基于梯度的时域声波和弹性全波形反演法重建Marmousi-2地质模型

Daniela Teodor
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摘要

Marmousi-2是一种弹性模型,由纵波、波速(分别为Vp和Vs)和地质体密度定义,再现了安哥拉Quanza盆地北昆圭兰的地质剖面。本研究的重点是使用基于梯度的声学和弹性全波形反演(FWI)来评估Vp和Vs估计模型及其Vp / Vs比的准确性和质量。测试了两种不同的初始模型:第一种是用遗传算法(GA)得到的,第二种是用平滑函数对Marmousi-2真模型进行平滑得到的。这些初始模型的特点是在深度和水平方向上具有可变的Vp/Vs比率,可以很好地接近真实的地质环境。对FWI获得的不同层析图像的分析和模型失配计算表明,只有当初始模型的Vp/Vs比能够再现真实模型的深度和水平变化时,Vp和Vs模型才能得到较好的重建;这是因为Vp/Vs比值的背景趋势从初始模型到重建模型都被保留了下来,并且没有被FWI修改太多。此外,值得注意的是,使用遗传算法获得的初始模型也允许良好的Vp和Vs模型重建。然而,在这种情况下,最终的重建模型与真实模型的匹配程度较低。然而,基于梯度的FWI带来的改进,即减少了真实模型与重建模型之间的模型不拟合,在遗传情况下比在平滑情况下更大。然后,从一个不太精确的初始模型开始,FWI能够提供接近真实模型的最终模型。
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The reconstruction of the Marmousi-2 geological model using the gradient-based time domain acoustic and elastic Full-Waveform Inversion
Marmousi-2 is an elastic model defined by the P-wave, the Swave velocities (Vp and Vs respectively) and by the densities of the geological bodies that reproduce a geological profile of North Quenguelain the Quanza Basin in Angola. This study is focused on the evaluation of the accuracy and quality of the Vp and Vs estimated models and of their Vp to Vs ratio, using the gradient-based acoustic and elastic Full-Waveform Inversion (FWI). Two different initial models have been tested: the first one obtained with Genetic Algorithms (GA) and the second one obtained by applying a smoothing function to the Marmousi-2 true model. These initial models are characterized by variable Vp/Vs ratios, both in depth and in the horizontal direction, which nicely approximate the true geological setting. The analysis of the different tomographic images obtained by the FWI and the model misfit computation proved the good reconstruction of the Vp and Vs model only when the initial model has Vp/Vs ratios that reproduce the depth and the horizontal variations of the true model; this happens because the background trend of Vp/Vs ratio is preserved from the initial to the reconstructed models and is not much modified by FWI.Furthermore, it was noticed that also the use of an initial model obtained with Genetic Algorithms allows for a good Vp and Vs model reconstruction. However, the final reconstructed model has in this case a lower degree of matching with the true model. Nevertheless, the improvements brought by gradient-based FWI, that is, the decrease of the model misfit between the true model and the reconstructed model, are greater for the GA case compared to the smooth case. Then, starting from a less accurate initial model, the FWI is able to deliver a final model close to the true model.
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