Seismic Attribute Rendering via Sub-Volume Statistical Comparison
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Solution Overview
Problem
Current seismic data processing techniques require extensive knowledge and experience to optimally manipulate rendering parameters, leading to time-consuming and potentially error-prone processes for novice and experienced users in visualizing and interpreting seismic attribute data, especially in identifying subtle geological features.
Innovation Solution
A method for determining rendering parameters and mappings of transfer functions by selecting a sub-volume from seismic data, computing statistical distributions for both the data volume and sub-volume, and deriving parameters to emphasize differences, thereby enhancing visualization and interpretation of seismic attributes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual manipulation of rendering parameters is used, then visualization quality can be optimized, but the process becomes time-consuming and error-prone
Solution Approach 1:
The system automatically performs the rendering parameter optimization process without requiring manual user intervention. The computer-executed method computes statistical distributions, compares sub-volume statistics to overall statistics, and derives optimal rendering parameters autonomously, eliminating the time-consuming manual manipulation while maintaining high visualization quality.
Solution Approach 2:
The method dynamically adjusts rendering parameters based on computed statistical distributions. By calculating statistics from seismic attribute data and deriving parameters that emphasize differences between sub-volumes and overall data, the system automatically optimizes visualization parameters without manual intervention, resolving the contradiction between quality optimization and time consumption.
2Measurement precision
If extensive knowledge and experience are required for parameter manipulation, then accurate visualization can be achieved, but the process becomes complex and error-prone
Solution Approach 1:
The system eliminates the need for extensive user knowledge and experience by performing parameter optimization autonomously. The computer-executed method independently computes statistical distributions, compares data characteristics, and derives optimal rendering parameters, transforming a complex expert-dependent process into an automated system that requires minimal user involvement while maintaining high visualization accuracy.
3Measurement precision
If rendering parameters are manually optimized, then subtle geological features can be identified, but the process becomes time-consuming
Solution Approach 1:
The method automatically computes statistical distributions from seismic attribute data and derives rendering parameters that emphasize differences between sub-volumes and overall data characteristics. This automated parameter adjustment enables efficient processing while maintaining the ability to identify subtle geological features, as the system objectively analyzes data statistics and optimizes visualization parameters without manual intervention.
Solution Approach 2:
The system replaces manual mechanical parameter adjustment with an automated computational process. By using computer-executed methods to compute statistics, compare data characteristics, and derive optimal parameters, the system substitutes human expertise with automated algorithms that efficiently process data and produce optimized visualizations, thereby improving productivity while maintaining feature detection accuracy.
Data Source
AI summary
Method for determining visualization rendering parameters for seismic data to heighten subtle differences. The full data volume and at least one sub-volume are processed in the inventive method (12). Statistics are extracted for the data or attributes of the data (13). Rendering parameters are derived based on comparing and computing the statistical information for the volume and sub-volumes (14).