| 基于模型优化的广义自由表面多次波压制技术在印度洋深水海域的应用 |
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| 引用本文： | 王小杰, 颜中辉, 刘俊, 刘欣欣, 杨佳佳. 基于模型优化的广义自由表面多次波压制技术在印度洋深水海域的应用[J]. 海洋地质与第四纪地质, 2021, 41(5): 221-230. doi: 10.16562/j.cnki.0256-1492.2020101202 |
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| 作者姓名： | 王小杰 颜中辉 刘俊 刘欣欣 杨佳佳 |
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| 作者单位： | 1.中国地质调查局青岛海洋地质研究所，青岛 266237;; 2.青岛海洋科学与技术试点国家实验室海洋矿产资源评价与探测技术功能实验室，青岛 266237 |
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| 基金项目： | 国家自然科学基金;基于双相介质理论的天然气水合物地震反射特征及量化方法研究;国家重点研发计划;实验室开放基金;深海科学钻探井位选址项目 |
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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。
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| 关 键 词： | 多次波 GSMP技术 曲波变换 多次波模型 信噪比 |
| 收稿时间： | 2020-10-12 |
| 修稿时间： | 2020-12-07 |
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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| WANG Xiaojie, YAN Zhonghui, LIU Jun, LIU Xinxin, YANG Jiajia. Generalized free surface multiple suppression technique based on model optimization and its application to the deep water of the Indian Ocean[J]. Marine Geology & Quaternary Geology, 2021, 41(5): 221-230. doi: 10.16562/j.cnki.0256-1492.2020101202 |
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| Authors: | WANG Xiaojie YAN Zhonghui LIU Jun LIU Xinxin YANG Jiajia |
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| Affiliation: | 1.Qingdao Institute of Marine Geology, China Geological Survey,Qingdao 266237, China;; 2.Laboratory for Marine Mineral Resources, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266237, China |
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| 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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| Keywords: | multiwave GSMP technique curvelet transform multiwave model signal to noise ratio |
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