Seismic Source Signature Deconvolution via Angle-Dependent Filtering
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Solution Overview
Problem
The strong variation of bubble-tuned seismic source signatures with vertical polar angle complicates source signature deconvolution, leading to noise issues and precursor energy at higher offsets, making existing deconvolution methods economically unviable for a wide range of polar angles.
Innovation Solution
A method using a frequency-dependent deconvolution filter weighted by a semblance term, derived from far-field source signature signals at multiple polar angles, to efficiently remove source signature effects from seismic data, applicable in the time or frequency domain, and optimized for marine seismic environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If bubble-tuned source signatures are used to enhance primary pulse, then signal strength is improved, but variation with vertical polar angle increases causing noise and precursor energy at higher offsets
Solution Approach 1:
The patent applies dynamics by making the deconvolution filter angle-dependent rather than using a single fixed filter. The filter characteristics are dynamically adjusted based on the vertical polar angle of the seismic signal, allowing optimal deconvolution performance across different angles. This resolves the contradiction by adapting the processing parameters to the specific signal conditions at each angle, preventing noise and precursor energy issues while maintaining signal strength enhancement.
Solution Approach 2:
The patent changes the parameters of the deconvolution filter based on the vertical polar angle. Different filter characteristics are applied for different angular ranges, transforming the deconvolution process from a static one-parameter approach to a multi-parameter approach that accounts for angular variations. This resolves the technical contradiction by optimizing the filter parameters for each angular condition, thereby maintaining both signal strength and deconvolution accuracy.
2Device complexity
If existing deconvolution methods are applied to bubble-tuned sources, then processing is simplified, but noise and precursor energy problems occur at higher offsets
Solution Approach 1:
The patent applies local quality by treating different angular regions differently. Instead of applying a uniform deconvolution filter to all signals, the method identifies local angular characteristics and applies appropriate filter characteristics to each region. This resolves the contradiction by preventing noise and precursor energy at higher offsets through angle-specific filtering, while maintaining relative processing simplicity through automated angular classification and filter selection.
3Productivity
If angle-independent deconvolution is used, then processing is faster and simpler, but performance deteriorates for signals at multiple polar angles
Solution Approach 1:
The patent applies segmentation by dividing the angular range into multiple segments or bins. The deconvolution process is segmented into angle-specific processing paths, where each segment corresponds to a particular vertical polar angle range. This resolves the contradiction by maintaining processing efficiency through automated segmentation while achieving superior angular coverage through tailored filter characteristics for each segment.
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 effectively reduces noise and precursor energy, providing a more robust and economically viable deconvolution process for a wider range of polar angles, improving seismic data interpretation without requiring extensive additional processing.
Implementation Method 1
the elasticity of the air couples with the inertial mass of the surrounding water to produce an oscillating system as the air expands and contracts in size
Implementation Method 2
the elasticity of the air couples with the inertial mass of the surrounding water to produce an oscillating system
Implementation Method 3
These bubble oscillations generate spherical sound waves which form the seismic signal
Implementation Method 4
A method using a frequency-dependent deconvolution filter weighted by a semblance term, derived from far-field source signature signals at multiple polar angles, to efficiently remove source signature effects from seismic data
Data Source
AI summary
A method of filtering seismic signals is described using the steps of obtaining the seismic signals generated by activating a seismic source and recording signals emanating from the source at one or more receivers; defining a source signature deconvolution filter to filter the seismic signal, wherein the filter is scaled by a frequency-dependent term based on an estimate of the signal-to-noise (S/N) based on the spectral power of a signal common to a suite of angle-dependent far-field signatures normalized by the total spectral power of the signatures within the angular suite and performing a source signature deconvolution using the source signature deconvolution filter.