Transfer Learning for Rare Earth Deposit Localization
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Solution Overview
Problem
The complex morphology and uneven spatial distribution of igneous carbonate rock-type rare earth deposits pose challenges for effective geophysical exploration and refined resolution in geophysical data processing, making it difficult to accurately identify and locate these deposits.
Innovation Solution
A transfer learning-based method that comprehensively analyzes multi-source geophysical data by obtaining electric, magnetic, seismic, and gravity exploration data, performing feature decomposition, dimensionality reduction, feature enhancement, and weighted fusion processing to generate sectional images and segment rare earth mineralized geological bodies, thereby improving the accuracy of identification and positioning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional geophysical exploration methods are used to analyze igneous carbonate rock-type rare earth deposits, then the exploration process can be completed with conventional techniques, but the accuracy of identification and positioning is insufficient due to complex rock composition and uneven spatial distribution
Solution Approach 1:
The patent segments the complex geophysical data processing into multiple specialized modules: electric exploration module, magnetic exploration module, seismic exploration module, and gravity exploration module. Each module processes specific types of geophysical data independently, then the results are integrated through transfer learning to achieve accurate positioning of rare earth deposits while managing processing complexity
Solution Approach 2:
The patent introduces transfer learning as an intermediary technique that bridges the gap between conventional geophysical exploration methods and accurate deposit positioning. The transfer learning model learns from multi-source geophysical data and transfers knowledge to improve identification accuracy, acting as a mediator that enhances measurement precision without requiring complete redesign of the exploration system
2Measurement precision
If multi-source geophysical data is comprehensively analyzed using transfer learning, then the accuracy of rare earth deposit positioning is improved, but the complexity of data processing and analysis increases
Solution Approach 1:
The patent designs a universal transfer learning framework that can process multiple types of geophysical data (electric, magnetic, seismic, gravity) through a single integrated system. This multi-functional approach allows the same core processing architecture to handle diverse data sources, improving positioning accuracy while avoiding the need for separate complex processing systems for each data type
3Manufacturing precision
If feature decomposition, dimensionality reduction, and weighted fusion processing are performed on sectional images, then the resolution and detail of geological body identification is enhanced, but the computational time and processing resources increase
Solution Approach 1:
The patent performs dimensionality reduction and feature decomposition as preliminary actions before the main identification process. By pre-processing the sectional images to extract key features and reduce data dimensions in advance, the system enhances identification resolution while reducing the computational burden during the actual processing phase, thereby minimizing time loss
Data Source
AI summary
The present application proposes a method and apparatus for transfer learning-based localization of igneous carbonate-hosted rare earth mineralization. The technology relates to electromagnetic exploration and includes obtaining exploration data for electric, magnetic, seismic, and gravity methods in the target area. Transfer learning-based localization is applied to the electric, magnetic, seismic, and gravity exploration data to determine the cross-sectional map corresponding to the anomalous position of igneous carbonate-hosted rare earth mineralization in the target area. Feature decomposition dimensionality reduction, feature enhancement, and weighted fusion processing are applied to the cross-sectional map, followed by segmentation of the igneous carbonate-hosted rare earth mineralization geological body in the fused image to obtain the spatial distribution of the detection target. The integrated analysis of multi-source geophysical data using images improves the accuracy of identification and localization of igneous carbonate-hosted rare earth mineralization.