Generalized free surface multiple suppression technique based on model optimization and its application to the deep water of the Indian Ocean
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摘要:
印度洋深水海域海底地形整体较为平坦，存在的多次波主要是海底相关多次波，能量强，频带宽，利用常规的广义自由表面多次波预测技术很难去除干净。本文首先利用广义自由表面多次波预测技术预测出多次波模型，然后将原始数据和多次波模型分为低频数据和高频数据，低频数据利用常规的自适应减得到低频多次波模型，高频数据转成曲波域对多次波模型进行优化，最终得到优化后的多次波模型，再利用原始数据直接减多次波模型，达到压制多次波的目的。该技术在印度洋深水海域的应用效果较好，海底相关多次波得到了较好的压制，有效信号得到了凸显，剖面的信噪比明显提高，剖面质量得以提升。
Abstract:The bottom of the deep water area of the Indian Ocean is rather flat, and the main multiples are usually related to the multiples created by strong energy and wide frequency wave band, which are difficult to be removed by the conventional generalized free surface multiples prediction technique. In order to solve the problem, the multi-wave model is improved with the generalized free surface multi-wave prediction technique, and thus the original data are separated into two parts, i.e. low frequency data and high frequency data. The low-frequency data may be converted into curvelet domain to optimize the multi-wave model. Reducing the multi-wave model directly from the original data, multiples are suppressed. The technique has been successfully applied in the deep waters of the Indian Ocean, as the correlation multiples of the seabed are suppressed, the effective signals highlighted, and the signal-to-noise ratio and quality of the profile obviously improved.
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Key words:
- multiwave /
- GSMP technique /
- curvelet transform /
- multiwave model /
- signal to noise ratio
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