Open Webinar: Development of geospatial Modflow 6 Disv Voronoi meshes for mining projects with Python

Spatial discretization for a mine related groundwater model has to come from geospatial data that has a system of reference (crs). The latest version of Modflow that is Modflow 6 implements the discretized by vertices option (DISV) that allows the creation of triangular, quadtree, voronoi meshes among other options. We have developed some Python scripts to create voronoi meshes from shapefiles where the user has to define a limit polygon, layers (point, line or polygon) and refinement levels. The code generates meshes with adequate performance and gives geospatial output for the final voronoi mesh as well as the intermediate steps. This webinar will develop the voronoi discretization of three sites: and underground mine project, a tailings storage facility and a complete mine with several components.

Instructor / Instructor:

Saul Montoya M.Sc

Hydrogeologist - Numerical Modeler

Mr. Montoya is a Civil Engineer graduated from the Catholic University in Lima with postgraduate studies in Management and Engineering of Water Resources (WAREM Program) from Stuttgart University – Germany with mention in Groundwater Engineering and Hydroinformatics. Mr Montoya has a strong analytical capacity for the interpretation, conceptualization and modeling of the surface and underground water cycle and their interaction. He is in charge of numerical modeling for contaminant transport and remediation systems of contaminated sites. Inside his hydrological and hydrogeological investigations Mr. Montoya has developed an holistic comprehension of the water cycle, understanding and quantifying the main hydrological dynamic process of precipitation, runoff, evaporation and recharge to the groundwater system.

Language / Lenguaje:

English

Event date / Fecha del evento:

Wednesday, May 25 2022 6:00 p.m. Amsterdam Time

Hosted by / Organizado por:

Hatarilabs

Stream link / Enlace de transmisión:

https://meet.google.com/kye-efwg-hui

Input data / Datos de entrada:

https://owncloud.hatarilabs.com/s/nshOMDluEby194Z

Additional instructions / Instrucciones adicionales:

Password to download the input data: Hatarilabs Please install Anaconda: www.anaconda.com/products/distribution Scripts will be written on a text editor. You can use the one you like, we will work on Atom: atom.io You need to have a geospatial conda environment that you can install with this link: hatarilabs.com/ih-en/how-to-install-python-geospatial-libraries-gdal-fiona-rasterio-etc-under-a-conda-env-in-windows