Parallel Pattern Query Evaluation for Seismic Feature Identification
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Solution Overview
Problem
Seismic interpretation in identifying features of interest in large N-dimensional datasets is challenging due to the combinatorial nature of pattern search in N-dimensional structures, which are common in seismic data, and requires efficient methods to reduce uncertainty and speed up the process.
Innovation Solution
The method involves parallel evaluation of pattern queries over large N-dimensional datasets using similarity-based pattern matching, employing algebraic operators like Trace Match, Candidate Solution, and Ranking operators, which account for differences in scale, shape, and rotation, and dynamically select operators based on query characteristics and computing resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual seismic interpretation is performed by highly skilled interpreters, then measurement precision of geological features is improved, but loss of time increases significantly
Solution Approach 1:
The patent segments the large N-dimensional seismic dataset into multiple smaller partitions or blocks that can be processed independently and in parallel. This segmentation allows the interpretation task to be divided among multiple processing units, significantly reducing the overall time required while maintaining the ability to identify geological features with high precision through coordinated processing of all segments.
Solution Approach 2:
The patent performs preliminary actions by pre-processing the seismic data to identify and extract potential features of interest before the main interpretation task. This includes pre-computing distance metrics, pre-identifying candidate patterns, and preparing data structures that enable faster subsequent processing, thereby reducing the time required for detailed analysis while preserving measurement precision.
2Productivity
If automatic pattern search is implemented in large N-dimensional datasets, then productivity is improved, but measurement precision deteriorates due to combinatorial complexity
Solution Approach 1:
The patent applies local quality by focusing the pattern matching process on local regions or neighborhoods within the N-dimensional dataset rather than performing exhaustive global searches. This allows the system to efficiently identify patterns in relevant local areas while maintaining high precision through targeted comparison with reference patterns, avoiding the combinatorial explosion of exhaustive search methods.
Solution Approach 2:
The patent changes parameters by transforming the search problem into a distance metric evaluation task. Instead of directly searching for patterns in the original N-dimensional space, the system transforms data representations and evaluates candidate patterns using defined distance metrics, making the search more efficient while maintaining accuracy through mathematical transformations that preserve pattern characteristics.
3Measurement precision
If exhaustive pattern search is performed considering all positions and rotations, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent creates a universal distance metric evaluation framework that can handle multiple pattern matching scenarios (different positions, orientations, and scales) through a single unified computational approach. This universal method eliminates the need for separate processing pipelines for each search variant, reducing computational complexity while maintaining comprehensive pattern detection capability across all positions and rotations.
Solution Approach 2:
The patent applies partial action by performing pattern matching on a selected subset of candidate positions and orientations rather than exhaustively evaluating all possible configurations. The system strategically samples or prioritizes likely pattern locations based on preliminary analysis, achieving sufficient measurement precision for practical applications while dramatically reducing the computational complexity compared to exhaustive search.
Data Source
AI summary
Pattern queries are evaluated in parallel over large N-dimensional datasets to identify features of interest. Similarity-based pattern matching tasks are executed over N-dimensional input datasets comprised of numeric values by providing data representations for the N-dimensional input datasets, a pattern query and one or more candidate solutions for the pattern query, such that the pattern query specifies a pattern of an N-dimensional body that is compared to at least one candidate solution corresponding to an N-dimensional body extracted from the N-dimensional input datasets; defining a distance metric that compares the N-dimensional body formed by the candidate solution extracted from the N-dimensional input datasets and the N-dimensional body formed by the pattern query, taking into account one or more of the following criteria: differences between mapped values, differences in scale and differences in shape; and executing, in parallel, a plurality of independent instances of at least one algebraic operator to generate and score the candidate solutions based on the distance metric. The exemplary algebraic operators comprise a Trace Match operator, a Ranking operator, a Candidate Solution operator, and a Query Clustering operator.