Abstract
Simulation of continuous variables conditioned to meander structures is an important tool in the context of soil contamination assessment, namely, when the contamination is related with depositional sediments in water channels. Hence, this paper proposes using bi-point statistics stochastic simulation with local anisotropy trends to simulate continuous variables inside predefined channels. To accomplish this objective, the Direct Sequential Simulation (DSS) algorithm was modified to account for local anisotropy when searching for the simulation node. This methodological approach was applied to the spatial characterization of polluted sediments in a coastal lagoon located in the North of Portugal (Barrinha de Esmoriz).
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References
Caetano H, Pereira MJ, Guimarães C (2004) Use of factorial kriging to incorporate meteorological information in estimation of air pollutants. In: Sanchez-Vila X, Carrera J, Gómez-Hernández J (eds) geoENV IV – Geostatistics for environmental applications. Kluwer, The Netherlands, pp 55–66
DHVFBO (2001) Technical Report “Soil and Groundwater Contamination Assessment in Barrinha de Esmoriz”
Franco C, Soares A, Delgado J (2006) Geostatistical modelling of heavy metal contamination in the topsoil of Guadiamar river margins (S Spain) using a stochastic simulation technique. Geoderma 136(3–4):852–864
Horta A, Carvalho J, Soares A (2008) Assessing the quality of the soil by stochastic simulation. In: Soares A, Pereira MJ, Dimitrakopoulos R (eds) geoENV VI – geostatistics for environmental applications. Springer, Berlin, pp 385–396
Luis JJ, Almeida JA (1997) Stochastic characterisation of fluvial sand channels. In: Baafi EY, Schofield NA (eds) Geostatistics Wollongong 96, vol. 1. Kluwer, The Netherlands, pp 477–488
Russo A, Trigo RM, Soares A (2008) Stochastic modelling applied to air quality space-time characterization. In: Soares A, Pereira MJ, Dimitrakopoulos R (eds) geoENV VI – geostatistics for environmental applications. Springer, Berlin, pp 83–93
Soares A (1990) Geostatistical estimation of orebody geometry: morphology kriging. Math Geol 22(7):787–802
Soares A (2001) Direct sequential simulation. Math Geol 33(8):911–926
Soares A, Pereira MJ (2007) Space–time modelling of air quality for environmental-risk maps: a case study in south Portugal. Comput Geosci 33(10):1327–1336
Strebelle S (2002) Conditional simulation of complex geological structures using multi-point statistics. Math Geol 34(1):1–21
Strebelle S (2007) Simulation of petrophysical property trends within facies geobodies. In: EAGE (ed) Petroleum Geostatistics 2007
Stroet C, Snepvangers J (2005) Mapping curvilinear structures with local anisotropy kriging. Math Geol 37(6):635–649
Xu W (1997) Conditional curvilinear stochastic simulation using pixel-based algorithms. In: Baafi EY, Schofield NA (eds) Geostatistics Wollongong 96, vol. 1. Kluwer, The Netherlands, pp 454–464
Yao T, Calvert C, Jones T, Foreman L, Bishop G (2007) Conditioning geologic models to local continuity azimuth in spectral simulation. Math Geol 39:349–354
Acknowledgements
This paper was produced in the context of Project “Soil contamination risk assessment” (PTDC/CTE-SPA/69127/2006) (financed by FEDER through the national Operational Science an Innovation Program 2010 with the support of the Foundation for Science and Technology).
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Horta, A., Caeiro, M.H., Nunes, R., Soares, A. (2010). Simulation of Continuous Variables at Meander Structures: Application to Contaminated Sediments of a Lagoon. In: Atkinson, P., Lloyd, C. (eds) geoENV VII – Geostatistics for Environmental Applications. Quantitative Geology and Geostatistics, vol 16. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-2322-3_15
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DOI: https://doi.org/10.1007/978-90-481-2322-3_15
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