Abstract
Most multiple removal algorithms focus on multiples of primary P-wave
reflections; removal of multiples of converted reflections have not
received comparable attention, so explicit consideration is overdue.
A target-oriented algorithm predicts converted wave multiples by
coupling apparent slownesses, and then subtracts them from elastic
common-source data in a data-adaptive window. Prediction is based
on matching apparent slownesses in common-source and common-receiver
gathers at all source and receiver locations along the propagation
path. Predictions use only offset and traveltime, of the primary
pure and converted waves that produce the multiples, picked from
common-source gathers, and the slownesses calculated from them. Higher-order
multiples can be predicted by repeating this process to match slownesses
at a sequence of alternating source and receiver locations in turn.
Primary reflections (e.g., SS, SP, and PS) that are considered to
be noise, can also be subtracted. The predictions are data-driven
and require no velocities, angles, reflector orientations or free-surface
topography. Any single component (usually vertical) may be used to
identify and pick the traveltimes. The resulting predictions are
also valid for all other components. The subtraction involves flattening
the predicted time trajectory of the multiple, followed by trace
averaging to estimate the local wavelet at each location in a moving
trace and time window that contains the wavelet of the multiple.
The subtraction is data-adaptive, and implicitly involves amplitude
and phase information, so separate or prior estimation of the source
time or directivity functions is not required. Two synthetic examples
showed that the slowness-based algorithm is successful in predicting
and reducing converted wave multiples in an elastic medium. Migrated
P-wave subsurface images are generated before and after multiple
removal to evaluate the performance. Polarity correction of the horizontal
component (either before or after subtraction) ensures coherent stacking.
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