Seismic True Estimated Wavelet for Source Separation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current seismic survey methods face challenges in accurately separating seismic signals from multiple sources due to the use of similar seismic sources with non-distinctive characteristics, leading to imperfect source separation and poor data quality, especially when using vibrators or airgun arrays, as the true source signal is not precisely known.
Innovation Solution
A method is developed to create a 'Source True Estimated Wavelet' by identifying an initial source wavelet, considering the geometric relationship between seismic sources and receivers, and applying a computed earth response to enhance the accuracy of seismic data processing, including inversion, separation, and deconvolution, thereby improving source separation and data quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If similar seismic sources are used throughout a survey, then operational efficiency is improved, but source separation accuracy deteriorates
Solution Approach 1:
The patent applies local quality by creating individual source wavelet models for each specific source location and receiver pair, rather than using a generic uniform model. Each source wavelet is locally adapted to account for specific geological conditions, source characteristics, and geometric relationships, thereby maintaining source distinguishability even when similar source types are used throughout the survey.
Solution Approach 2:
The patent changes parameters by computing source wavelets with varying characteristics including frequency content, amplitude, phase, and temporal duration based on specific source-receiver geometries and earth models. This parameter variation allows similar sources to produce distinguishable signals tailored to local conditions, resolving the contradiction between operational efficiency and source separation accuracy.
2Device complexity
If ground force estimate is used as proxy for true source signal, then processing complexity is reduced, but separation fidelity deteriorates
Solution Approach 1:
The patent applies preliminary action by computing accurate source wavelet models before the source separation process. These pre-computed wavelets incorporate earth filtering effects and geometric relationships, providing high-fidelity reference signals that improve separation accuracy without adding complexity during the actual separation operation.
Solution Approach 2:
The patent introduces an intermediary element - the computed source wavelet model - that mediates between the simple ground force estimate and the complex true source signal. This intermediary wavelet model captures the essential characteristics of the true source signal while remaining computationally tractable, thereby improving separation fidelity without proportionally increasing processing complexity.
3Speed
If earth filtering effects are not accounted for, then processing speed is improved, but wavelet accuracy deteriorates
Solution Approach 1:
The patent computes earth filtering responses and applies them to source wavelets in advance, before the source separation step. This preliminary computation of filtered wavelets ensures accuracy is built into the reference signals, while the actual separation process operates efficiently without needing to repeatedly model earth filtering effects.
Data Source
AI summary
The invention relates to processing seismic data that includes signals from at least two sources and typically three or four sources where source separation is necessary for geophysical analysis. Specifically, the present invention is a process for correcting data prior to inversion where the correction is provided to correct for the filtering effect of the earth. The earth is a non-homogenous seismic propagator that causes distortions of wavelets of seismic energy related to the source and receiver azimuth and offset that makes the identification of source specific data within the composite data harder to identify. Computing an earth response and correcting for the effects of the earth on the wavelets provides for more resolution and more clarity in the resulting data and better geophysical interpretation.