HR: 11:45h
AN: S32A-06    [Abstracts]
TI: Suppressing Near-Array Scattered Noise in Controlled and Passive-Source Seismic Data
AU: * Campman, X
EM: campman@math.tudelft.nl
AF: Delft University, Mekelweg 4, Delft, 2628 CD Netherlands
AU: Rondenay, S
EM: rondenay@mit.edu
AF: Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139-4307 United States
AB: A common problem in controlled and passive-source seismic imaging is the presence of secondary surface waves in the data. We focus here on surface waves that are excited as upcoming body waves hit near-array heterogeneity or nearby surface topography. These surface waves can interfere with events of interest, thereby limiting the maximum obtainable resolution in the final image. We present a method to image and suppress near-array scattered waves which was originally developed for the controlled-source (exploration) seismic case. The method revolves around the following deterministic idea: If one knows the distribution of near-surface scatterers, it is possible to predict and subsequently subtract the secondary surface waves from the data. Hence, we require an estimate of the near-surface distribution of scatterers, which we get from inversion of scattered surface waves excited by one or a few particular P-wave event(s). The prediction-and-subtraction method effectively suppresses scattered noise on data from high resolution 2D exploration seismic lines acquired in areas with strong near-surface heterogeneity and/or surface topography. This leads to increased trace-to-trace coherency and continuity of reflection events. Field data examples will be presented and used to address issues relating to assumptions and limitations inherent to the method. The availability of teleseismic data recorded at dense short-period arrays has opened the door for formulating this algorithm for teleseismic settings as well. In this context, we expect to suppress secondary surface waves in the scattered P-wave field that would otherwise be imaged with the wrong operators, giving rise to artifacts in the final image. We discuss the implications and applicability of the algorithm in a teleseismic framework, focusing in particular on data sampling requirements and preconditioning of the data before the inversion step in order to obtain the near-surface scattering distribution.
DE: 0910 Data processing
DE: 7203 Body wave propagation
DE: 7255 Surface waves and free oscillations
SC: Seismology [S]
MN: 2005 Joint Assembly