Filtering random layering effects in imaging

Objects that are buried deep in heterogeneous media produce faint echoes which are difficult to distinguish from the backscattered field. Sensor array imaging in such media cannot work unless we filter out the backscattered echoes and enhance the coherent arrivals that carry information about the objects that we wish to image. We study such filters for imaging in strongly backscattering, finely layered media. The filters are based on a travel time transformation of the array data, the normal move-out, used frequently in connection with differential semblance velocity estimation in seismic imaging. In a previous paper [10] we showed that the filters can be used to remove coherent signals from strong plane reflectors. In this paper we show theoretically and with extensive numerical simulations that these filters, based on the normal move-out, can also remove the incoherent arrivals in the array data that are due to fine random layering in the medium. Key words. array imaging, randomly layered media, filtering

Filtering Random Layering Effects in Imaging

Objects that are buried deep in heterogeneous media produce faint echoes which are difficult to distinguish from the backscattered field. Sensor array imaging in such media cannot work unless we filter out the backscattered echoes and enhance the coherent arrivals that carry information about the objects that we wish to image. We study such filters for imaging in strongly backscattering, finely layered media. The fine layering is unknown and we model it with random processes. The filters use ideas from common seismic imaging techniques, such as normal move-out and semblance velocity estimation. These methods are based on the single scattering approximation, so it is surprising that the filters can annihilate the incoherent echoes produced by random media. The goal of the paper is to study theoretically and numerically this phenomenon