图像扭曲
稳健性(进化)
虚假关系
计算机视觉
人工智能
计算机科学
图像(数学)
运动学
偏移量(计算机科学)
反演(地质)
数学
算法
地质学
物理
古生物学
生物化学
化学
经典力学
构造盆地
机器学习
基因
程序设计语言
作者
Francesco Perrone,Paul Sava
出处
期刊:Proceedings
日期:2015-05-26
标识
DOI:10.3997/2214-4609.201413496
摘要
Summary Migration Velocity Analysis in the subsurface-domain measures velocity errors via (extended) imagedomain residuals with respect to an ideal reference image and then updates the velocity model in order to minimise those residuals. Because of the similarity between images with similar extension parameter (shot number, offset, incidence angle, etc.), image-warping presents a robust approach to compute image residuals in different subsurface domains. However, since similarity measures cannot in general distinguish between signal and coherent noise, kinematic artefacts that contaminate the migrated images in certain extended domains can lead to strong and spurious events in the image perturbations and hinder the robustness of the tomographic inversion.
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