Multi-source Heterogeneous Data Integration for Mineral Exploration
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Solution Overview
Problem
Traditional methods for delineating prospecting target areas in mineral exploration have low accuracy and poor adaptability due to the integration of multi-source heterogeneous data, leading to unclear deposit types and unreliable target area delineation.
Innovation Solution
A method using multi-source heterogeneous information, including geological, geochemical, and remote sensing data, integrated through a neural network and machine learning algorithms to build a conceptual model of a metallogenic system, extract spatial proxy mineralization information, and optimize hyper-parameters for accurate target area delineation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional single exploration means are used, then the ecological environment is protected from unnecessary damage, but the reliability of prospecting target area delineation is low
Solution Approach 1:
The patent performs preliminary data integration and processing before field verification. By integrating multi-source heterogeneous data (geological, geochemical, remote sensing) and using spatial superposition technology to pre-delineate prospecting target areas, the method identifies high-potential zones in advance, allowing engineers to focus verification efforts only on these predetermined areas, thereby avoiding unnecessary ecological damage from broad-scale engineering verification while maintaining high reliability through comprehensive data analysis
Solution Approach 2:
The patent introduces an information integration system as an intermediary between data collection and field verification. This system processes and synthesizes multi-source heterogeneous data to generate reliable prospecting target area delineations, serving as a mediator that reduces the need for direct ecological intervention while maintaining high prediction reliability through comprehensive data analysis
2Productivity
If spatial superposition integration technology is used to integrate multi-source data, then the delineation speed is improved, but the adaptability and prediction capability are poor due to different data distributions in different areas
Solution Approach 1:
The patent applies local quality by adjusting the spatial superposition integration parameters according to different regional characteristics. The system recognizes that different areas have different data distributions and applies localized weighting and integration strategies tailored to each region's specific geological and geochemical characteristics, thereby maintaining high adaptability while preserving fast delineation speed through automated local parameter optimization
Solution Approach 2:
The patent introduces dynamic adaptability into the spatial superposition integration technology by making the integration parameters adjustable and region-specific. The system dynamically adapts to different data distributions in different areas by allowing parameter modification based on local characteristics, transforming a static integration method into a dynamic one that can respond to varying regional conditions while maintaining efficient delineation
3Device complexity
If multi-element comprehensive anomaly maps are used for geochemical data integration, then the processing is simplified, but the deposit type identification and mineralization strength indication are unclear
Solution Approach 1:
The patent segments the geochemical anomaly data processing into multiple distinct components rather than using a single comprehensive anomaly map. It separates deposit type identification, mineralization strength assessment, and spatial distribution analysis into independent processing streams, each optimized for its specific purpose. This segmentation maintains relatively simple processing while significantly improving measurement precision by providing detailed, specialized information for each analytical goal
4Loss of time
If traditional methods integrate geological, geochemical, and remote sensing data, then the target area can be delineated quickly, but the accuracy and reliability of delineation are low
Solution Approach 1:
The patent creates a composite information model by integrating geological, geochemical, and remote sensing data through spatial superposition technology. This composite approach combines multiple data sources with different strengths to achieve both rapid delineation and high accuracy. The synergistic integration of heterogeneous data types compensates for individual data source limitations, enabling fast target area identification with improved reliability and precision
Data Source
AI summary
A method for metallogenic prediction by using multi-source heterogeneous information, includes the following steps: collecting geological, geochemical and remote sensing multi-source geoscience information data; building a conceptual model of a metallogenic system; extracting geoscience multi-source spatial proxy mineralization indication information according to the conceptual model of the metallogenic system; integrating and training data based on a neural network to obtain multi-dimensional spatial proxy layer data sets and training points; inputting the multi-dimensional spatial proxy layer data sets and the training points, and applying a machine learning algorithm for hyper-parameter optimization to obtain an optimized machine learning model; and applying the optimized machine learning model to complete machine learning result evaluation and target area delineation. The present disclosure has the advantages of a small amount of required data, a quick operation speed, and a small and reliable delineation range of a target area.