This webinar focuses on the practical application of AI and machine learning. We will walk through the exercise of creating an unsupervised SOM (self-organized map) in Paradise. It includes explaining the simple concepts of Principal Component Analysis - in order to go through data reduction and come up with a "recipe" of seismic attributes to be used in SOM, then go through harvest (learning process) to final analysis. After showing results in the 3-D viewer, the focus turns to creating geobodies out of key neurons and to show how to apply volumetric information to obtain estimated reserves. The presentation includes taking bits and pieces of data from projects to show the principles of Machine Learning and the results. This is a working presentation of the software and how to achieve the results shown in the case histories.
This webinar focuses on the practical application of AI and machine learning. We will walk through the exercise of creating an unsupervised SOM (self-organized map) in Paradise. It includes explaining the simple concepts of Principal Component Analysis - in order to go through data reduction and come up with a "recipe" of seismic attributes to be used in SOM, then go through harvest (learning process) to final analysis. After showing results in the 3-D viewer, the focus turns to creating geobodies out of key neurons and to show how to apply volumetric information to obtain estimated reserves. The presentation includes taking bits and pieces of data from projects to show the principles of Machine Learning and the results. This is a working presentation of the software and how to achieve the results shown in the case histories.