Hybrid Multi-Channel Prediction Operator for Shallow Water Multiple Removal
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Solution Overview
Problem
Current methods for removing shallow water multiples in seismic exploration are inadequate, especially in very shallow water environments where the seafloor reflections are indistinct or in the post-critical angle range, leading to difficulties in accurately determining the geological structure beneath the ocean floor.
Innovation Solution
A hybrid multi-channel prediction method that combines shallow water demultiple (SWD) and model-based water-layer de-multiple (MWD) techniques by estimating a prediction operator and generating a primary reflections model using seismic data, merging these with Green's function to create a hybrid prediction operator for effective multiple removal.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If conventional single-method demultiple techniques are used, then the processing is simple, but the signal-to-noise ratio of seafloor reflections deteriorates in very shallow water environments
Solution Approach 1:
The patent combines two distinct demultiple techniques (shallow water demultiple and model-based water-layer demultiple) into a hybrid method. The first technique estimates a prediction operator while the second generates primary reflections model using Green's function, and these are merged to create a comprehensive solution that maintains high signal-to-noise ratio in shallow water environments where conventional single methods fail
2Ease of operation
If shallow water demultiple technique is used alone, then the processing is straightforward, but accurate modeling of multiples is impossible when seafloor reflections are indistinct or in post-critical angle range
Solution Approach 1:
The patent introduces Green's function as an intermediary element that bridges the gap between the shallow water demultiple technique and model-based water-layer demultiple technique. Green's function enables the estimation of primary reflections model which serves as a mediator to improve multiple modeling accuracy when seafloor reflections are indistinct or in post-critical angle range
3Manufacturing precision
If model-based water-layer demultiple is used alone, then multiple modeling is improved, but the processing complexity increases
Solution Approach 1:
The patent segments the demultiple processing into two distinct stages: first estimating a prediction operator using shallow water demultiple technique, then generating primary reflections model using model-based water-layer demultiple with Green's function. This segmentation allows each technique to be applied in its optimal domain, improving multiple modeling accuracy while managing processing complexity through structured division of tasks
Data Source
AI summary
A system and method are provided for determining shallow water multiples when seismically exploring a geographical area of interest under a body of water. The system and method estimate a multi-channel prediction operator F using a model of water layer related multiples with respect to received and stored seismic data, estimate a travel time of the transmitted seismic wavelets from the one or more sources to each of the plurality of receivers, and then generate water layer primary reflections models using the estimated travel time and Green's function. The system and method then merge the generated water layer primary reflections models with the multi-channel prediction operator F to create a hybrid multi-channel prediction operator FH, and convolute the hybrid multi-channel prediction operator FH with the stored received data to determine a final multiples model.