Paradise uses robust, non-linear machine learning technology to classify patterns in multiple attributes simultaneously, thereby extracting more information from the seismic response, even below classic seismic tuning. Use Paradise to:
Test drive Paradise
Machine learning techniques apply algorithms that learn iteratively from the data and adapt independently to produce repeatable results. The goal is to address the big data problem of interpreting massive volumes of data while helping the interpreter better understand the interrelated relationships of different types of attributes contained within 3-D data. The technology classifies attributes by breaking data into what computer scientists call “objects” to accelerate the evaluation of large datasets and allow the interpreter to reach conclusions much faster.
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Machine Learning is changing the way interpretation is done. Find out how these geoscientists are using machine learning to reveal unprecedented levels of detail in seismic data.
New Tools for Interpretation
Select an icon below to learn more about powerful, straightforward workflows in Paradise
Identify attributes that have the greatest contribution to the region according to their relative variance
Probe, analyze, and understand classification results in the Universal Viewer to refine an interpretation
Run different SOM configurations to extract greater information from multiple attributes simultaneously
Display 2D and 3D views of data while using the 2D Colormap to gain understanding of classification results