Geostatistical Data Integration Model for Contamination Assessment
Soil contamination assessments can be improved with new methods aimed at the accurate estimation of the volume and extension of contaminated soil to be remediated. Geostatistical models that use secondary information to characterize soil contamination are incorporated into a new integration model to provide accurate three-dimensional maps. The proposed integration model is based on a stochastic inversion approach and uses sequential indicator simulation. A two-dimensional reference image representing the areal extension of the contamination is combined with local measurements of contamination in the vertical direction, to render a three-dimensional contamination map. To demonstrate how well the integration model performs, the case study presented focuses on geophysical data and how it can be integrated with soil contamination measurements to improve the characterization of a contaminated site. The results show that the model reproduces successfully the reference image thus providing an accurate three-dimensional contamination map.
KeywordsData integration Contamination assessment Indicator simulation Stochastic inversion
This work was possible due to the financial support of the Portuguese Foundation for Science and Technology (FCT) through the postdoctoral scholarship SFRH/BPD/70315/2010.
- Bortolli L, Alabert F, Haas A, Journel A (1993) Constraining stochastic images to seismic data. In: Soares A (ed) Geostatistics Troia 92, Quantitative geology and geostatistics, vol 1. Kluwer Academic Publishers, Dordrecht Google Scholar
- Deutsch C, Journel A (1992) GSLIB: geostatistical software library and user’s guide. Oxford University Press, New York Google Scholar
- Gómez-Hernández J, Journel A (1993) Joint sequential simulation of multiGaussian fields. In: Soares A (ed) Geostatistics Troia 92, Quantitative geology and geostatistics, vol 1. Kluwer Academic Publishers, Dordrecht Google Scholar
- Goovaerts P (1997) Geostatistics for natural resources evaluation. Oxford University Press, New York Google Scholar
- Haas A, Dubrule O (1994) Geostatistical inversion—a sequential method for stochastic reservoir modelling constrained by seismic data. First Break 12(11):561–569 Google Scholar
- Soares A, Diet JD, Guerreiro L (2007) Stochastic inversion with a global perturbation method. EAGE–Petroleum Geostatistics, Cascais Google Scholar
- Xu W, Tran TT, Srivastava RM, Journel A (1992) Integrating seismic data in reservoir modeling: the collocated cokriging alternative. SPE Paper #24742 Google Scholar