Seismic Data Deghosting via Noise Estimation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Seismic data acquisition in marine environments faces challenges in deghosting and interpolating seismic data effectively, particularly due to measurement noise and unevenly spaced receiver positions, which affect the accuracy of subterranean geological formation imaging and hydrocarbon deposit detection.
Innovation Solution
The technique involves estimating characteristics of measurement noise and using these estimates to deghost seismic data, processing seismic data from multi-component sensors to separate upgoing and downgoing wavefields, and interpolating data at unevenly spaced receiver positions to generate unbiased estimates of the upgoing wavefield, employing methods such as maximum likelihood estimation and weighted least-squares techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional deghosting methods are used, then processing speed is maintained, but measurement noise reduces imaging accuracy
Solution Approach 1:
The deghosting process is segmented into distinct stages: first estimating measurement noise characteristics from the seismic data, then using these estimates to compute deghosted data. This segmentation allows each stage to be optimized independently, improving overall accuracy without proportionally increasing complexity.
Solution Approach 2:
The method performs preliminary estimation of measurement noise characteristics before the main deghosting operation. By preparing noise estimates in advance, the subsequent deghosting process can proceed more efficiently with improved accuracy, rather than dealing with noise effects during the main processing.
2Adaptability or versatility
If data is collected at unevenly spaced receiver positions, then survey flexibility is improved, but interpolation accuracy deteriorates
Solution Approach 1:
The method changes the statistical parameters used for interpolation by estimating measurement noise characteristics and incorporating them into the interpolation process. This allows accurate interpolation at unevenly spaced positions by adapting the statistical model to the actual noise conditions rather than assuming uniform spacing.
Solution Approach 2:
The patent replaces traditional mechanical interpolation methods (which assume regular spacing) with a statistical approach that uses estimated noise characteristics. This substitution allows the system to handle uneven spacing mathematically rather than requiring physical repositioning of receivers.
3Measurement precision
If measurement noise is not accounted for, then processing simplicity is maintained, but deghosting accuracy deteriorates
Solution Approach 1:
The method introduces feedback by using estimated measurement noise characteristics to inform and adjust the deghosting process. The noise estimates are fed back into the deghosting algorithm, allowing it to adapt its processing based on the actual noise conditions in the data, thereby improving accuracy.
Solution Approach 2:
The system performs self-service by automatically estimating measurement noise characteristics from the seismic data itself, rather than requiring external calibration or manual noise characterization. This self-estimation capability enables improved accuracy without requiring additional complex external processing systems.
Data Source
AI summary
A technique includes receiving seismic data indicative of measurements acquired by seismic sensors. The measurements are associated with a measurement noise. The technique includes estimating at least one characteristic of the measurement noise and deghosting the seismic data based at least in part on the estimated characteristic(s) of the measurement noise.