Seismic Traveltime Operator Correction via Modal Node Consistency
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Solution Overview
Problem
Seismic data processing is hindered by noise, leading to errors in estimating traveltime operators, which are crucial for combining seismic samples from different spatial locations.
Innovation Solution
A method and system that form nodes in a multi-dimensional space, determine traveltime operators for each node based on a portion of the seismic dataset within an aperture, and assign canonical operators. This process identifies modal and deviating nodes, allowing for the determination of replacement traveltime operators for deviating nodes based on modal nodes within a window.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traveltime operators are estimated from seismic data, then the ability to combine seismic samples from different spatial locations is improved, but noise in the seismic dataset causes errors in the estimation
Solution Approach 1:
The patent combines multiple estimated traveltime operators from neighboring nodes to form a consolidated operator. By merging information from multiple nodes and using consistency checks across the network, the method integrates multiple measurements to overcome the noise affecting individual estimates, thereby improving overall accuracy.
Solution Approach 2:
The patent implements a feedback mechanism where estimated traveltime operators are validated against consistency criteria and neighboring node estimates. When inconsistencies are detected (indicating noise-induced errors), the system iteratively adjusts the estimates by comparing with modal operators from the network, providing continuous feedback to refine the accuracy of traveltime operators.
2Reliability
If manual quality control is performed on traveltime operators, then error detection is improved, but the processing time and complexity increase
Solution Approach 1:
The patent implements self-service quality control where the traveltime operator estimation system automatically detects and corrects its own errors through consistency checks and comparison with neighboring nodes. The network of nodes performs mutual validation, eliminating the need for external manual quality control while maintaining high reliability.
Solution Approach 2:
The system uses automated feedback loops where each node's traveltime operator estimate is continuously validated against consistency criteria and neighboring estimates. This automated feedback mechanism replaces manual quality control, providing continuous error detection and correction without requiring human intervention, thus reducing processing time while maintaining reliability.
3Reliability
If extensive manual quality control is performed, then the reliability of traveltime operators is improved, but device complexity and operational difficulty increase
Solution Approach 1:
The system performs self-verification through automated consistency checks and mutual validation among neighboring nodes. This self-service approach inherently improves reliability without requiring complex external quality control infrastructure, as the network itself validates its own estimates through mathematical consistency criteria.
Solution Approach 2:
Automated feedback mechanisms continuously monitor and validate traveltime operator estimates against consistency criteria. This self-regulating feedback system improves reliability while minimizing device complexity, as the validation logic is embedded in the computational algorithm rather than requiring separate complex quality control equipment or procedures.
Data Source
AI summary
Methods and systems are disclosed. The methods may include obtaining a seismic dataset with a plurality of dimensions pertaining to a subterranean region of interest and forming a plurality of spatial nodes within the seismic dataset. The method may further include determining, for each node, a traveltime operator, based on a portion of the seismic dataset within an aperture surrounding the node, assigning a canonical operator based on the traveltime operator, and forming a plurality of windows containing a neighboring node. For each window, the method may include determining a modal canonical operator, based on the canonical operator for each neighboring node, determining modal-nodes and deviating-nodes within the window, and determining a replacement traveltime operator for each deviating-node. The method may still further include forming a seismic image based on the seismic dataset, the traveltime operator of the modal-nodes, and the replacement traveltime operator of the deviating-nodes.