Multi-dimensional Geophysical Data Visualization via Deep Learning Color Mapping
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Solution Overview
Problem
Existing methods for visualizing multi-dimensional geophysical data require users to assimilate separate images of layers and properties, making it a difficult and time-consuming task to analyze 3D geophysical data effectively.
Innovation Solution
A method using a deep convolutional neural network trained via backpropagation-enabled regression to create blended geophysical data attributes, which are then displayed in a multi-dimensional color space, allowing simultaneous visualization of multiple layers or properties as a single 2D image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If separate images of layers and properties are displayed side-by-side, then each layer or property can be visualized individually, but the user must manually assimilate multiple images which is difficult and time-consuming
Solution Approach 1:
The patent combines multiple separate geophysical attribute images into a single composite image by mapping multiple attributes to different color channels (RGB) or transparency levels. This merging allows users to view multiple layers and properties simultaneously in one display, eliminating the need to manually assimilate separate images and significantly reducing analysis time.
Solution Approach 2:
The patent adds a color dimension to the visualization by mapping geophysical attributes to color channels (e.g., red, green, blue) or transparency levels. This dimensional transformation allows multiple attributes that would normally require separate 2D images to be encoded within a single 2D image, enabling simultaneous visualization without increasing the number of displays required.
2Productivity
If multiple geophysical attributes are mapped to color channels, then simultaneous visualization is achieved, but the complexity of color mapping and attribute blending increases
Solution Approach 1:
The patent transforms geophysical attribute data into visual parameters by mapping attributes to color channel intensities (0-255) or transparency levels (0-100%). This parameter transformation automatically handles the complexity of blending multiple attributes, as the system manages the color mixing and transparency layering mathematically rather than requiring manual configuration of each attribute combination.
Data Source
AI summary
A method for visualization of multi-dimensional geophysical data involves combining several attributes from multi-dimensional geophysical data or seismic data using color modeling techniques and provides for the interpretation of data more efficiently by a user. A color space is defined and multi-dimensional geophysical data attributes are created along with blending filters, such as asymmetric blending filters. Blended multi-dimensional geophysical data attribute cubes are created from the blending filters and the geophysical data attributes by making a prediction using a deep convolutional neural network trained via a backpropagation-enabled regression process.