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: Entry and Intermediate levels
Prerequisites (Knowledge/Experience/Education Required): The course is designed to be followed by anyone with a broad geoscience background and basic signal analysis. A familiarity with geophysical terminology as well as basic algebra will be useful.
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 and predictive 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.
- Introduction: Overview of the reflection seismic method
- Causal digital: Digital filtering of signals; filter coefficients, Z-transform, MA and FIR filters
- Frequency spectra: The Fourier transform; Euler's equation; amplitude and phase characteristics of digital filters; minimum-phase spectrum of a digital filter; dual sensor system
- Sampling: Sampling and Nyquist frequency; sampling geophysical data
- Frequency analysis: Frequency spectrum; magnitude and phase spectra; workshop exercises
- Minimum-delay and feedback: Time series, wavelets, and linear systems; minimum, mixed, and maximum delay; feedback stability and minimum delay
- Least squares filtering: Basic concepts, mathematical details, and examples; multiple reflections, dual sensor system
- Predictive Deconvolution: Weiner prediction and filtering; prediction error filter; convolutional model and spiking deconvolution; gap deconvolution, the tail shaping filter and the head shaping filter; examples of predictive deconvolution
- Applications of predictive decon: The general convolutional model, signature deconvolution, predictive deconvolution, pre-whitening, prediction distance, convolutional model in the frequency domain and time variant spectral whitening; workshop exercises
- Inverse Q-filtering
- Spherical spreading and attenuation; absorption
- Vibroseis deconvolution
Enders A. Robinson
Osman M. Hassan