Time-lapse seismic data reconstruction using compressive sensing

The time-lapse seismic method plays a critical role in the reservoir monitoring and characterization. However, time-lapse data acquisitions are costly. Sparse acquisitions combined with post-acquisition data reconstruction could reduce the cost and facilitate more frequent applications of the time-lapse seismic monitoring. We present a sparse time-lapse seismic data reconstruction methodology based on compressive sensing. The method works with a hybrid of repeated and non-repeated sample locations. To make use of the additional information from non-repeated locations, we present a view that non-repeated samples in space are equivalent to irregular samples in calendar time. Therefore, we use these irregular samples in time coming from non-repeated samples in space to improve the performance of compressive sensing reconstruction. The tests on synthetic and field datasets indicate that our method can achieve a sufficiently accurate reconstruction by using as few as 10% of the receivers or traces. The method not only works with spatially irregular sampling for dealing with the land accessibility problem and for reducing the number of nodal sensors, but also utilizes the non-repeated measurements to improve the reconstruction accuracy.