利用根据地球动力学训练的生成神经网络完善层析成像技术

IF 2.8 3区 地球科学 Q2 GEOCHEMISTRY & GEOPHYSICS
T Santos, T Bodin, F Soulez, Y Ricard, Y Capdeville
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引用次数: 0

摘要

摘要 在地球物理学的许多领域都会出现逆问题,即利用地表观测结果来推断地球的内部结构。鉴于这些问题固有的非线性和非唯一性,标准的策略是纳入有关未知模型的先验信息。有时,通过强制要求反演模型与参考模型保持接近,并具有平滑的横向变化(例如,强制要求相关长度或最小波长),就能获得解决方案。这种方法可以避免在恢复的模型中出现强烈的梯度或不连续性。诚然,不连续性,如层与层之间的界面,或地质带或地质物体(如板块)的形状,可以先验地强加,甚至由数据本身提出。不过,这仅限于一小部分可能的约束条件。例如,在俯冲板块的顶部可能存在形状未知的不连续面的情况下,进行层析反演是非常具有挑战性的,而且计算成本很高。这个问题似乎很棘手,因为我们甚至无法想象如何对先验空间进行采样:每个特定的板块是连续的,还是分成具有各自界面的不同部分?似乎没有一套连续的参数可以描述我们可以考虑的所有可能的界面。为了规避这些问题,我们建议训练一个生成对抗神经网络(GAN),根据从地球动力学模拟中获得的地质上可信的先验分布生成模型。在贝叶斯框架下,使用马尔科夫链蒙特卡洛算法对描述潜在地质模型集合的低维模型空间进行采样。这样就能整合复杂的先验信息,并在有利于高效采样的低维模型空间内进行参数化。该方法在降尺度问题中的应用得到了验证,降尺度问题的目标是从平滑的地震层析成像中推断小尺度地质结构。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Refining tomography with generative neural networks trained from geodynamics
Summary Inverse problems occur in many fields of geophysics, wherein surface observations are used to infer the internal structure of the Earth. Given the non-linearity and non-uniqueness inherent in these problems, a standard strategy is to incorporate a priori information regarding the unknown model. Sometimes a solution is obtained by imposing that the inverted model remains close to a reference model and with smooth lateral variations (e.g., a correlation length or a minimal wavelength are imposed). This approach forbids the presence of strong gradients or discontinuities in the recovered model. Admittedly, discontinuities, such as interfaces between layers, or shapes of geological provinces or of geological objects such as slabs can be a priori imposed or even suggested by the data themselves. This is however limited to a small set of possible constraints. For example, it would be very challenging and computationally expensive to perform a tomographic inversion where the subducting slabs would have possible top discontinuities with unknown shapes. The problem seems formidable because one cannot even imagine how to sample the prior space: is each specific slab continuous or broken into different portions having their own interfaces? No continuous set of parameters seems to describe all the possible interfaces that we could consider. To circumvent these questions, we propose to train a Generative Adversarial neural Network (GAN) to generate models from a geologically plausible prior distribution obtained from geodynamical simulations. In a Bayesian framework, a Markov chain Monte Carlo algorithm is used to sample the low-dimensional model space depicting the ensemble of potential geological models. This enables the integration of intricate a priori information, parametrized within a low-dimensional model space conducive to efficient sampling. The application of this approach is demonstrated in the context of a downscaling problem, where the objective is to infer small-scale geological structures from a smooth seismic tomographic image.
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来源期刊
Geophysical Journal International
Geophysical Journal International 地学-地球化学与地球物理
CiteScore
5.40
自引率
10.70%
发文量
436
审稿时长
3.3 months
期刊介绍: Geophysical Journal International publishes top quality research papers, express letters, invited review papers and book reviews on all aspects of theoretical, computational, applied and observational geophysics.
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