A random layer-stripping method for seismic reflectivity inversion

Reflection coefficients and arrival times, together with seismic velocities, are significantly important for possible evaluation of reservoir properties in exploration seismology. Reflectivity inversion is one of the robust inverse techniques used to estimate layer properties by minimising misfit error between seismic data and model. On the other hand, the layer-stripping method produces subsurface images via a top-down procedure so that a given layer is modelled after all the upper layers have been inverted. In this paper, we have combined these two methods to develop a new random layer-stripping scheme which first determines the reflectivity series via a random-search algorithm and then estimates P-wave velocities. The first step can be viewed as a variant of sparse spiking deconvolution, and the second step is accomplished by considering empirical relations between density and P-wave velocity. The method has been successfully applied to Marmousi synthetic data to examine dipping reflectors and velocity gradients, and it has been found to be quite reliable for analysing complex structures. A comparison with minimum entropy deconvolution showed that our inversion algorithm gives better results in detecting the amplitudes and arrival times of seismic reflection events.