Seismic Data Separation Using Derived Ground Force Estimates
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Solution Overview
Problem
Conventional seismic vibrator technologies face challenges in accurately separating seismic signals due to imperfect Ground Force Estimates (GFE), particularly at higher frequencies, leading to cross-talk and leakage issues in vibroseis systems, which affect the quality of seismic data acquisition.
Innovation Solution
The method involves using the total dataset from a grouping of vibratory sources and receivers, performing iterative inversion with optimization parameters derived from initial GFE to minimize cross-talk, and subtracting crosstalk from the initial GFE to obtain a derived GFE for improved seismic data separation without requiring additional equipment or re-engineering of existing systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional Ground Force Estimate (GFE) methods are used for seismic data separation, then the system maintains simplicity and avoids additional equipment, but cross-talk and leakage occur between different vibratory sources especially at higher frequencies
Solution Approach 1:
The patent implements an iterative feedback process where the initial GFE is used to separate seismic data, the separated data is analyzed for cross-talk, and the GFE is refined based on this analysis. This cycle repeats until cross-talk is minimized, creating a self-correcting system that improves separation accuracy without additional hardware.
Solution Approach 2:
The patent replaces the need for mechanical load sensors with a computational approach. Instead of physically measuring ground force with additional sensors, the system uses signal processing and iterative mathematical optimization to derive accurate GFE from existing accelerometer data and seismic records.
2Measurement precision
If load sensors are installed to measure true ground force, then measurement accuracy improves, but device complexity and cost increase
Solution Approach 1:
The patent creates a computational model that replicates the function of load sensors. By using the weighted-sum method with accelerometer measurements and iterative optimization, the system generates a virtual representation of true ground force that matches what load sensors would measure, without requiring the physical sensors themselves.
Solution Approach 2:
The system uses its own existing sensors (accelerometers on the baseplate and reaction mass) and seismic recording capabilities to measure and refine the GFE. The vibrator system essentially measures its own performance using data already being collected during normal operation, eliminating the need for external measurement devices.
3Measurement precision
If iterative inversion with optimization is performed to minimize cross-talk, then separation accuracy improves, but processing time and computational complexity increase
Solution Approach 1:
The patent performs preliminary separation using the initial GFE before refinement. This allows for quick initial results and enables progressive improvement rather than requiring complete processing from scratch. The iterative process can be stopped at any point, providing flexible trade-offs between processing time and accuracy.
Solution Approach 2:
The patent applies partial optimization by focusing the iterative refinement on minimizing cross-talk specifically, rather than optimizing all parameters simultaneously. This targeted approach reduces computational complexity while achieving the primary goal of improving separation accuracy.
Data Source
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AI summary
Imperfect separation at the higher frequencies has been observed and was eventually was tracked down to the poor GFE signal that is normally used in the inversion. The invention thus uses a "derived GFE" for each source, obtained by comparing the shot records and remove the differences, instead of the prior estimated GFE signal put out by the controller, thus accurately maximizing the separation of the data.