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Resolving near-seabed velocity anomalies: Deep water offshore eastern India

机译:解决近海床速度异常:印度东部近海深水

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摘要

Imaging in deep-water environments poses a specific set of challenges, both in data preconditioning and velocity model building. These challenges include scattered, complex 3D multiples, aliased noise, and low-velocity shallow anomalies associated with channel fills and gas hydrates. We describe an approach to tackling such problems for data from deep water off the east coast of India, concentrating our attention on iterative velocity model building, and more specifically the resolution of near-surface and other velocity anomalies. In the region under investigation, the velocity field is complicated by narrow buried canyons (500 m wide) filled with low-velocity sediments, which give rise to severe pull-down effects; possible free-gas accumulation below an extensive gas-hydrate cap, causing dimming of the image below (perhaps as a result of absorption); and thin-channel bodies with low-velocity fill. Hybrid gridded tomography using a conjugate gradient solver (with 20-m vertical cell size) was applied to resolve small-scale velocity anomalies (with thicknesses of about 50 m). Manual picking of narrow-channel features was used to define bodies too small for the tomography to resolve. Prestack depth migration, using a velocity model built with a combination of these techniques, could resolve pull-down and other image distortion effects in the final image. The resulting velocity field shows high-resolution detail useful in identifying anomalous geobodies of potential exploration interest.
机译:在数据预处理和速度模型建立方面,深水环境中的成像提出了一系列特定的挑战。这些挑战包括分散的,复杂的3D倍数,混叠噪声以及与通道填充物和天然气水合物相关的低速浅层异常。我们描述了一种解决此类问题的方法,用于解决印度东海岸深水区的数据问题,将注意力集中在迭代速度模型的建立上,尤其是近地表速度和其他速度异常的分辨率。在所调查的区域,速度场因充斥着低速沉积物的狭窄的隐蔽峡谷(宽500 m)而变得复杂,这引起了严重的下拉效应。自由气体会在较大的气体水合物上限以下积累,从而导致下面的图像变暗(可能是吸收的结果);低速填充的细通道体。应用共轭梯度求解器(垂直像元大小为20 m)进行混合网格层析成像,以解决小尺度速度异常(厚度约50 m)。手动拾取窄通道特征用于定义无法通过断层扫描解析的物体。使用结合了这些技术的速度模型建立的叠前深度偏移可以解决最终图像中的下拉和其他图像失真效应。所得的速度场显示出高分辨率的详细信息,可用于识别潜在勘探兴趣的异常地质体。

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