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南海海域水深可达5 km,是我国未来油气勘探最有前景的区域.南海海域地震资料含有各种类型多次波,在地震成像前需将多次波去除.然而与传统的一次反射波偏移比较,多次波携带更多的地下结构信息,多次波偏移可提供更好的地下成像.本文提出了一种修改传统逆时偏移的方法,利用多次波进行成像.该方法用包含一次波和多次波的记录代替震源子波,用预测多次波代替一次反射波作为输入数据.在逆时偏移处理流程中,将地表多次波记录沿时间延拓到各个深度层,通过互相关成像条件将包含一次波和多次波的地震记录沿时间延拓到相同深度层.将多次波偏移结果与一次波偏移结果的差应用目标函数进行匹配,弥补一次波偏移的不足.对Sigsbee2B数据做数值测试的结果表明与传统一次反射波偏移相比,该方法可以得到范围更大、均衡性更好的地下照明度.将该方法应用到南海深水实际资料成像,获得了南海深水地震资料多次波成像剖面.偏移结果表明,多次波成像可以弥补反射波照明的不足,由此说明多次波偏移是未来的一个重要研究方向.
The water depth of the South China Sea is up to 5 km, which is the most promising area for future oil and gas exploration in China. The seismic data in the South China Sea contain various types of multiple waves and multiple waves should be removed before seismic imaging. However, Compared with multipath migration, multiple subsurface structures carry more information of subsurface structure and multiple wave migration can provide better subsurface imaging.This paper presents a method to modify the traditional inverse time migration, using multiple wave imaging, The source wavelet is replaced by a record containing primary and multiple waves, and the predicted multiple is used as input instead of the primary reflection In the reverse-time migration process, surface multiple records are time-spans to various depths Layer, the seismic records containing the primary and multiple waves are extended to the same depth level by the cross-correlation imaging conditions, and the difference between the multiple-wave offset results and the primary wave offset results is matched with the target function to make up once The result of numerical test on Sigsbee2B data shows that this method can obtain a larger range and better balanced underground illumination than the traditional one-time reflected wave migration.This method is applied to The South China Sea deepwater real data imaging, get the South China Sea deepwater seismic data multiple imaging profile.The migration results show that the multiple imaging can make up for the lack of reflected wave illumination, indicating that multiple wave migration is an important research direction in the future .