Transdimensional Electrical Resistivity Tomography
Transdimensional Electrical Resistivity Tomography
复制标题
跨维电阻率断层扫描
DOI：
10.1029/2017jb015418
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Andrew Curtis
中科院分区：
文献类型：
--
作者：
E. Galetti;Andrew Curtis
本文表明，成像的固体内部与完全非线性物理可以是非常有益的成像与等效的线性层析成像方法相比，这是真实的各种不同类型的物理。包括完全非线性提供了可解释的不确定性和更大的深度的图像渗透到未知的目标，如地球的地下。我们使用一个自适应参数化的蒙特卡罗方法来反演电阻率数据的电导率结构的地球，并证明了该方法的两个字段数据集。主要结果包括观察直接解释的不确定性循环，定义可能的几何变化的边缘孤立的异常，从而量化这些边界的空间分辨率。这些拓扑结构的不确定性是类似的观察时，执行完全非线性地震走时层析成像。这表明，在使用各种数据类型和物理定律（这里是拉普拉斯方程;在以前的工作中是Eikonal或射线方程）的各种层析成像问题的解决方案中，预期会出现类似于回路的不确定性拓扑。我们还表明，深度，我们可以构建一个断层图像使用电气数据扩展到8个因素，使用非线性方法相比，使用常见的标准线性化程序的线性化反演。这些优点是以显著增加的计算为代价的。所有这些结果都说明了合成和真实的数据的例子。
This paper shows that imaging the interior of solid bodies with fully nonlinear physics can be highly beneficial compared to imaging with the equivalent linearized tomographic methods and that this is true for a variety of different types of physics. Including full nonlinearity provides interpretable uncertainties and far greater depth of image penetration into unknown targets such as the Earth's subsurface. We use an adaptively parameterized Monte Carlo method to invert electrical resistivity data for the conductivity structure of the Earth and demonstrate the method on two field data sets. Key results include the observation of directly interpretable uncertainty loops which define possible geometrical variations in the edges of isolated anomalies, hence quantifying the spatial resolution of these boundaries. These topologies of uncertainties are similar to those observed when performing fully nonlinear seismic traveltime tomography. This shows that loop‐like uncertainty topologies are expected in the solutions to a wide variety of tomographic problems, using a variety of data types and hence laws of physics (here the Laplace equation; in previous work the Eikonal or ray equations). We also show that the depth to which we can construct a tomographic image using electrical data is extended by up to a factor of 8 using nonlinear methods compared to linearized inversion using common standard linearized programs. These advantages come at the cost of significantly increased computation. All of these results are illustrated on both synthetic and real data examples.
影响因子：
5.3
作者：
Gallagher, Kerry;Bodin, Thomas;Large, David
通讯作者：
Large, David