Estimating the Background Velocity Model with the Normalized Integration Method
Estimating the Background Velocity Model with the Normalized Integration Method
复制标题
DOI：
10.3997/2214-4609.20130411
复制
发表时间：
2013-06
期刊：
影响因子：
--
通讯作者：
D. Donno;H. Chauris;H. Calandra
中科院分区：
文献类型：
--
作者：
D. Donno;H. Chauris;H. Calandra
全波形反演（FWI）是一种数据拟合方法，可以从观测到的叠前地震数据中反演地球属性。当输入地震数据包含低频分量或初始模型非常接近实际模型时，FWI产生接近完美的结果。然而，由于FWI目标函数是高度非线性的，并且具有许多局部极小值，记录的低频的缺乏可能会严重损害最终的FWI反演结果。在这里，我们建议使用归一化积分法（NIM）的背景速度模型的确定，后来与FWI细化。因为我们只比较随时间增加的函数，NIM目标函数具有更凸的形状，因此即使在数据的低频分量缺失时，也更容易收敛到精确的模型。一个简单的二维合成模型的数值试验验证了这种新方法是有效的恢复速度模型的长波长。
Full Waveform Inversion (FWI) is a data-fitting method that allows retrieving the Earth properties from the observed pre-stack seismic data. FWI produces nearly perfect results when the input seismic data contains low-frequency components or when the initial model is very close to the actual model. However, as the FWI objective function is highly nonlinear and has many local minima, lack of recorded low frequencies might seriously harm the final FWI inversion result. We propose here to use the Normalized Integration method (NIM) for the determination of the background velocity model, later refined with FWI. Because we only compare functions increasing with time, the NIM objective function has a more convex shape, thus allowing more easily the convergence towards the exact model even when the low-frequency components of the data are missing. Numerical tests with a simple 2D synthetic model verify that this new method is efficient at recovering the long wavelengths of the velocity model.