Digital Signal Analysis in Seismic Data Processing

by Enders A. Robinson and Osman M. Hassan

*Please note: This course is not available for in-house training.

Duration: Two days

Intended Audience: Intermediate level

Prerequisites (Knowledge/Experience/Education Required): The course is designed to be followed by students with a basic knowledge of seismic exploration and interpretation. Two semesters of a university-level mathematics course are required, with the understanding that substitutions can be made.


This course is designed at an introductory level to cover in some details the theoretical background and the practical applications of digital signal analysis in seismic data processing. The course describes the basic definitions of digital filters, their coefficients, and their manipulations in time and in frequency domains. The sampling theory and the wavelet concept are introduced. The design and the applications of least-squares filters are covered. Applications of different types of deconvolution filters such as spiking filters and shaping filters are discussed along with examples and exercises.

The intended audiences for this course are seismic data analysts, field and office processors, and seismic interpreters who are seeking to understand some basic and important concepts of digital signal analysis in seismic data processing.

Course Outline:

  1. Overview of the seismic reflection method
  2. Sampling, Nyquist frequency, digital signals, Z-transform
  3. Wavelets, reflectivity, thin beds, wavelet processing
  4. Frequency, Fourier transform, amplitude and phase characteristics, minimum-phase
  5. Filtering, feed-forward, feedback, FIR filters, IIR filters
  6. Convolutional model, reverberations, multiple reflections
  7. Deconvolution, linear prediction, spiking deconvolution, shaping deconvolution, signature deconvolution

Learner Outcomes:

  1. Will be able to understand the objective of seismic data acquisition (land and marine)
  2. Clearly understand the different source wavelets (signatures) and their manipulation
  3. Easily interpret signals in time and in frequency domain
  4. Calculate with confidence the convolution, correlation, and auto correlation of a signal
  5. Select and understand the deconvolution process and the optimum parameters for application
  6. Design and select optimum filters parameters

Instructor Biographies:
Enders A. Robinson
Osman M. Hassan