Elastic Vector Reflectivity Inversion for Anisotropic Earth Models
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional Full Waveform Inversion (FWI) methods fail to capture anisotropic or angle-dependent scattering effects, limiting the resolution and fidelity of inverted models due to the focus on scalar physical parameters without explicitly modeling seismic vector reflectivity.
Innovation Solution
Incorporate seismic vector reflectivity into the FWI process by determining high and low spatial frequency components using an initial earth model, generating synthetic seismic data, and updating the earth model based on observed seismic data to enhance model accuracy and convergence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional FWI methods focus on scalar physical parameters (acoustic velocity or impedance), then the inversion process is simpler and faster, but the methods fail to capture anisotropic or angle-dependent scattering effects, limiting resolution and fidelity
Solution Approach 1:
The patent segments the reflectivity parameter into multiple components: acoustic reflectivity (scalar) and elastic impedance (vector), which captures both isotropic and anisotropic scattering effects. This segmentation allows the inversion to handle complex geological settings while maintaining computational tractability by processing different physical phenomena separately but combining them in the final model.
Solution Approach 2:
The patent transitions from scalar acoustic parameters to vector elastic parameters, adding a new dimension to the inversion problem. By incorporating elastic impedance (a vector quantity with magnitude and direction) alongside acoustic reflectivity, the method captures angle-dependent scattering effects that scalar parameters cannot represent, thereby improving resolution and fidelity.
2Productivity
If conventional FWI methods use scalar physical parameters, then the inversion is easier to formulate, but the rate of convergence is significantly slowed in complex geological settings
Solution Approach 1:
The patent changes the parameter formulation from scalar acoustic parameters to vector elastic parameters including elastic impedance. This parameter transformation improves the conditioning of the inverse problem, leading to faster convergence rates in complex geological settings. The vector parameterization provides better illumination of the solution space, allowing the inversion to converge more efficiently.
3Reliability
If conventional FWI methods do not explicitly model seismic vector reflectivity, then the inversion process is simpler, but important anisotropic or angle-dependent scattering effects are not captured
Solution Approach 1:
The patent creates a composite parameterization that combines acoustic reflectivity (scalar) and elastic impedance (vector) into a unified inversion framework. This composite approach allows the inversion to simultaneously capture both isotropic and anisotropic scattering effects, improving the reliability of inverted models while managing complexity through a structured combination of physical parameters.
Data Source
AI summary
A method for generating an earth model is disclosed. The method includes receiving input data. The input data includes an initial earth model and observed seismic data. The method also includes determining a high spatial frequency component and a low spatial frequency component based on the input data. The method further includes preparing a full bandwidth model based on the high spatial frequency component and the low spatial frequency component. The method also includes generating synthetic seismic data using the full bandwidth model. The method also includes generating the earth model based on the synthetic seismic data and the observed seismic data.