Notional Source Signature Computation Using Weighted Near-Field Measurements
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Solution Overview
Problem
Accurate characterization of the source pressure wavefield in marine seismic surveys is hindered by noise contamination and errors in modeling, particularly due to cross-talk and uncertainties in calibration and input parameters.
Innovation Solution
A method for computing notional source signatures from measured near-field signatures and modeled notional signatures, where weights are assigned based on frequency reliability, allowing for quality control and sensitivity verification, and input parameter scaling to minimize differences between measured and modeled signatures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pressure measurements are taken within near fields of the source elements, then source pressure wavefield can be determined, but measurements are contaminated with noise caused by cross-talk and hydrophones picking up motion from other source elements
Solution Approach 1:
The patent segments the source array into individual source elements and processes their signatures separately. By computing notional signatures for each source element independently and then combining them, the method isolates the contribution of each element, enabling noise reduction through selective weighting of measured versus modeled components for each segment.
Solution Approach 2:
The patent introduces modeled notional signatures as an intermediary between the noisy measured near-field signatures and the final source pressure wavefield characterization. These modeled signatures serve as a clean reference that mediates the relationship between measurements and the desired far-field signature, allowing noise-contaminated measurements to be corrected through weighted combination.
2Measurement precision
If source pressure wavefield is modeled, then accurate characterization can be achieved, but errors occur due to calibration accuracy and assumptions made in modeling
Solution Approach 1:
The patent merges measured near-field signatures and modeled notional signatures through a weighted combination process. By integrating both measured and modeled components, the method combines the advantages of actual measurements with the theoretical accuracy of modeling, while mitigating the weaknesses of each approach through frequency-dependent weighting.
Solution Approach 2:
The patent applies parameter changes by introducing frequency-dependent weights that dynamically adjust the contribution of measured versus modeled signatures across different frequency ranges. This parameter adjustment allows the system to optimize reliability by relying more on measurements in frequency ranges where they are accurate and more on modeling where measurements are noisy.
3Measurement precision
If far-field measurements are used to calibrate models, then source pressure wavefield can be characterized, but measurements must be taken at large distances which increases complexity
Solution Approach 1:
The patent performs preliminary computation of notional signatures for each source element before combining them to produce the final far-field signature. This preliminary segmentation and individual processing allows the system to work with near-field measurements without requiring actual far-field measurement setups, thereby reducing measurement complexity while maintaining accuracy.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enhances the accuracy of source pressure wavefield characterization by combining reliable parts of measured and modeled signatures, reducing noise and calibration errors, and improving the reliability of seismic data processing.
Implementation Method 1
obtained from a pressure measurement at the jth pressure sensor
Implementation Method 2
produce acoustic impulses at selected times. Each impulse is a sound wave that travels down through the water and into the subterranean formation
Implementation Method 3
At each interface between different types of rock, a portion of the sound wave is refracted, a portion of the sound wave is transmitted, and another portion is reflected back toward the body of water to propagate toward the surface
Data Source
AI summary
Methods and systems for computing notional source signatures from modeled notional signatures and measured near-field signatures are described. Modeled near-field signatures are calculated from the modeled notional signatures. Low weights are assigned to parts of a source pressure wavefield spectrum where signatures are less reliable and higher weights are assigned to parts of the source pressure wavefield spectrum where signatures are more reliable. The part of the spectrum where both sets of signatures are reliable can be used for quality control and for comparing the measured near-field signatures to modeled near-field signatures. When there are uncertainties in the input parameters to the modeling, the input parameters can be scaled to minimize the differences between measured and modeled near-field signatures. Resultant near-field signatures are computed by a weighted summation of the modeled and measured near-field signatures, and notional source signatures are calculated from the resultant near-field signatures.