Seismic Imaging With Velocity Gradient Conditioning for Subsurface Accuracy
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Solution Overview
Problem
Existing seismic data processing methods struggle to accurately model subsurface structures due to issues like near-surface heterogeneity and coverage gaps, leading to false structural anomalies and incomplete illumination of subsurface regions.
Innovation Solution
A method and system are developed to generate synthetic seismic data using a wavelet and velocity model, apply a gradient conditioner, and perform iterative inversion to enhance the accuracy of subsurface modeling, addressing issues of near-surface heterogeneity and coverage gaps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional seismic data processing methods are used, then the processing workflow is simple, but the imaging accuracy and resolution of subsurface structures deteriorate due to near-surface heterogeneity and coverage gaps
Solution Approach 1:
The inversion process is divided into multiple iterations, with each iteration focusing on specific frequency bands or parameter ranges. The velocity model is updated incrementally through successive iterations, allowing complex subsurface imaging to be broken down into manageable computational steps that progressively improve accuracy
Solution Approach 2:
A gradient conditioner is applied to the velocity model before performing the inversion process. This preliminary conditioning of the velocity gradient information prepares the data for more effective inversion by pre-processing the gradient to account for near-surface heterogeneity and illumination variations, thereby improving final imaging accuracy
2Reliability
If conventional inversion processes are used, then the computational workload is manageable, but the illumination of subsurface regions is incomplete leading to false structural anomalies
Solution Approach 1:
The inversion process uses feedback from the misfit between observed and synthetic seismic data to iteratively update the velocity model. The gradient conditioner provides feedback about illumination quality, allowing the algorithm to adjust and improve subsurface imaging by identifying and correcting false structural anomalies through successive iterations
Solution Approach 2:
The inversion process systematically changes parameters including frequency bands, velocity model parameters, and gradient conditioning factors across multiple iterations. By varying these parameters progressively, the method achieves more complete subsurface illumination and higher structural accuracy while managing computational workload through controlled parameter evolution
Data Source
AI summary
A method can include performing a simulation to generate synthetic seismic data using a wavelet and a velocity model of a subsurface geologic region, where the velocity model includes representations of subsurface structural features and associated geophysical parameters; generating a velocity gradient conditioner for the velocity model based at least in part on the synthetic seismic data; performing an iterative inversion process using acquired seismic data for the subsurface geologic region, the velocity model, and the velocity gradient conditioner; and outputting a final velocity model of the subsurface geologic region as a result of the iterative inversion process.