Internal Multiple Estimation in Reflection Seismology
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Solution Overview
Problem
Current seismic data processing methods face challenges in effectively attenuating internal multiples, which interfere with the interpretation of subsurface structures, especially in areas with complex geology, as existing techniques often require prior velocity information and are computationally intensive.
Innovation Solution
The implementation of the Extended Internal Multiple Prediction (XIMP) method, which uses a data-driven approach to predict and remove internal multiples at true azimuth without prior velocity information, combining adaptive subtraction techniques with curvelet transforms to enhance primary energy representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional seismic data processing methods are used to attenuate internal multiples, then prior velocity information is required, but this increases the complexity of data processing and limits applicability in areas with complex geology
Solution Approach 1:
The method uses the seismic data itself to predict internal multiples without requiring external velocity information. The prediction is performed by correlating the seismic data with itself at different time lags, allowing the data to serve its own processing needs and eliminating the need for additional velocity models or prior information.
Solution Approach 2:
The method extracts internal multiple predictions from the seismic data by identifying and isolating the multiple reflection components through autocorrelation techniques. This extracted multiple prediction is then subtracted from the original seismic data to produce multiple-attenuated results, separating the harmful internal multiples from the useful primary reflections.
2Productivity
If conventional seismic data processing methods are used to attenuate internal multiples, then computational intensity increases, but this reduces processing efficiency and increases costs
Solution Approach 1:
The method applies autocorrelation only to specific time windows and depth ranges where internal multiples are expected to occur, rather than processing the entire seismic dataset uniformly. This partial application of the processing technique reduces computational demand while maintaining effectiveness in attenuating the most problematic internal multiple events.
3Measurement precision
If internal multiples are not attenuated, then interpretation of subsurface structures is compromised, but existing techniques require prior velocity information that is often unavailable
Solution Approach 1:
The method uses the seismic data itself to predict internal multiples without requiring external velocity information. The prediction is performed by correlating the seismic data with itself at different time lags, allowing the data to serve its own processing needs and eliminating the need for additional velocity models or prior information.
Data Source
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AI summary
A method includes receiving seismic data of a seismic survey; defining a two-dimensional domain in dimensions x and y; identifying a target trace (S, R) of the seismic survey where S represents a source at (xs, ys) and where R represents a receiver at (XR, yR); defining with respect to the two-dimensional domain, a source trace (S, X1) as a primary trace, a receiver trace (R, X2) as a primary trace, and a generator trace (X1, X2) as associated with an interbed multiple generator; convolving the primary traces and crosscorrelating with the generator trace for a plurality of different (X1, X2) pairs where each of the plurality of (X1, X2) pairs defines a line segment where the line segments are substantially parallel to one another; and, based at least in part on the convolving the primary traces and crosscorrelating with the generator trace, generating seismic data with attenuated multiple energy.