Adaptive Least-Squares Reverse Time Migration (LSRTM)

  • LSRTM 

    LSRTM is an inversion-based imaging technique which refines conventional depth migration images toward true reflectivity. It overcomes the shortcomings of conventional migration algorithms via iterative least-squares inversion in order to gradually correct migration errors. 

    Compared to conventional depth migration algorithms, LSRTM generates superior seismic images with the following advantages:

    • Amplitudes are better balanced by revealing weak signals and pushing the image towards the true reflectivity mode
    • Migration artifacts caused by acquisition footprint or non-uniform illumination are reduced
    • The spatial resolution is increased by enriching the low frequencies and suppressing the image side lobe
    • The signal-to-noise ratio is enhanced by improving the images of steep dips or other complex structures.

    LSRTM can be implemented with one or two objectives in mind: one would be to produce a broadband image with higher resolution, and the second would be to improve poorly illuminated areas. The first objective is usually focused on areas of interest in shallower zones , and the second objective would be focused on deeper zones, such as subsalt. In practice, the workflows to achieve these two objectives are different.  

    Freedom RTM


    Freedom LSRTM


    In the broadband implementation, LSRTM iteratively integrates one-way wave equation migration (WEM) and two-way RTM methods to produce high resolution depth images with enriched low frequencies as well as high frequencies. The final output image contains not only high-frequency and high-resolution horizons but also clear complex structures with sharp geologic boundaries. The computation cost of the broadband LSRTM is diminished when compared to the cost of directly calculating high-frequency RTM images, and therefore more affordable. The image quality is significantly improved compared to that of either conventional RTM or high-frequency WEM. 

    For the application focusing on subsalt enhancements, we employ an adaptive strategy to speed up the convergence and address the practical issues such as imperfect migration velocity. It helps improve the image coherency of subsalt horizons by enhancing the weak signals, and clarify event termination of sediments toward salt boundaries by reducing the halo artifacts.