在过去的十年里,计算费用的降低使深度成象经济而切实可行。因此,最近的焦点就对准了第二道障碍——如何得到精确的速度模型。深度成象并不是一种新技术,但是在过去它的使用一直受到限制。为什么?首先是因为它是计算量特别大的方法,其次是因为这种方法在对传统的时间成象方法改进之前要求有较高精度的界面速度。在介绍用于建模的主要方法之后,本文将讨论理论和方法。 In the past decade, the reduction in computational costs has made depth imaging economic and viable. As a result, the focus has been on the second obstacle lately - how to get accurate velocity models. Deep imaging is not a new technology, but its use has been limited in the past. Why? First, because it is a computationally intensive method, and secondly because this method requires a faster interface speed before the traditional time-based imaging method is improved. After introducing the main methods used for modeling, this article will discuss theories and methods.