Coda-wave interferometry and time-lapse seismic reservoir monitoring

Coda-wave interferometry employs the sensitivity of multiply scattered waves to detect minute changes of the propagation velocity. In most applications the underlying assumption is that the velocity changes take place approximately uniformly in a large region. Time-lapse seismic reservoir monitoring, on the other hand, aims to infer local time...

Application of Marchenko-based isolation to a land seismic dataset

The Marchenko method is capable of estimating Green’s functions between the surface of the Earth and arbitrary locations in the subsurface. These Green’s functions are used to redatum wavefields to a deeper level in the subsurface. The Marchenko method enables the isolation of the response of a specific layer or package of layers, free from the...

Plane-Wave Marchenko Imaging Method: Field Data Application

Seismic imaging is often used to interpret subsurface formations. However, images obtained by conventional methods are contaminated with internal multiples. The Marchenko method provides the means to obtain multiple-free subsurface images. Due to the high computational cost of the conventional point-source Marchenko imaging method, the less...

Employing internal multiples in time-lapse seismic monitoring, using the Marchenko method

Time-lapse seismic monitoring aims at resolving changes in a producing reservoir from changes in the reflection response. When the changes in the reservoir are very small, the changes in the seismic response can become too small to be reliably detected. In theory, multiple reflections can be used to improve the detectability of traveltime...

Correcting for imperfectly sampled data in the iterative Marchenko scheme

The Marchenko method retrieves the responses to virtual sources in the subsurface, accounting for all orders of multiples. The method is based on two integral representations for focusing and Green’s functions. In discretized form these integrals are represented by finite summations over the acquisition geometry. Consequently, the method...

Data-driven internal multiple elimination applications using imperfectly sampled reflection data

We consider reflection data that have been subsampled by 70% and use Point-Spread-Functions to reconstruct the original data. The subsampled, original and reconstructed reflection data are used to image the medium of interest with the Marchenko method. The image obtained using the subsampled data shows artifacts caused by internal multiples,...

Representations for the Marchenko Method for imperfectly sampled data

The Marchenko method is based on two integral representations for focusing functions and Green’s functions. In practice the integrals are replaced by finite summations. This works well for regularly sampled data, but the quality of the results degrades in case of imperfect sampling. We reformulate the integral representations into summation...