Simple Kriging #2. Conditional Gaussian Simulation скачать в хорошем качестве

Simple Kriging #2. Conditional Gaussian Simulation

Lesson 2: Simple Kriging. Conditional Gaussian Simulation 📚 Explore the full course: https://giscourse.online/courses/cond... In Lesson 2 of our Gaussian Conditional Simulation course, we delve into the fundamentals of Simple Kriging, a valuable technique used in geostatistical analysis. Throughout this lesson, we explore essential theory and apply it to a practical exercise using QGIS for a comprehensive geo-statistical analysis of contaminated soils. To begin, we discuss data exploration and the Gaussian anamorphosis technique for data transformation. This method allows us to normalize the dataset and ensure that it adheres to the Gaussian distribution assumptions required for our analysis. Next, we perform a variographic analysis on the transformed data, identifying spatial relationships and trends that aid in modeling the underlying processes. After completing this step, we create a grid and execute Simple Kriging on the generated grid to estimate values at unsampled locations. Following the Kriging process, we reverse the Gaussian anamorphosis transformation, returning the data to its original scale. This allows us to generate a final raster that displays the estimated zinc concentration in parts per million (ppm) at unsampled locations. Lastly, we perform cross-validation to assess the accuracy and reliability of our model. We compare Simple Kriging to Ordinary Kriging, evaluating the strengths and weaknesses of both models in the context of our dataset. By the end of Lesson 2, students will have gained valuable knowledge and hands-on experience in Simple Kriging and its application in geostatistical analysis using QGIS. 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: linkedin.com/in/marcel-cedrez Contact us at: admin@giscourse.online #geographicinformationsystem #QGIS #SpatialAnalysis #mapping #GoogleEarth #historicalimagery