There are some limitations when we apply conventional methods to analyze the massive amounts of seismic data acquired with high-density spatial sampling since processors usually obtain the properties of raw data from ...There are some limitations when we apply conventional methods to analyze the massive amounts of seismic data acquired with high-density spatial sampling since processors usually obtain the properties of raw data from common shot gathers or other datasets located at certain points or along lines. We propose a novel method in this paper to observe seismic data on time slices from spatial subsets. The composition of a spatial subset and the unique character of orthogonal or oblique subsets are described and pre-stack subsets are shown by 3D visualization. In seismic data processing, spatial subsets can be used for the following aspects： （1） to check the trace distribution uniformity and regularity; （2） to observe the main features of ground-roll and linear noise; （3） to find abnormal traces from slices of datasets; and （4） to QC the results of pre-stack noise attenuation. The field data application shows that seismic data analysis in spatial subsets is an effective method that may lead to a better discrimination among various wavefields and help us obtain more information.展开更多
In this paper we present a novel method for dividing and clustering large volumetric scalar out-of-core datasets. This work is based on the Ordered Cluster Binary Tree (OCBT) structure created using a top-down or divi...In this paper we present a novel method for dividing and clustering large volumetric scalar out-of-core datasets. This work is based on the Ordered Cluster Binary Tree (OCBT) structure created using a top-down or divisive clustering method. The OCBT structure allows fast and efficient sub volume queries to be made in combination with level of detail (LOD) queries of the tree. The initial partitioning of the large out-of-core dataset is done by using non-axis aligned planes calculated using Principal Component Analysis (PCA). A hybrid OCBT structure is also proposed where an in-core cluster binary tree is combined with a large out-of-core file.展开更多
Recently,the expertise accumulated in the field of geovisualization has found application in the visualization of abstract multidimensional data,on the basis of methods called spatialization methods.Spatialization met...Recently,the expertise accumulated in the field of geovisualization has found application in the visualization of abstract multidimensional data,on the basis of methods called spatialization methods.Spatialization methods aim at visualizing multidimensional data into low-dimensional representational spaces by making use of spatial metaphors and applying dimension reduction techniques.Spatial metaphors are able to provide a metaphoric framework for the visualization of information at different levels of granularity.The present paper makes an investigation on how the issue of granularity is handled in the context of representative examples of spatialization methods.Furthermore,this paper introduces the prototyping tool Geo-Scape,which provides an interactive spatialization environment for representing and exploring multidimensional data at different levels of granularity,by making use of a kernel density estimation technique and on the landscape "smoothness" metaphor.A demonstration scenario is presented next to show how Geo-Scape helps to discover knowledge into a large set of data,by grouping them into meaningful clusters on the basis of a similarity measure and organizing them at different levels of granularity.展开更多
文摘There are some limitations when we apply conventional methods to analyze the massive amounts of seismic data acquired with high-density spatial sampling since processors usually obtain the properties of raw data from common shot gathers or other datasets located at certain points or along lines. We propose a novel method in this paper to observe seismic data on time slices from spatial subsets. The composition of a spatial subset and the unique character of orthogonal or oblique subsets are described and pre-stack subsets are shown by 3D visualization. In seismic data processing, spatial subsets can be used for the following aspects： （1） to check the trace distribution uniformity and regularity; （2） to observe the main features of ground-roll and linear noise; （3） to find abnormal traces from slices of datasets; and （4） to QC the results of pre-stack noise attenuation. The field data application shows that seismic data analysis in spatial subsets is an effective method that may lead to a better discrimination among various wavefields and help us obtain more information.
文摘In this paper we present a novel method for dividing and clustering large volumetric scalar out-of-core datasets. This work is based on the Ordered Cluster Binary Tree (OCBT) structure created using a top-down or divisive clustering method. The OCBT structure allows fast and efficient sub volume queries to be made in combination with level of detail (LOD) queries of the tree. The initial partitioning of the large out-of-core dataset is done by using non-axis aligned planes calculated using Principal Component Analysis (PCA). A hybrid OCBT structure is also proposed where an in-core cluster binary tree is combined with a large out-of-core file.
文摘Recently,the expertise accumulated in the field of geovisualization has found application in the visualization of abstract multidimensional data,on the basis of methods called spatialization methods.Spatialization methods aim at visualizing multidimensional data into low-dimensional representational spaces by making use of spatial metaphors and applying dimension reduction techniques.Spatial metaphors are able to provide a metaphoric framework for the visualization of information at different levels of granularity.The present paper makes an investigation on how the issue of granularity is handled in the context of representative examples of spatialization methods.Furthermore,this paper introduces the prototyping tool Geo-Scape,which provides an interactive spatialization environment for representing and exploring multidimensional data at different levels of granularity,by making use of a kernel density estimation technique and on the landscape "smoothness" metaphor.A demonstration scenario is presented next to show how Geo-Scape helps to discover knowledge into a large set of data,by grouping them into meaningful clusters on the basis of a similarity measure and organizing them at different levels of granularity.
