Seismic Imaging of Subsurface Geology
(Acquisition, Processing, and Modeling)


by Michael Schoenberger

Intended Audience: Intermediate level

Prerequisites (Knowledge/Experience/Education Required): The course is designed for those who have a qualitative background in the technical side of geophysics (acquisition, processing, and modeling) and want to upgrade or develop a more quantitative understanding of these technologies.

This course provides an overview of the many specialties that comprise seismic exploration technology: acquisition, processing, modeling, and imaging.

Who Should Attend:
Seismic interpreters, geologists, and exploration managers who need to understand seismic exploration technology and technology specialists who need cross training in other specialties.


  • Communicate effectively with specialists in seismic acquisition, processing, and modeling
  • Assess the effects of earth filtering, data acquisition, and processing on seismic sections
  • Appreciate seismic data quality criteria: resolution, signal-to-noise ratio, and image integrity
  • Understand the methodology of seismic survey design and the reasons for common data processing and imaging streams
  • Recognize whether appropriate technology has been applied to your exploration project
  • Be aware of the trade-off between data quality and cost

About the Course:
Proper interpretation of seismic data requires an understanding of the underlying seismic model. Top-notch interpreters understand the manner in which seismic data are affected by earth structure and stratigraphy, as well as by approaches used to acquire, process, and image them. By understanding when a seismic section is a good representation of a geologic cross-section, the interpreter can assess the reliability of his seismic data. Furthermore, by appreciating the value of seismic modeling and inversion, he can utilize those technologies to verify the reasonability of his interpretation and to achieve additional insight into subsurface geology. This course also discusses analysis techniques such as Fourier spectra to enable him to evaluate the potential of his data and to communicate with specialists who use these techniques. The material is presented in sufficient detail to enable the graduate to determine whether an existing data set meets his exploration needs and, if it does not, to work with specialists to reprocess it or to acquire a new data set that does meet his needs. The lectures are complemented by many case-history examples and by a large number of hands-on exercises. 

Instructor Biography:
Michael Schoenberger