Transmission compensated primary reflection retrieval in the data domain and consequences for imaging

Abstract

We have developed a scheme that retrieves primary reflections in the two-way traveltime domain by filtering the data. The data have their own filter that removes internal multiple reflections, whereas the amplitudes of the retrieved primary reflections are compensated for two-way transmission losses. Application of the filter does not require any model information. It consists of convolutions and correlations of the data with itself. A truncation in the time domain is applied after each convolution or correlation. The retrieved data set can be used as the input to construct a better velocity model than the one that would be obtained by working directly with the original data and to construct an enhanced subsurface image. Two 2D numerical examples indicate the effectiveness of the method. We have studied bandwidth limitations by analyzing the effects of a thin layer. The presence of refracted and scattered waves is a known limitation of the method, and we studied it as well. Our analysis indicates that a thin layer is treated as a more complicated reflector, and internal multiple reflections related to the thin layer are properly removed. We found that the presence of refracted and scattered waves generates artifacts in the retrieved data.

Green's theorem in seismic imaging across the scales

“…Time-reversal acoustics also finds applications in geophysics at various scales, but in those applications the time-reversed field is emitted numerically into a model of the Earth. This is used for source characterization (McMechan, 1982;Gajewski and Tessmer, 2005;Larmat et al, 2010) and for structural imaging by reverse-time migration (McMechan, 1983;Whitmore, 1983;Baysal et al, 1983;Etgen et al, 2009;Zhang and Sun, 2009;Clapp et al, 2010). In these model-driven applications it is much more difficult to account for multiple scattering, which is therefore usually ignored.…”

“…These methods address all orders of internal multiples, but only with approximate amplitudes. Variants of the Marchenko method have been developed that aim to eliminate the internal multiples from the reflection data at the surface (Meles et al, 2015; and Wapenaar, 2016;Zhang et al, 2019). The last reference shows that all orders of multiples are, at least in theory, predicted with the correct amplitudes without needing model information.…”

Green's theorem in seismic imaging across the scales

“…These methods work well for first order internal multiples, but higher order internal multiples are predicted only with approximate amplitudes. Variants of the Marchenko method have been developed that also aim to eliminate the internal multiples from the reflection data at the surface (Meles et al, 2015;van der Neut and Wapenaar, 2016;Zhang et al, 2019). The last reference shows that all orders of multiples are, at least in theory, predicted with the correct amplitudes without needing model information.…”

A field data example of Marchenko multiple elimination

“…In theory, the MME scheme eliminates internal multiple reflections without requiring model information or adaptive subtraction. A small adaptation not only eliminates the internal multiple reflections but also compensates for transmission loss in the primary reflections (Zhang et al, 2019). Free-surface multiple reflections can be included in the removal scheme as well (Zhang and Slob, 2019a).…”