https://youtu.be/CNItOzuua8Q

Master Surfer & Voxler: Variograms, Kriging, Geologic Maps, Resource Estimation. In this critical video, "Warning: Your Geochemical Maps May Be WRONG! (SURFER Fix)", we address the often-overlooked pitfalls in creating accurate Geologic Maps and highlight how Surfer Software from Golden Software is essential for robust geochemical Geospatial Modeling and effective Data Visualization for Resource Exploration. Geochemical maps can be unreliable if spatial autocorrelation is ignored, leading to deceptively high predictive power. This video will guide junior and senior geologists, earth science students, structural geologists, and ore prospectors through modern techniques to overcome these issues, focusing on Variogram Analysis and Kriging, which are central tools in Geostatistics. Poor variogram models can lead to kriged predictions that lack validity, despite appearing visually appealing. We emphasize the importance of computing reliable Semivariograms and fitting suitable mathematical models to ensure accurate predictions with minimum variance. Surfer Software provides powerful gridding and contouring capabilities, enabling the creation of publication-quality Contour Maps and 3D Surface Maps. It allows for the interpolation of XYZ data using various methods, customization of gridding parameters, and integration of faults or break lines to improve chemical representation. The video will demonstrate how to perform Fault Digitization from raster layers to vector-based layers, which is crucial for more precise mapping. Additionally, Surfer's new subsurface visualization functionality allows for the modeling of a fourth 'C' variable, such as contaminant or chemical concentration, alongside X, Y, Z values, and viewing these in a 3D View window. For advanced Resource Estimation and understanding subsurface geology, Voxler can be utilized to visualize 3D well data, including drillholes, rock layers, and sample results. This supports decisions in mineral exploration and environmental cleanup by providing powerful Drillhole Maps and 3D subsurface views. The video will also touch upon the Quartile Method for representing potential mineralization probabilities, making complex geochemical data more understandable. Techniques like Inverse Distance Gridding will be explored, especially when faults need to be incorporated into the gridding process. Geostatistics and geospatial modeling are widely applied in Environmental Data science for mapping, monitoring, and management, including assessing environmental pollution. However, implementing Machine Learning (ML) and deep learning algorithms in geospatial tasks faces challenges like imbalanced data, spatial autocorrelation, and prediction errors. Addressing these data issues is crucial for reliable spatial predictions and the ability to represent real-world processes accurately. Our video will also provide insights into Site Characterization and Sampling Design considerations, which are vital for acquiring representative data and reducing uncertainties in environmental investigations. Quality Assurance and quality control are emphasized throughout the sampling process to ensure data reliability, supporting robust Predictive Mapping. The bridge between Academy and Industry! P. Geo. Ricardo A Valls, M. Sc. and Geo Gadfly Valls Geoconsultant ORCID ID- https://orcid.org/0000-0002-5421-0914 Scopus Author ID: 7003369619/35335510700 ResearcherID: S-6604-2018 If you like this content, please "buy me a coffee" https://www.buymeacoffee.com/goldendroplets #VallsGeoconsultant #GeochemicalMapping #SurferSoftware #Geostatistics