A Gaussian-based framework for local Bayesian inversion of geophysical data to rock properties
A Gaussian-based framework for local Bayesian inversion of geophysical data to rock properties
复制标题
DOI：
10.1190/geo2015-0314.1
复制
发表时间：
2016-04
期刊：
影响因子：
3.3
通讯作者：
Martin Jullum;O. Kolbjørnsen
中科院分区：
文献类型：
--
作者：
Martin Jullum;O. Kolbjørnsen
在贝叶斯框架下，我们推导出了一个基于地球物理数据反演岩石性质的程序。其目的是达到一个广泛适用的和一般的程序，其中很少和弱的假设需要应用到地球物理行业内的各种反问题。我们的贝叶斯统计方法结合了基于采样的技术和高斯近似，以评估与岩石性质的后验分布相关的量的局部近似。这些近似量定义了贝叶斯反演。我们的方法的一个概念上的优势是，有一些限制的初始模型，允许现实的统计模型直接近似。该方法很容易并行化，并提供了一系列的程序，这给出了一个反相速度和精度之间的权衡。我们已经测试了在监测设置使用地震振幅的方法，通过评估合成的情况下，从Sleipner的真实的数据。
ABSTRACTWorking in a Bayesian framework, we have derived a procedure for inverting rock properties based on geophysical data. The purpose was to arrive at a widely applicable and general procedure in which few and weak assumptions are required for application to various inverse problems within the geophysical industry. Our Bayesian statistical approach combines sampling-based techniques and Gaussian approximations to assess local approximations to quantities related to the posterior distribution of rock properties. These approximated quantities define the Bayesian inversion. A conceptual advantage of our approach is that there are few restrictions on the initial model, allowing realistic statistical models to be approximated directly. The methodology is easily parallelized and offers a range of procedures, which gives a trade-off between inversion speed and accuracy. We have tested the approach in a monitoring setting using seismic amplitudes by evaluating a synthetic case and real data from the Sleipner ...