Geophysical Data Texture Segmentation Using Double-Windowed Clustering Analysis
AssigneeExxonMobile Upstream Research Company
InventorKrishnan Kumaran
Life of this patent
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Abstract
An automated method for texture segmentation (11) of geophysical data volumes, where texture is defined by double-window statistics of data values, the statistics being generated by a smaller pattern window moving around within a larger sampling window (12). A measure of “distance” between two locations is selected based on similarity between the double-window statistics from sampling windows centered at the two locations (13). Clustering of locations is then based on distance proximity (14).
Claims11
A computer-implemented method for performing texture segmentation of one or more volumes of geophysical data, comprising using local statistical distributions of data values, said distributions computed using two moving windows of user-selected size and shape, one being a pattern window and the other a sampling window larger than the pattern window, wherein the pattern window moves about within the sampling window to generate the local statistical distribution for the sampling window's location, and then selecting a distance measure of similarity followed by partitioning the geophysical data into clusters on a basis of application of the distance measure of similarity to the local statistical distributions for pairs of locations.
A computer program product, comprising a non-transitory computer usable medium having a computer readable program code embodied therein, said computer readable program code adapted to be executed to implement a method for performing texture segmentation of one or more volumes of geophysical data, said method comprising using local statistical distributions of data values, said distributions computed using two moving windows of user-selected size and shape, one being a pattern window and the other a sampling window larger than the pattern window, wherein the pattern window moves about within the sampling window to generate the local statistical distribution for the sampling window's location, and then selecting a distance measure of similarity followed by partitioning the geophysical data into clusters on a basis of application of the distance measure of similarity to the local statistical distributions for pairs of locations.
Dependencies are inferred from the claim text.