Geophysical Insights hosting the 2018 Oil & Gas Machine Learning Symposium in Houston on September 27, 2018
Introduction to Machine Learning for Multi–Attribute Interpretation and AASPI attributes - A 1-day, DGS Continuing Education course in Denver, CO on September 18th
Dr. Tom Smith presenting on Machine Learning at the 3D Seismic Symposium on March 6th in Denver
What is the "holy grail" of Machine Learning in seismic interpretation? by Dr. Tom Smith, GSH Luncheon 2018
Using Attributes to Interpret the Environment of Deposition - A Video Course. Taught by Kurt Marfurt, Rocky Roden, and ChingWen Chen

Interpretion, Attributes, and Machine Learning
e-Learning Courses

 Seismic attribute e-learning

Seismic Attributes for the Environment of Deposition
a Video Course

Instructors: Dr. Kurt Marfurt, Rocky Roden, Dr. ChingWen Chen
Cost: $99

The evaluation of seismic attributes is a powerful tool in the interpretation of different geologic environments of deposition.  Seismic attributes, specifically geometric and spectral decomposition attributes, provide a framework for interpreting geologic features that define depositional environments.  This video course identifies the appropriate seismic attributes for various geologic settings and describes how these attributes are applied. Lecture and demonstrations cover the use of attributes in interpretation workflows and manipulate attribute parameters to highlight geologic features.  The last video segment of the course describes how sets of attributes are analyzed and classified using multi-attribute, Machine Learning processes to extract more information from the seismic response. 

In this 10-part video series, leading experts demonstrate the applications of attributes.  The $99 registration fee allows access to all course content - both videos and powerpoints, for 7 days.  Click the link to purchase your access. 

Course Learning Objectives: 

  1. Assess the appropriate seismic attributes and associated parameters to improve images for different geologic features.
  2. Systematically incorporate seismic attribute evaluations in a comprehensive interpretation of the environment of deposition.
  3. Utilize Attribute Generator in Paradise software to generate appropriate attribute volumes.
  4. Conduct multi-attribute analysis via different visualization tools and machine learning techniques.


  • Faulting and Folds
  • Seismic stratigraphy

  • Architectural elements of fluvial-deltaic systems

  • Architectural elements of deep water systems

  • Diapirs

  • Carbonates
  • Shale resource drilling (“geo”) hazards

  • Attribute and suboptimum seismic data

  • Examples of Multi-attribute visualization vs. Mulit-attribute SOM