Iterative Least-Squares Migration
Iterative Least-Squares Migration: Standard migration images can suffer from migration artifacts arising from 1) poor source-receiver sampling, 2) weak amplitudes caused by geometric spreading, 3) attenuation, 4) defocusing, 5) poor resolution from a limited source-receiver aperture, and 6) an oscillatory source wavelet. To partially remedy these problems, least-squares migration (LSM), also known as linearized seismic inversion or migration deconvolution, often is proposed to invert seismic data for the reflectivity distribution. If the migration velocity model is suficiently accurate, then LSM can mitigate many of the above problems and lead to a more resolved migration image – sometimes with twice the spatial resolution. However, there are two problems with LSM: the cost can be an order of magnitude more than standard migration and the quality of the LSM image is no better than the standard image for modest velocity errors. I now show how to get the most from least-squares migration by reducing the cost and the sensitivity of LSM to velocity errors.
Figures & Tables
This book describes the theory and practice of inverting seismic data for the subsurface rock properties of the earth. The primary application is for inverting reflection and/or transmission data from engineering or exploration surveys, but the methods described also can be used for earthquake studies. I have written this book with the hope that it will be largely comprehensible to scientists and advanced students in engineering, earth sciences, and physics. It is desirable that the reader has some familiarity with certain aspects of numerical computation, such as finite-difference solutions to partial differential equations, numerical linear algebra, and the basic physics of wave propagation (e.g., Snell’s law and ray tracing). For those not familiar with the terminology and methods of seismic exploration, a brief introduction is provided in the Appendix of Chapter 1. Computational labs are provided for most of the chapters, and some field data labs are given as well. Matlab and Fortran labs at the end of some chapters are used to deepen the reader’s understanding of the concepts and their implementation. Such exercises are introduced early and geophysical applications are presented in every chapter. For the non-geophysicist, geophysical concepts are introduced with intuitive arguments, and their description by rigorous theory is deferred to later chapters.