Seismic Multiple Suppression via Frequency-Wavenumber Segmentation
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Solution Overview
Problem
Conventional methods for suppressing seismic multiple reflection signals in land-based seismic data are inadequate due to data smearing caused by low signal-to-noise ratios and unpredictable multiple events, which obscure primary reflections and complicate data interpretation.
Innovation Solution
A method that separates random noise and primary reflection signals using frequency-wavenumber domain techniques, followed by localized parabolic path summation to model multiple reflection signals without transforming the entire data set, thereby maintaining the original data characteristics and reducing smearing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If Radon transform-based methods are used to suppress multiple reflection signals, then multiple suppression capability is improved, but data smearing occurs and primary reflection characteristics are degraded
Solution Approach 1:
The patent segments the seismic data processing into distinct stages: first separating random noise from primary reflections using frequency-wavenumber filtering, then modeling multiples only on the cleaned data. This segmentation allows each processing stage to focus on specific components, avoiding the data smearing that occurs when all components are processed simultaneously by Radon transforms.
Solution Approach 2:
The patent performs preliminary separation of random noise and primary reflections before attempting to model and suppress multiples. By removing interfering components first, the subsequent multiple modeling operates on cleaner data with better signal-to-noise ratio, preventing the degradation of primary reflection characteristics that occurs when multiples are modeled directly from noisy data.
2Productivity
If full data transform is used to convert seismic data to another domain, then comprehensive data processing is achieved, but noise energy excessive problem makes it difficult to mute only multiples
Solution Approach 1:
The patent extracts and removes random noise and primary reflections from the full seismic data using frequency-wavenumber filtering before modeling multiples. This extraction process isolates the multiple components by removing other signal components first, enabling selective multiple suppression without the difficulty of separating multiples from excessive noise in a full data transform approach.
Solution Approach 2:
The patent transitions from processing data in the time-space domain to the frequency-wavenumber domain for noise and primary separation, then back to time domain for multiple modeling. This dimensional transformation enables effective separation of different signal components based on their distinct frequency-wavenumber characteristics, solving the isolation problem that plagues single-domain processing.
3Speed
If conventional methods process land-based seismic data with low signal-to-noise ratio, then processing speed is maintained, but unpredictable multiple events cause data smearing
Solution Approach 1:
The patent applies localized processing windows to segments of the seismic data, allowing the multiple modeling to adapt to local variations in signal characteristics. This local quality approach enables the processing to handle unpredictable multiple events in different regions of the data without causing widespread smearing, while maintaining efficient processing speed through parallel operation on segmented data.
Data Source
AI summary
A method of modeling seismic wave-field data in order to suppress near-surface and sub-surface related multiple reflection signals is provided. The reflection signals include main primary reflection signals, main random noise signals, main multiple reflection signals, residual primary reflection signals, residual random noise signals, and residual multiple reflection signals. Main random noise signals are separated from the reflection signals using a frequency-wavenumber domain method to provide data having suppressed main random noise. Main primary reflection signals are separated from the data having suppressed main random noise using frequency-wavenumber filtering and weighted median filtering to provide data having suppressed main random noise and main primary reflections. Multiple reflection signals are modeled using parabolic path summation on the data having suppressed main random noise and main primary reflections.