Joint Minimum Entropy Method for Multi-Physics Data Fusion
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Solution Overview
Problem
Existing methods struggle to effectively fuse and process multi-physics data from different sensors, lacking a unified approach for integrating data from sensors with varying physical properties, which is crucial for applications like medical imaging, remote sensing, and geological exploration.
Innovation Solution
A joint minimum entropy method is employed to calculate a nonnegative joint entropy functional as a weighted average of model parameters, using smoothing or focusing stabilizing functionals to enforce consistency and sharp boundaries, and solve a minimization problem with linear or nonlinear optimization to achieve simultaneous imaging and data fusion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If multiple multi-physics sensors are used to capture data from different physical properties, then the quantity and diversity of information about the target is improved, but the complexity of data processing and fusion increases
Solution Approach 1:
The patent combines multiple multi-physics datasets into a single joint inversion framework, merging seismic, gravity, magnetic, and other physical property data to simultaneously image subsurface structures and physical properties, thereby reducing processing complexity while maintaining information completeness
Solution Approach 2:
The joint inversion method serves multiple functions simultaneously: it images subsurface geometry, determines physical properties (density, magnetic susceptibility, electrical conductivity), and integrates diverse sensor data types through a unified mathematical framework, eliminating the need for separate processing pipelines
2Measurement precision
If joint inversion of multiple datasets is performed to image both geometry and physical properties, then the accuracy of subsurface characterization is improved, but the computational cost and processing time increase
Solution Approach 1:
The patent applies preliminary regularization constraints and prior geological models to guide the joint inversion process, pre-defining acceptable ranges for physical properties and structural configurations to reduce the solution space and accelerate convergence without sacrificing accuracy
Solution Approach 2:
The method iteratively adjusts inversion parameters including regularization weights, damping factors, and physical property bounds during the processing sequence, optimizing computational efficiency at different stages while maintaining high accuracy in the final subsurface model
3Ease of operation
If separate processing of different sensor data is performed, then the simplicity of individual processing workflows is maintained, but the consistency and integration of multi-physics information deteriorates
Solution Approach 1:
The patent merges multiple datasets into a single objective function with unified regularization constraints, ensuring that seismic, gravity, magnetic, and other data types are processed together to maintain consistency in subsurface geometry and physical properties across all derived models
Data Source
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AI summary
A method for the simultaneous imaging of different physical properties of an examined medium from multi-physics datasets and for digital enhancement and restoration of multiple multidimensional digital images is described. The method introduces nonnegative joint entropy determined as a joint weighted average of the logarithm of the corresponding density of the model parameters and/or images and/or their attributes. The joint entropy measures are introduced as additional constraints, and their minimization results in enforcing of the order and consistency between the different model parameters and/or multiple images and/or their transforms. The method does not require a priori knowledge about specific physical, or analytical, or empirical, or statistical relationships between the different model parameters and/or multiple images and their attributes, nor does the method require a priori knowledge about specific geometric or structural relationships between different model parameters, images, and/or their attributes.