Directional Oriented Wavefield Imaging for Seismic Subsurface Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional reverse-time migration (RTM) techniques face challenges in generating accurate subsurface images due to low frequency artifacts and irregular acquisition geometries, leading to contaminated signals and amplitude artifacts, especially in complex velocity models.
Innovation Solution
The directional oriented wavefield imaging (DOWFI) algorithm employs a spatially variable weighting function and optical flow methodology to decompose wavefields and compute subsurface azimuth and reflection angles, compensating for irregular illumination and complex wave propagation effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional reverse-time migration techniques are used, then subsurface images can be generated, but low frequency artifacts and amplitude artifacts contaminate the signals reducing image quality
Solution Approach 1:
The wavefield is decomposed into multiple components using spectral decomposition techniques, separating the useful signal from low frequency artifacts. This segmentation allows selective processing of different frequency components to eliminate artifacts while preserving image quality.
Solution Approach 2:
The imaging process applies spatially varying filters and weighting functions that adapt to local geological conditions. This local quality approach allows optimal artifact suppression and signal enhancement at different locations in the subsurface model.
2Measurement precision
If conventional RTM techniques are used, then subsurface images can be generated, but irregular acquisition geometries lead to irregular illumination and complex wave propagation effects
Solution Approach 1:
The imaging methodology uses dynamic ray tracing and time-dependent weighting functions that adapt to irregular acquisition geometries. The system dynamically adjusts the illumination compensation based on the actual sensor positions and wave propagation paths, enabling accurate imaging despite geometric irregularities.
Solution Approach 2:
The method transforms the imaging problem by changing parameters from fixed-grid conventional methods to continuous spatial and temporal domains. This allows flexible handling of irregular geometries through parameterized wavefield representations and adaptive sampling strategies.
3Manufacturing precision
If conventional RTM techniques are used, then subsurface images can be generated, but complex velocity models result in contaminated signals and reduced resolution
Solution Approach 1:
The methodology performs preliminary wavefield decomposition and artifact filtering before the final imaging step. By preprocessing the wavefield data to remove low frequency artifacts and correct for velocity model complexities, the subsequent imaging produces higher resolution results without contamination.
Solution Approach 2:
The patent introduces intermediate processing steps including spectral decomposition, angle-domain common image gather extraction, and illumination compensation as mediators between the raw wavefield data and final images. These intermediaries filter out artifacts caused by complex velocity models while preserving structural information.
Data Source
Figure 1
Figure 2~3
Figure 4A
AI summary
The present disclosure describes methods and systems, including computer-implemented methods, computer program products, and computer systems, for generating geophysical images. One computer-implemented method includes receiving a set of seismic data associated with a subsurface region; generating source analytic wavefields and receiver analytic wavefields based on the set of seismic data; decomposing the source analytic wavefields and receiver analytic wavefields; computing directions of propagations for the source analytic wavefields and receiver analytic wavefields; computing, for a plurality of subsurface points, an azimuth angle and a reflection angle for a respective subsurface point based on the directions of propagations; generating for each of the plurality of subsurface points, a weighting function for a respective subsurface point based on the azimuth angle and the reflection angle of the respective subsurface point; and generating a subsurface image using the weighting functions of the plurality of subsurface points.