Low frequency full waveform seismic inversion within a tree based Bayesian framework
Low frequency full waveform seismic inversion within a tree based Bayesian framework
复制标题
基于树的贝叶斯框架内的低频全波形地震反演
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
U. Albertin
中科院分区：
文献类型：
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作者：
A. Ray;S. Kaplan;J. Washbourne;U. Albertin
总结 有限的照明、不足的偏移、噪声数据和较差的启动模型可能给地震全波形反演带来挑战。我们提出了一种基于树的贝叶斯反演方案的应用，该方案试图通过考虑数据不确定性同时使用有关地下结构的轻度信息先验来减轻这些问题。我们在速度的小波变换域中使用跨维（trans-D）或可逆跳跃马尔可夫链蒙特卡罗方法对压缩速度的后验模型分布进行采样。这使我们能够快速收敛到后验模型的平稳分布，同时需要有限数量的小波系数来定义采样模型。提供了两个合成的低频噪声数据示例。第一个示例是简单的反射 + 传输逆问题，第二个示例使用 Marmousi 速度模型的缩放版本，以反射为主。这两个例子最初都是从背景速度不正确的半无限半空间开始的。我们发现，基于反维树的方法与用于导航崎岖可能性（即失配）地形的并行回火相结合，为解决难以优化的大规模地球物理反演问题提供了一种有前途的、易于推广的方法，但其中真实模型包含多个尺度的特征层次结构。
S U M M A R Y Limited illumination, insufficient offset, noisy data and poor starting models can pose challenges for seismic full waveform inversion. We present an application of a tree based Bayesian inversion scheme which attempts to mitigate these problems by accounting for data uncertainty while using a mildly informative prior about subsurface structure. We sample the resulting posterior model distribution of compressional velocity using a trans-dimensional (trans-D) or Reversible Jump Markov chain Monte Carlo method in the wavelet transform domain of velocity. This allows us to attain rapid convergence to a stationary distribution of posterior models while requiring a limited number of wavelet coefficients to define a sampled model. Two synthetic, low frequency, noisy data examples are provided. The first example is a simple reflection + transmission inverse problem, and the second uses a scaled version of the Marmousi velocity model, dominated by reflections. Both examples are initially started from a semi-infinite half-space with incorrect background velocity. We find that the trans-D tree based approach together with parallel tempering for navigating rugged likelihood (i.e. misfit) topography provides a promising, easily generalized method for solving large-scale geophysical inverse problems which are difficult to optimize, but where the true model contains a hierarchy of features at multiple scales.