Abstract

The estimation of a reliable velocity–depth model from towed streamer marine seismic data recorded in deep water, especially with a complex seafloor environment, is challenging. The determination of interval velocities from the normal move-out (NMO) of the reflected seismic signals for shallow reflectors (<1 km below the seafloor) is compromised by the combination of a long wave path in the water column and the complex ray paths due to topography, leading to small move-out differences between reflectors. Furthermore, low sediment velocities and deep water produce refraction arrivals only at limited far offsets that contain information about deeper structures. Here, we present an innovative method where towed streamer seismic data are downward continued to the seafloor leading to the collapse of the seafloor reflection and the emergence of refraction events as first arrivals close to zero offset, which are used to determine a high-resolution near surface velocity–depth model using an efficient tomographic method. These velocities are then used to perform pre-stack depth migration. We found that the velocity–depth model derived from tomography of downward continued towed streamer data provides a far superior pre-stack depth migrated image than those produced from velocity–depth models derived from conventional velocity estimation techniques.

Research Highlights

• A comparison in velocity from conventional NMO, sparsely spaced OBS tomography, and tomography of downward continue streamer data is carried out.

• Accuracy in velocities are proved from the prestack depth migration results.

中文翻译：

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使用向下连续长偏移多通道地震（MCS）数据在复杂深水环境中进行可靠的速度估算

摘要

从深水中记录的拖缆海洋地震数据（尤其是在复杂的海底环境中）估算可靠的速度-深度模型具有挑战性。水柱中的长波路径和复杂的射线路径的组合影响了根据浅层反射器（低于海床以下1 km）的反射地震信号的法向运动（NMO）确定间隔速度的能力。到地形，导致反射器之间的微小移动差异。此外，低泥沙速度和深水仅在有限的远偏移处产生折射到达，其中包含有关深层结构的信息。这里，我们提出了一种创新的方法，其中拖缆地震数据向下连续向下延伸到海底，导致海底反射的崩溃和折射事件的出现，因为初次到达接近零偏移，这可用于确定高分辨率近地表速度–有效的层析成像方法的深度模型。然后将这些速度用于执行叠前深度偏移。我们发现，从向下连续拖曳拖缆数据的层析成像得出的速度-深度模型提供的叠前深度偏移图像要比从常规速度估算技术得出的速度-深度模型产生的图像好得多。然后将这些速度用于执行叠前深度偏移。我们发现，从向下连续拖曳拖缆数据的层析成像得出的速度-深度模型提供的叠前深度偏移图像要比从常规速度估算技术得出的速度-深度模型产生的图像好得多。然后将这些速度用于执行叠前深度偏移。我们发现，从向下连续拖曳拖缆数据的层析成像得出的速度-深度模型提供的叠前深度偏移图像要比从常规速度估算技术得出的速度-深度模型产生的图像好得多。

研究重点

• 进行了常规NMO，稀疏间隔OBS断层扫描和向下连续拖缆数据的断层扫描的速度比较。

• 从叠前深度偏移结果证明了速度的准确性。