Large-scale stochastic inverse and data assimilation problems with applications to subsurface imaging

点击翻译按钮获取中文摘要

Improvements in sensing technology and experimental measurements have led to an explosion of data in many fields. This is the case for example in imaging and inverse problems. Although many numerical algorithms are available to process this data, until recently the computational cost of processing the data was not the main issue. For example in the field of imaging the space below the land surface of the earth, with application in groundwater and oil exploration, CO2 sequestration, contaminant location, etc., researchers used to have only a limited number of measurements and only coarse reconstruction of the underground was possible. Nowadays it is possible to have hundreds of thousands of measurement data collected at different locations and times. As a result the volume of data to process as well as the resolution of the predictions that are possible have increased tremendously, making many existing methods impractical. To explore the range of solutions consistent with measurements, the prevalent approach is based on a stochastic or probabilistic description of the subsurface properties and is called stochastic inversing. This research will apply novel numerical algorithms to address computational challenges for stochastic inversing in this context.In recent years, there has been a renewed interest in the subsurface (underground) for example in the context of CO2 storage. In this example, CO2 is extracted from emissions in thermal power plants and then injected underground under a geologic layer that is impermeable to CO2. This is an effective technology to reduce CO2 emissions to the atmosphere and allows the continued use of fossil fuel plants such as coal plants without releasing large amounts of greenhouse gases. This technology however has drawbacks; in particular it requires monitoring of the CO2 plume underground to make sure that there is no leak to the surface. This is important for example to provide guarantees to the public, in cases where the surface area above the injection site is populated. Although leakage is extremely rare and would not be a serious cause of concern (CO2 does not become toxic until high levels of concentration), such monitoring is required for complete confidence. Among other objectives, this project will develop novel computer algorithms to allow reconstructing the location of CO2 plumes from measurements taken for example at well sites.