Walkthrough of an interactive workflow in Python Jupyter Notebook with GeostatsPy, matplotlib and pywidgets for directional variogram calculation and modeling.
Thaank you soo much for this lecture! And the other ones too, they're helping me a lot in my plants to complement the content of an article that I'm writing! Thank you again!
Superb demonstration about variogram modeling. It really helps a lot to understand about structure contribution and major/minor axis. In other words, you would recommend Gaussian modeling to deal with strutural, isopach, isochore data, for example, and spherical modeling when dealing with petrophysics-related information.
Great stuff and super helpful jupyter notebook! I have a question on the use of nested variograms to account for zonal anisotropy. In this case, you use nesting to model the 45 azi with an "apparent sill" of 0.6. I can see that nested structures could be more theoretically correct (so that all sills add to 1), but it adds some complexity to the modeling (e.g., enough to make a workflow like this difficult to explain to my colleagues who do not deal as much with geostats). Assuming our plan was to run a sequential gaussian simulation within the extents of this site: Would a single structure variogram model in the 45 azi direction with a sill of ~0.6 be a valid alternative? Would we then be okay to pair that with a 135 azi model with a sill of 1? I'm not sure if this would create any undesired effects relative to the nesting done here, or if it would give practically the same simulation results
Thank you very much for these excellent lectures. Love what you do!
Thaank you soo much for this lecture! And the other ones too, they're helping me a lot in my plants to complement the content of an article that I'm writing! Thank you again!
Superb demonstration about variogram modeling. It really helps a lot to understand about structure contribution and major/minor axis.
In other words, you would recommend Gaussian modeling to deal with strutural, isopach, isochore data, for example, and spherical modeling when dealing with petrophysics-related information.
Excellent!!. Thanks for share.
Great stuff and super helpful jupyter notebook! I have a question on the use of nested variograms to account for zonal anisotropy. In this case, you use nesting to model the 45 azi with an "apparent sill" of 0.6. I can see that nested structures could be more theoretically correct (so that all sills add to 1), but it adds some complexity to the modeling (e.g., enough to make a workflow like this difficult to explain to my colleagues who do not deal as much with geostats). Assuming our plan was to run a sequential gaussian simulation within the extents of this site: Would a single structure variogram model in the 45 azi direction with a sill of ~0.6 be a valid alternative? Would we then be okay to pair that with a 135 azi model with a sill of 1? I'm not sure if this would create any undesired effects relative to the nesting done here, or if it would give practically the same simulation results
Hi teacher, i have a question for you. In the end, which direction do we choose for kriging modeling?
How do we determine whether to use a nested structure or not? Also, great videos!