Real-Time Switching And Visualization Of Logging Attributes Based On Subspace Learning

In three-dimensional visualization, sufficient memory and computing power can ensure real-time graphics rendering. However, due to equipment and algorithm performance limitations, it is difficult to graphically present big volume data efficiently and accurately, especially for high-dimensional and large volume geological data. In this paper we propose a real-time visualization method for logging data, which combines volume data compression and fast switching algorithm. First, we introduce an adaptive sampling method for large volume of data compression. Each block of the same size is sampled according to the dispersion and the sampling density grade, after which ray casting algorithm is used to render compressed volume data. Second, aiming at the graphic presentation delay caused by the exchange of large amounts of data in internal and external memory, a fast switching algorithm(FSA) based on subspace learning is presented. The attributes with strong correlation are put into the same group, from which feature subspace are learned and a mapping model between associated attributes is established according to base vector invariance. Once we need to switch from the currently displayed attribute to another for display, only a few coefficient values in the mapping model need to be changed, reducing the amount of data exchange. Our proposed method can greatly increase the compression ratio and reduce the computing time, ensuring real-time visualization for geological data.