Ray-Based Moment Tensor Imaging for Seismic Source Accuracy
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Solution Overview
Problem
Current methods for seismic exploration and microseismic event analysis, such as diffraction stack migration and interferometric source imaging, face challenges in accurately determining source locations and moment tensor elements due to complications from moment tensor sources, which cause amplitude and polarity variations, leading to distorted images and low stack amplitudes at true source positions.
Innovation Solution
A method utilizing ray-based moment tensor imaging, which projects seismic data onto moment tensor components using ray-based Green's functions, allowing for coherent summation of data at the source position and incorporating multiple propagation modes, thereby improving the detection of smaller magnitude events and maintaining computational efficiency comparable to diffraction stack procedures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If diffraction stack migration or interferometric source imaging is used, then source location can be determined, but image distortion occurs and stack amplitudes are low at true source positions due to moment tensor source complications
Solution Approach 1:
The patent changes the mathematical parameters of the imaging approach by using ray-based Green's functions that explicitly account for moment tensor source characteristics. This involves transforming the imaging equations to incorporate moment tensor radiation patterns, allowing proper handling of amplitude and polarity variations without image distortion
Solution Approach 2:
The patent introduces ray-based Green's functions as an intermediary mathematical tool that mediates between the raw seismic data and the final image. These Green's functions serve as transfer functions that properly propagate the moment tensor source effects through the medium, enabling accurate source location determination while maintaining image quality
2Measurement precision
If moment tensor imaging is implemented to improve source characterization, then detection of smaller magnitude events improves, but computational complexity increases
Solution Approach 1:
The patent segments the moment tensor imaging process into distinct computational components: ray tracing to compute Green's functions, data projection onto moment tensor components, and coherent summation. This segmentation allows each component to be optimized independently and facilitates parallel implementation, reducing overall computational complexity
Solution Approach 2:
The patent replaces traditional full-waveform moment tensor inversion methods with a ray-based approach. This substitution uses ray theory approximations instead of solving the complete elastic wave equation, significantly reducing computational complexity while maintaining the ability to detect smaller magnitude events through coherent summation
Data Source
AI summary
A method for estimating moment tensor components of a source can use a waveform based method that performs a stack function on projected data from each sensor. The method can produce a cumulative result for each moment tensor component, for each propagation phase at each image point and origin time. A system for use in imaging a subterranean region of interest can include instructions which operate on recorded actual seismic data output from seismic receivers having known positions, and at least one processor, whereby the instructions cause the processor to project a selected data sample from the seismic data onto a moment tensor component.