Seismic First Break Curve Fitting for Noise Reduction
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Solution Overview
Problem
Automating the process of determining first breaks in seismic surveys is challenging due to the need for precise and accurate identification of seismic waves across numerous seismic traces, often requiring refinement from approximate or noisy initial picks.
Innovation Solution
A method involving the selection of proximal picks within a time window, determining near-offset and far-offset picks, and fitting a first break curve to coincident picks to refine the set of first breaks in a seismic dataset, utilizing a computer-readable medium and seismic processor to automate this process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated first break picking is implemented, then productivity is improved, but measurement precision deteriorates due to noisy and erroneous picks
Solution Approach 1:
The patent implements an iterative feedback process where initial automated picks are evaluated, and erroneous picks are identified and corrected by comparing with neighboring traces. The system uses the picked first breaks to update velocity models, which then feed back into the picking process to improve accuracy in subsequent iterations.
Solution Approach 2:
The system performs self-correction by using the picked first breaks from one trace to inform and improve the picking on adjacent traces. The automated system refines its own output by identifying inconsistent picks and correcting them based on geological continuity principles without requiring manual intervention for each pick.
2Productivity
If approximate first break picks are determined quickly, then productivity is improved, but measurement precision worsens requiring later refinement
Solution Approach 1:
The patent performs preliminary automated picking to establish initial first break estimates quickly across all traces. This preliminary action provides a starting point that can be rapidly processed, and subsequent refinement steps then improve the quality of these initial picks through comparison and correction algorithms.
Solution Approach 2:
The picking process is segmented into distinct phases: initial automated picking, error identification, correction, and refinement. This segmentation allows the system to handle large volumes of data quickly in the initial phase, then focus computational resources on refining specific problematic areas in subsequent phases.
3Measurement precision
If manual refinement of first breaks is performed, then measurement precision is improved, but productivity deteriorates due to time-consuming processes
Solution Approach 1:
The system performs self-refinement by automatically identifying erroneous picks through comparison with neighboring traces and geological constraints, then correcting them without requiring manual intervention. This self-service capability maintains high measurement precision while preserving productivity by eliminating the need for time-consuming manual review of each pick.
Solution Approach 2:
The automated refinement process uses feedback from velocity models and neighboring trace information to identify and correct erroneous picks. This feedback-driven approach achieves manual-quality refinement automatically, maintaining precision without the productivity loss associated with manual processes.
4Measurement precision
If velocity models are updated frequently, then measurement precision is improved, but use of energy increases due to computational requirements
Solution Approach 1:
The system updates velocity models periodically at strategic points in the iterative process rather than continuously after each pick. This periodic updating maintains measurement precision by ensuring models are current when needed, while reducing computational energy consumption by avoiding unnecessary frequent updates.
Data Source
AI summary
A system and method for determining a set of first breaks of a seismic dataset are disclosed, the method including obtaining the seismic dataset composed of a plurality of seismic traces and a provisional first break for each seismic trace. The method further includes selecting a plurality of proximal picks for each seismic trace, determining a near-offset pick for each seismic trace starting with shortest offset and sequentially selecting traces in order of increasing offset, and determining a far-offset pick for each seismic trace starting with the farthest offset and sequentially selecting traces in order of decreasing offset. The method further includes determining a set of coincident picks based on the near-offset and the far-offset picks for each seismic trace, fitting a curve to the set of coincident picks, and determining the set of first breaks of the seismic dataset from the curve.


