Reporter Topic:Bayesian supervised learning of spatially varying but sparsely measured geo-data
Reporter: Professor Yu Wang, City University of Hong Kong
Time:14:00, July.31, 2019
Location:Conference Room 8319, Department of Engineering
Spatial data (i.e., data depending on spatial coordinates, such as geographic locations) are ingredients of many important applications, including building information modeling (BIM) and smart city. Examples of spatial data include geotechnical data, seismic data, wind data, traﬃc data, hydrologic data, geological data, air quality data, soil & water contamination data. Although spatial data are spatially varying and correlated, they are often sparsely measured due to time, resource, or technical constraints. The seminar presents some emerging methods for eﬀective interpretation of sparsely measured spatial data using Bayesian supervised learning (BSL) and compressive sensing (CS). The BSL and CS results can also be used together with Karhunen–Loève (KL) expansion for random ﬁeld modeling. Some insights into the random ﬁeld modeling of site-speciﬁc spatial variability will be discussed.