Elastic least-squares reverse time migration

Least-squares migration (LSM) can produce images with improved resolution and reduced migration artifacts. We propose a method for elastic least-squares reverse time migration (LSRTM) based on different types of imaging condition. Perturbation imaging condition leads to images for squared P and S velocity models; the displacement imaging condition crosscorrelates components of the source and receiver displacement wavefields; the potential and scalar imaging conditions lead to images of various combination of Pand S-wave modes. Using each imaging condition, we form an LSM algorithm by defining the migration and demigration operators. Among the combined images, the perturbation and scalar images do not suffer from polarity changes, and thus they can be stacked over experiments without an additional polarity correction. The scalar imaging condition requires geologic dip information, while the perturbation imaging condition does not need additional information. Therefore, we apply LSRTM using the perturbation imaging condition to 2D examples. Results show that elastic LSRTM iteratively increases the image resolution and attenuates artifacts. Also, the computed LSRTM images have correct relative-amplitudes, which are suitable for reservoir characterization.