Summary

Despite the development of excellent techniques for predicting multiples such as convolution based or wavefield extrapolation based approaches, subtracting multiples in the data using the predicted multiples still remains as a challenge. The difficulty stems from the fact that many existing techniques try to match the predicted multiples to the multiples in the data by either adaptation or pattern matching. Because the prediction techniques mentioned above change the waveform of the predicted multiples, it will be very difficult to perfectly match the waveform of the predicted multiples to that of the multiples in the data. We report a new technique for subtracting the multiples using the attributes of the predicted multiples to determine the multiples in the data without any matching process. We illustrate the technique using a synthetic data set and show 3-D field data examples.

Introduction

A substantial progress has been made for predicting surface-related multiples in the marine seismic data. There are two approaches for predicting multiples: convolution based prediction (e.g., Verschuur et. al., 1992) and wavefield extrapolation based prediction (Pica et. al, 2006). Comparison of these two approaches was well summarized by Matsen and Xia (2007). Both prediction methods mentioned above, although they can accurately predict the timing of the multiples, alter the waveform of the multiples. Convolution based approaches change the waveform by doubling the source wavelet spectrum in the frequency domain. In addition, interpolated traces to generate missing source-receiver pairs at the bounce points under the water surface may not have the same waveform of the missing traces. Wavefield extrapolation based approaches also change the waveforms unless one uses a perfect reflectivity model, which is impractical to obtain. Because of these waveform changes, subtracting multiples in the data using the predicted multiples is still a challenging task.

One common approach for subtracting the multiples using the predicted multiples is adaptive subtraction (Verschuur et. al., 1992). Adaptive subtraction tries to match the waveform of the predicted multiples to that in the data in both amplitude and phase in a window. If the window is small enough to include only multiples, the window may not be able to provide enough statistics to design a reliable filter. On the other hand, if the window is large, it may contain primaries and other noise to limit the adaptation process. Another approach is based on pattern matching (Spitz, 1999). One designs a prediction error filter (PEF) for the primary by deconvolving the PEF of the data with that of the predicted multiples. Comparison of adaptive subtraction versus pattern matching is well documented by Abma et. al. (2005). They reported that a pattern matching technique tends to leave much residual multiple energy and to weaken the primaries where the predicted multiples overlapped the primaries.

We report a new approach using the attributes of the predicted multiples; namely, dip and average absolute value (AAV) along the dip. Instead of subtracting adapted or matched multiples, we subtract multiples directly estimated from the data using the dip and AAV of the predicted multiples.

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