A graphical representation of neuron classifications of attributes. Each hexagonal object (neuron) in the interactive 2D Colormap represents a class of data in the region and a corresponding geologic condition. The 2D Colormap is used interactively with the Paradise Universal Viewer to select and isolate specific neurons which have a classified set of seismic attributes according to where the data is concentrated.
A tool in Paradise that quantifies the relative contribution of attributes within a neuron or set of neurons. The 2D Colormap reveals the specific attributes comprising a selected classification result as represented by each neuron on the 2D Colormap. The interpreter can then run a SOM across the refined area to expose geobodies with similar properties. Note: This feature will be available in Paradise 3.0.
A beneficial technique when single attributes are indistinct. These natural patterns or clusters represent geologic information embedded in the data, and can help identify geologic features, geobodies, and aspects of geology that often cannot be interpreted by any other means.
A class of machine learning. While there are several forms of applying neural networks, Paradise uses the Self-Organizing Map (SOM) process, which is sometimes referred to as Kohonen maps after Professor Teuva Kohonen at the University of Finland.
Attributes differ in their relative contribution to information in a given volume. PCA is a linear process that helps to determine those attributes that have the greatest contribution to the data and quantifying the relative contribution of each attribute based on its variance.
The results of the PCA are given in 2 bar charts – Eigenvalues and Eigenvectors. Together, they indicate the direction and magnitude of the greatest variance among the set of attributes.
Eigenvalues – Graphically presents the extent of variance among a set of attributes in the PCA and can be selected to reveal its corresponding set of Eigenvectors
Eigenvector – The graphical bar chart, and associated table which lists the relative contribution on a percentage basis of each attribute in a set.
A neural network based, machine learning process that is applied to multiple attribute volumes simultaneously. A SOM analysis enables interpreters to identify the natural organizational patterns in the data from multiple seismic attributes. Applied at single sample seismic resolution in Paradise, the SOM produces a non-linear classification of the data in a region designated by the interpreter. Regions can be constrained by time, between horizons, or above and below a given horizon. SOM evaluations have proven to be beneficial in essentially all geologic settings, including unconventional resource plays, moderately compacted onshore regions, and offshore unconsolidated sediments.