Automated Mineral Classification Using Partial Spectral Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing mineral identification systems using x-ray spectroscopy require expert users and extensive time for manual data collection and rule formulation, limiting the development of automated and user-friendly mineral classification.
Innovation Solution
Combining a rules-based approach with a similarity metric approach to automate mineral identification, allowing for efficient classification of minerals without human intervention by eliminating irrelevant data points and using pre-determined mineral definitions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If EDS systems are used to collect x-ray spectra for mineral identification, then spectral resolution sufficient to uniquely identify minerals is achieved, but acquisition time becomes excessively long (several seconds per pixel)
Solution Approach 1:
The patent applies partial action by collecting x-ray spectral data at reduced acquisition times (less than several seconds per pixel) and using statistical methods to process the partial data. Instead of requiring complete spectral data collection, the system uses machine learning algorithms to identify minerals from incomplete or partially collected spectral information, thereby reducing acquisition time while maintaining identification accuracy.
Solution Approach 2:
The patent employs preliminary action through pre-training machine learning models using extensive spectral data collected beforehand. The system pre-processes and stores spectral signatures of known minerals in databases, allowing rapid comparison and identification during actual analysis without requiring lengthy real-time spectral collection and processing.
2Reliability
If both BSE and x-ray spectra are collected to accurately identify minerals, then identification accuracy is improved, but total acquisition time increases significantly
Solution Approach 1:
The patent merges BSE imaging and x-ray spectral data collection into a coordinated process. The system synchronizes BSE signal acquisition with x-ray spectral acquisition, allowing both data types to be collected simultaneously or in a coordinated sequence rather than separately. This integration reduces total acquisition time while maintaining the complementary information needed for accurate mineral identification.
Solution Approach 2:
The system uses partial data collection strategies where BSE images provide rapid initial mineral discrimination, and x-ray spectra are collected only for regions of interest identified by the BSE data. This selective approach reduces the total amount of spectral data collection needed while maintaining identification accuracy through the combined information from both techniques.
3Measurement precision
If manual rule formulation and data collection are used for mineral identification, then expert-level accuracy is achieved, but the system requires extensive user training and time investment
Solution Approach 1:
The patent implements self-service through automated machine learning models that perform mineral identification without requiring expert user intervention. The system automatically collects data, processes spectra, compares results against trained models, and generates mineral identification reports. This automation eliminates the need for users to manually formulate rules or interpret complex spectral data, making the system accessible to untrained operators while maintaining expert-level accuracy.
Solution Approach 2:
The patent replaces the mechanical system of manual rule formulation and expert analysis with an automated computational system using machine learning algorithms. The system substitutes human expert knowledge with pre-trained computational models that automatically process spectral data and provide mineral identifications, thereby eliminating the need for user training while preserving identification accuracy.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This combination significantly reduces analysis time, increases robustness, and enables untrained operators to use the system, making mineral identification more efficient and accessible.
Implementation Method 1
The incident beam may excite an electron in an inner shell, ejecting it from the shell while creating an electron hole where the electron was
Implementation Method 2
An electron from an outer, higher-energy shell then fills the hole, and the difference in energy between the higher-energy shell and the lower energy shell may be released in the form of an X-ray
Implementation Method 3
Back-scattered electrons (BSE) are electrons from the primary electron beam that are reflected from the sample by elastic or inelastic scattering
Implementation Method 4
Back-scattered electrons (BSE) are electrons from the primary electron beam that are reflected from the sample by elastic or inelastic scattering
Implementation Method 5
The number and energy of the X-rays emitted from a specimen can be measured by an x-ray spectrometer, such as an EDS or a wavelength dispersive spectrometer, to determine the elemental composition of the specimen
Data Source
AI summary
The present invention discloses a combination of two existing approaches for mineral analysis and makes use of the Similarity Metric Invention, that allows mineral definitions to be described in theoretical compositional terms, meaning that users are not required to find examples of each mineral, or adjust rules. This system allows untrained operators to use it, as opposed to previous systems, which required extensive training and expertise.