Machine Learning Terms

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 Colomap 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.

While a SOM or PCA are examples of batching applications, other features within Paradise are interactive, such as the 2D Colormap and Universal Viewer; therefore, Paradise has both batching and interactive capabilities.

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 process by which computer algorithms learn iteratively from the data and adapt independently to produce reliable, repeatable results. Machine learning addresses two significant issues:

  1. The Big Data problem of trying to interpret dozens, if not hundreds, of volumes of data
  2. The fact that humans cannot understand the relationship of several types of data all at once

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.

  • Machine learning technology that analyzes data at single sample resolution
  • Easy to use, left-to-right guided thought-flows can be applied by all interpreters
  • Interactive, 2D Colormap representing classification results
  • Integrated PCA to SOM (see terms below) thought-flow that identifies the best attributes and refines an interpretation
  • Small producers – attract investment capital through reduced risk and faster interpretation
  • Large producers – reduce risk and the cost/bbl for field development
  • At the intersection of machine learning and multi-attribute seismic analysis
  • The next generation of statistical classification tools
  • An evolving workbench that enables interpreters to extract greater insights from seismic data

Presents SOM process results through classification and probability volumes. Displays 2D and 3D views of the data while using the 2D colormap to gain understanding of the classification results

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.

Any measurable property of seismic data which aids interpreters in identifying geologic features that are not understood clearly in the original data.

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