research and Risk Assessment (SERRA)》2018,32(7):1955-1971
In this paper we propose Universal trace co-kriging, a novel methodology for interpolation of multivariate Hilbert space valued functional data. Such data commonly arises in multi-fidelity numerical modeling of the subsurface and it is a part of many modern uncertainty quantification studies. Besides theoretical developments we also present methodological evaluation and comparisons with the recently published projection based approach by Bohorquez et al. (Stoch Environ Res Risk Assess 31(1):53–70, 2016. https://doi.org/10.1007/s00477-016-1266-y). Our evaluations and analyses were performed on synthetic (oil reservoir) and real field (uranium contamination) subsurface uncertainty quantification case studies. Monte Carlo analyses were conducted to draw important conclusions and to provide practical guidelines for all future practitioners. 相似文献
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Hron Karel Menafoglio Alessandra Palarea-Albaladejo Javier Filzmoser Peter Talská Renáta Egozcue Juan José 《Mathematical Geosciences》2022,54(1):71-93
Mathematical Geosciences - It often occurs in practice that it is sensible to give different weights to the variables involved in a multivariate data analysis—and the same holds for... 相似文献
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A kriging approach based on Aitchison geometry for the characterization of particle-size curves in heterogeneous aquifers 总被引：2，自引：0，他引：2
Alessandra Menafoglio Alberto Guadagnini Piercesare Secchi 《Stochastic Environmental Research and Risk Assessment (SERRA)》2014,28(7):1835-1851
We consider the problem of predicting the spatial field of particle-size curves (PSCs) from a sample observed at a finite set of locations within an alluvial aquifer near the city of Tübingen, Germany. We interpret PSCs as cumulative distribution functions and their derivatives as probability density functions. We thus (a) embed the available data into an infinite-dimensional Hilbert Space of compositional functions endowed with the Aitchison geometry and (b) develop new geostatistical methods for the analysis of spatially dependent functional compositional data. This approach enables one to provide predictions at unsampled locations for these types of data, which are commonly available in hydrogeological applications, together with a quantification of the associated uncertainty. The proposed functional compositional kriging (FCK) predictor is tested on a one-dimensional application relying on a set of 60 PSCs collected along a 5-m deep borehole at the test site. The quality of FCK predictions of PSCs is evaluated through leave-one-out cross-validation on the available data, smoothed by means of Bernstein Polynomials. A comparison of estimates of hydraulic conductivity obtained via our FCK approach against those rendered by classical kriging of effective particle diameters (i.e., quantiles of the PSCs) is provided. Unlike traditional approaches, our method fully exploits the functional form of PSCs and enables one to project the complete information content embedded in the PSC to unsampled locations in the system. 相似文献
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