Open Webinar: Machine Learning for Geological Modeling with Python and Scikit Learn

This webinar covers an applied case of geological unit modeling done on the Queens Mary Reservoir, London, UK based on 266 drillings. The geological model was done in Python with the machine learning library Scikit Learn to create a geological model based on lithology from drillings. The code generates a point cloud of drilling lithologies that are transformed and scaled for a neural network classifier like the Multi-layer Perceptron classifier implemented on the Scikit Learn library as sklearn.neural_network.MLPClassifier. To validate the results of the geological model, an analysis of the confusion matrix from the neural network is performed. The webinar also includes a georeferenced 3D visualization and comparison from well lithology and interpolated geology as Vtk format in Paraview.

Intructor / 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:

Monday, November 29, 2021 at 6:00 p.m. Amsterdam Time

Hosted by / Organizado por:

Hatarilabs

Stream link / Enlace de transmisión:

https://meet.google.com/vvr-vvnr-huo

Input data / Datos de entrada:

https://owncloud.hatarilabs.com/s/pxNhhVQiT9dF0JD

Additional instructions / Instrucciones adicionales:

Password to download input data: Hatarilabs You need Anaconda installed on your computer. Please download it from this link: https://www.anaconda.com/products/individual For visualization and comparison you need to have Paraview: https://www.paraview.org/download/