Remediation Cost for Contaminated sites. #6 Conditional Gaussian Simulation. скачать в хорошем качестве

Remediation Cost for Contaminated sites. #6 Conditional Gaussian Simulation.

To learn more about Geo RGB, visit us at: https://giscourse.online/ Visit us in the Facebook page at:   / georgbcommunity   Follow us in LinkedIn:   / geor.  . Follow me in LinKedIn: www.linkedin.com/in/marcel-cedrez Contact us at: admin@giscourse.online Remediation Cost for Contaminated sites. #6 Conditional Gaussian Simulation. In this comprehensive lesson, we explore the nuances of Conditional Gaussian Simulation as a tool for assessing the remediation costs of soils contaminated with zinc. Starting with a dataset from the Meuse region, we visually analyze the distribution of zinc concentrations using histograms and summary statistics. To ensure normality, we apply Gaussian anamorphosis, transforming the original data into a Gaussian space. Next, we delve into the field of spatial statistics with variography analysis, which allows us to understand the spatial structure and correlation of our data. Using the GSTAT package, we perform both simple kriging interpolation and conditional simulations. These techniques generate probable spatial scenarios of zinc concentrations across the region. Having obtained our simulated realizations, the next crucial step is back-transformation. This process converts the simulated values in Gaussian space back to their original scale, providing realistic estimates of zinc concentrations. Once our simulations are in a comprehensible form, we segment them into distinct contamination levels: low, medium, high, and very high. This classification gives us a clearer picture of potential areas requiring attention and remediation. The lesson culminates with a thorough statistical summary of each contamination level across all simulations. We calculate key metrics such as mean, median, and standard deviation, offering a holistic view of the contamination landscape. By the end of this lesson, learners will have acquired a robust understanding of Conditional Gaussian Simulation and its applications in environmental risk assessment.