Markov chain Monte Carlo inversion of Rock Deformation Data: Applications to the Dynamics of Oceanic Mantle

GSTDTAP

项目编号	1736563
	Markov chain Monte Carlo inversion of Rock Deformation Data: Applications to the Dynamics of Oceanic Mantle
	Jun Korenaga
主持机构	Yale University
项目开始年	2017
	2017-08-15
项目结束日期	2019-07-31
资助机构	US-NSF
项目类别	Standard Grant
项目经费	161232(USD)
国家	美国
语种	英语
英文摘要	The dynamics of Earth's mantle are responsible for all kinds of geological activities including earthquakes, volcanic eruptions, continental drift, and even the long-term carbon cycle that regulates the surface temperature. The most important parameter of mantle dynamics is viscosity, which dictates how fast rocks can deform, but viscosity is also among the least understood properties of Earth materials. Rock deformation experiments provide important constraints on viscosity, but laboratory conditions are vastly different from mantle conditions; the rate of deformation for the mantle is 10 orders of magnitude slower than that for the laboratory experiments. By combining recent progress in the analysis of experimental data with a new kind of geodynamical modeling, the proposed project will build a theoretical framework that effectively bridges rock deformation experiments and geophysical observations and allows an improved understanding of mantle viscosity. Owing to its relatively simple tectonic setting, the dynamics of the oceanic mantle will serve as an ideal test bed for this multidisciplinary project. The project supports the training of a graduate student and provides topics for undergraduate students conducting senior research projects. The deformation of silicate rocks depends on a number of factors, including temperature, pressure, stress, grain size, water content, melt fraction, and oxygen fugacity. Incorporating such factors into geophysical modeling has become increasingly more common in recent years. At the same time, there have been two important threads of development in experimental rock mechanics, both of which can directly impact such efforts to incorporate the knowledge of rock mechanics into geophysical modeling. First, the rheology of olivine aggregates, which is usually thought to control the dynamics of the upper mantle, has long been considered to be governed mostly by the combination of diffusion creep and dislocation creep, but recent experimental studies suggest that grain boundary sliding may play a more important role than previously thought. Second, we have seen the development of a new statistical framework, using Markov Chain Monte Carlo (MCMC) sampling, that helps us to tackle the full complexity of estimating flow laws from deformation data. The improved command of MCMC inversion puts us into a unique position to conduct a new kind of geophysical modeling for the dynamics of suboceanic mantle by taking advantage of recent progress in both experimental rock mechanics and observational seismology. This project has three major objectives: (1) construct candidate flow-law models for olivine aggregates based on published experimental data; (2) conduct probabilistic modeling for the dynamics of suboceanic mantle; and (3) identify key flow-law uncertainties and design possible experimental setups that can resolve such uncertainties. The approach based on probabilistic modeling has potential to bring a better understanding of not only the dynamics of suboceanic mantle but also the rheology of olivine aggregates.
文献类型	项目
条目标识符	http://119.78.100.173/C666/handle/2XK7JSWQ/71541
专题	环境与发展全球科技态势
推荐引用方式 GB/T 7714	Jun Korenaga.Markov chain Monte Carlo inversion of Rock Deformation Data: Applications to the Dynamics of Oceanic Mantle.2017.