Adaptive Seismic Horizon Tracking Across Multi-Wave Data
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Solution Overview
Problem
Existing seismic horizon auto-tracking methods face challenges in precision and adaptability due to varying seismic wave types, propagation velocities, and differing resolutions and sampling intervals in multi-wave and multi-domain seismic data, leading to difficulties in setting optimal horizon tracking parameters.
Innovation Solution
A multi-wave and multi-domain adaptive seismic horizon auto-tracking method that dynamically adjusts horizon tracking parameters based on seismic relative resolution, using iterative calculations and curve fitting to identify horizon points without presetting window sizes, suitable for both time-domain and depth-domain seismic data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional horizon tracking parameters are used, then the method is simple to operate, but the precision and adaptability deteriorate due to varying seismic wave types and propagation velocities
Solution Approach 1:
The patent implements dynamic adjustment of horizon tracking parameters based on real-time seismic waveform characteristics. The system automatically modifies search window sizes, sampling intervals, and tracking parameters according to the detected seismic wave type, propagation velocity, and resolution, transforming static parameters into dynamic adaptive parameters that evolve with the seismic data characteristics.
Solution Approach 2:
The patent systematically changes multiple parameters including search window size, sampling interval, wave type identification thresholds, and propagation velocity models based on the analyzed seismic characteristics. By adjusting these parameters dynamically, the system adapts to different seismic datasets without requiring manual reconfiguration, resolving the contradiction between precision and complexity.
2Adaptability or versatility
If fixed window sizes are used for horizon tracking, then the operation is simplified, but the adaptability deteriorates across different seismic resolutions and sampling intervals
Solution Approach 1:
The system performs self-service by automatically analyzing seismic waveform characteristics and adjusting its own parameters without external intervention. The algorithm independently identifies wave types, measures propagation velocities, determines optimal search window sizes, and configures sampling intervals, eliminating the need for user input or manual parameter setting while maintaining high adaptability across diverse seismic datasets.
Solution Approach 2:
The patent applies preliminary action by pre-establishing a comprehensive parameter adjustment framework and pre-calculating optimal parameter values based on seismic characteristics before the actual horizon tracking begins. The system pre-analyzes the seismic data to determine appropriate search window sizes and sampling intervals in advance, enabling seamless adaptive tracking without requiring user configuration during execution.
3Productivity
If manual horizon picking is used, then the precision can be controlled, but the productivity deteriorates due to time-consuming processing
Solution Approach 1:
The patent implements feedback mechanisms where the system continuously monitors the quality of horizon tracking results and adjusts parameters accordingly. The algorithm analyzes the consistency and accuracy of detected horizons in real-time, providing feedback to modify search window sizes, sampling intervals, and tracking algorithms dynamically. This closed-loop feedback enables the system to maintain high precision while processing large datasets efficiently, automating what would otherwise require time-consuming manual verification.
Data Source
AI summary
The present disclosure provides a multi-wave and multi-domain adaptive seismic horizon auto-tracking method and apparatus. The multi-wave and multi-domain adaptive seismic horizon auto-tracking method comprises: importing seismic data, and setting a seed point on the seismic data; determining a seismic relative resolution according to a seismic waveform of a seismic trace where the seed point is located; determining horizon tracking parameters according to the seismic relative resolution; determining a horizon point according to the horizon tracking parameters and the seismic trace where the seed point is located; replacing the seed point with the horizon point, and performing corresponding iterative calculation to obtain a plurality of horizon points in one-to-one correspondence with the seismic traces in the seismic data; and combining horizon values corresponding to the plurality of horizon points into seismic horizon data and exporting the seismic horizon data.