Processing, Inversion and Reconstruction of Seismic Data

by M.D. Sacchi

Duration: Two days

Summary:
This course covers practical aspects of signal theory and inverse problems with application to seismic data processing. 
In particular, the course stresses regularization methods for inverse problems that arise in the inversion of seismic data, noise elimination and reconstruction of seismic surveys.

The course is intended for geophysicists working in data processing, R&D, and for people with interest in understanding current and emerging technologies for seismic data processing. Please contact the instructor for more information.

Course Outline:

1. Review of DSP and deconvolution

  • Fourier transform, properties, symmetries, FT of simple signals
  • Discrete signals, aliasing, DFT, IDFT
  • Linear systems, invariance, convolution, z-transform
  • Dipoles, min and max phase signals, inversion of dipoles
  • Spectral response of simple signals and spectral decomposition

 2. Design of inverse filters

  • Design of least squares inverse filters
  • Wavelet inversion and wavelet shaping
  • Pre-whitening and optimum lag
  • Resolution/noise amplification trade-off
  • White reflectivity assumption and wavelet estimation
  • Blind deconvolution
  • Minimum entropy
  • Maximum kurtosis

3. Reflectivity and impedance inversion

  • Post-stack inversion
  • Retrieval of full band-reflectivity sequences via sparsity tricks
  • Impedance constraints
  • Pres-stack inversion
  • AVO as multi-channel deconvolution problem
  • More advanced pre-stack inversion: Least-squares migration

4.SNR enhancement

  • FX methods
  • Canales FX filters and projection filters
  • Radon based methods
  • Linear, parabolic, hyperbolic, apex-shifted transforms
  • Time-variant and time-invariant operators
  • LS fast solvers and high resolution solvers
  • Local radon transforms
  • Eigen-image based methods

5. Data reconstruction

  • Local versus global methods for data reconstruction
  • Data reconstruction with radon methods
  • FX and FK interpolation
  • Band-limited multi-dimensional Fourier reconstruction
  • Minimum weighted norm Fourier reconstruction
  • Sparseness constraints for Fourier reconstruction
  • Reconstruction methods that use matching pursuit ideas
  • Projection onto convex sets reconstruction
  • Compressive sensing and new paradigms: regular vs. irregular sampling

Background Required: 
Basic knowledge of Linear Algebra and DSP

Instructor Biography:
Mauricio D. Sacchi