...
首页> 外文期刊>Mathematical geosciences >Anisotropic Mean Traveltime Curves: A Method to Estimate Anisotropic Parameters from 2D Transmission Tomographic Data
【24h】

Anisotropic Mean Traveltime Curves: A Method to Estimate Anisotropic Parameters from 2D Transmission Tomographic Data

机译:各向异性平均行程时间曲线:一种根据二维透射层析成像数据估计各向异性参数的方法

获取原文
获取原文并翻译 | 示例
   

获取外文期刊封面封底 >>

       

摘要

We present the mathematical deduction and properties of the mean traveltime curves for homogeneous elliptical anisotropic media. These curves generalize their isotropic counterparts which have been introduced in the past as a simple data quality analysis technique at the pre-inversion stage for 2D transmission experiments, allowing the inference of prior velocity models to gain stability at the tomographic inversion. Also, the anisotropy parameters (maximum velocity, anisotropic direction and ratio) are shown to affect the shape of these curves. The degree of asymmetry of the anisotropic mean traveltime curves (displacement of the mean time and standard deviation minima from the middle of the gathering line) is related to the direction of anisotropy which can then be visually estimated. Least squares' fitting of the anisotropic theoretical models to their experimental counterparts is an effective method to estimate at the pre-inversion stage a macroscopic elliptical anisotropic velocity model, valid at the scale of the experiment, and able to match the experimental mean traveltime distribution. Sensitivity analysis has shown that the mean curve is less prone to errors than the standard deviation curve. Parameter identification from the standard deviation curve becomes unstable for noise levels higher than 5%; data errors produce smearing of the value of the estimated anisotropy ratio and wrong directions of anisotropy biased towards zero degrees. Also, identification from the mean traveltime curve becomes stable when the maximum velocity is well constrained. Finally, this methodology is illustrated with the application to the Grimsel data set. Performing MTC analysis is always recommended since it does not need high numerical requirements, and as shown in the sensibility analysis section, errors in data can be misinterpreted as geological anisotropies.
机译:我们介绍了均质椭圆各向异性介质的平均传播时间曲线的数学推导和性质。这些曲线概括了它们的各向同性对应物,这些对应物是过去作为2D传输实验的反演前阶段的简单数据质量分析技术引入的,从而可以推断先前的速度模型在层析反演中获得稳定性。同样,各向异性参数(最大速度,各向异性方向和比率)显示为影响这些曲线的形状。各向异性平均行进时间曲线的不对称程度(平均时间的位移和标准差最小值(从集线的中点开始))与各向异性的方向有关,然后可以通过视觉方式进行估计。各向异性理论模型的最小二乘拟合与实验对应模型是在反演阶段估算宏观椭圆各向异性速度模型的有效方法,该模型在实验规模上有效,并且能够匹配实验平均旅行时间分布。灵敏度分析表明,平均曲线比标准偏差曲线更不容易出错。对于噪声水平高于5%的情况,从标准偏差曲线进行参数识别变得不稳定;数据错误会导致估计的各向异性比率值出现拖尾现象,并且各向异性的错误方向会偏向零度。同样,当最大速度受到很好的约束时,从平均行进时间曲线的识别变得稳定。最后,将这种方法与Grimsel数据集的应用进行了说明。始终建议执行MTC分析,因为它不需要很高的数值要求,并且如敏感性分析部分所示,数据中的错误可能被误解为地质各向异性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号