Seismic Inference System for Geocontextual Object Identification
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Solution Overview
Problem
Current seismic data analysis methods lack a computational framework that leverages expert knowledge and sophisticated pattern recognition techniques to identify complex geo-objects from multiple seismic attributes, leading to subjective and inefficient interpretation of large three-dimensional data sets.
Innovation Solution
An inference system that utilizes geophysical concepts and expert feedback to analyze multiple seismic attributes using geocontextual modeling, graphical modeling, and spatial contextual rules, allowing for the automated identification and ranking of anomalies and events in seismic data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional computer software applications are used for seismic data analysis, then visualization and basic feature extraction capabilities are provided, but the interpretation process remains subjective and lacks sophisticated pattern recognition techniques
Solution Approach 1:
The system incorporates iterative feedback loops where initial automated interpretations are refined through multiple processing stages, allowing the system to learn from and improve upon its own outputs while maintaining objective consistency
Solution Approach 2:
A computational framework acts as an intermediary between raw seismic data and final interpretations, applying sophisticated pattern recognition algorithms and geocontextual rules to bridge the gap between simple visualization and expert-level analysis
2Productivity
If manual seismic interpretation is performed by experts, then complex geo-objects can be identified using experience and knowledge, but the process is time-consuming and subjective
Solution Approach 1:
The system performs self-service by automatically applying expert knowledge encoded in geocontextual rules and patterns to seismic data, eliminating the need for continuous manual expert intervention while maintaining high analysis quality
Solution Approach 2:
The system transforms qualitative expert knowledge into quantitative parameters and rules that can be systematically applied to large datasets, converting subjective interpretation criteria into objective, reproducible analysis parameters
3Reliability
If multiple seismic attributes are analyzed simultaneously, then more comprehensive geo-object identification is achieved, but the complexity of integrating and interpreting multiple data sources increases
Solution Approach 1:
The system merges multiple seismic attributes into a unified analysis framework using geocontextual rules that define how different attributes relate to each other in geological contexts, allowing comprehensive analysis without managing each attribute separately
Solution Approach 2:
The computational framework serves multiple functions simultaneously: it analyzes individual attributes, integrates them according to geocontextual rules, identifies patterns across attributes, and generates interpretations, reducing the need for separate specialized tools
Data Source
AI summary
A workflow is presented that facilitates defining geocontextual information as a set of rules for multiple seismic attributes. Modeling algorithms may be employed that facilitate analysis of multiple seismic attributes to find candidate regions that are most likely to satisfy the set of rules. These candidates may then be sorted based on how well they represent the geocontextual information.