View source for Tibet Julong Copper Co., Ltd. (20240310554). METHOD FOR METALLOGENIC PREDICTION BY USING MULTI-SOURCE HETEROGENEOUS INFORMATION simplified abstract

=METHOD FOR METALLOGENIC PREDICTION BY USING MULTI-SOURCE HETEROGENEOUS INFORMATION=


==Organization Name==

[[:Category:Tibet Julong Copper Co., Ltd.|Tibet Julong Copper Co., Ltd.]]


[[Category:Tibet Julong Copper Co., Ltd.]]

==Inventor(s)==

[[:Category:Youye Zheng of Beijing (CN)|Youye Zheng of Beijing (CN)]][[Category:Youye Zheng of Beijing (CN)]]

[[:Category:Song Wu of Beijing (CN)|Song Wu of Beijing (CN)]][[Category:Song Wu of Beijing (CN)]]

[[:Category:Hongjun Cheng of Beijing (CN)|Hongjun Cheng of Beijing (CN)]][[Category:Hongjun Cheng of Beijing (CN)]]

[[:Category:Xiaofang Dou of Beijing (CN)|Xiaofang Dou of Beijing (CN)]][[Category:Xiaofang Dou of Beijing (CN)]]

[[:Category:Feng Gao of Beijing (CN)|Feng Gao of Beijing (CN)]][[Category:Feng Gao of Beijing (CN)]]

[[:Category:Shucun Wang of Beijing (CN)|Shucun Wang of Beijing (CN)]][[Category:Shucun Wang of Beijing (CN)]]

[[:Category:Defu Shu of Beijing (CN)|Defu Shu of Beijing (CN)]][[Category:Defu Shu of Beijing (CN)]]

[[:Category:Jiancuo Luosang of Beijing (CN)|Jiancuo Luosang of Beijing (CN)]][[Category:Jiancuo Luosang of Beijing (CN)]]

[[:Category:Jiangang Wei of Beijing (CN)|Jiangang Wei of Beijing (CN)]][[Category:Jiangang Wei of Beijing (CN)]]

[[:Category:Zhuoga Suolang of Beijing (CN)|Zhuoga Suolang of Beijing (CN)]][[Category:Zhuoga Suolang of Beijing (CN)]]

==METHOD FOR METALLOGENIC PREDICTION BY USING MULTI-SOURCE HETEROGENEOUS INFORMATION - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240310554 titled 'METHOD FOR METALLOGENIC PREDICTION BY USING MULTI-SOURCE HETEROGENEOUS INFORMATION

The patent application describes a method for metallogenic prediction using multi-source heterogeneous information. 

* Collect geological, geochemical, and remote sensing data
* Build a conceptual model of a metallogenic system
* Extract spatial proxy mineralization indication information
* Integrate and train data with a neural network
* Apply machine learning algorithm for hyper-parameter optimization
* Complete machine learning result evaluation and target area delineation

Key Features and Innovation:
- Utilizes multi-source geoscience information for metallogenic prediction
- Integrates neural network and machine learning algorithms for accurate results
- Provides a quick operation speed and reliable delineation of target areas

Potential Applications:
- Mineral exploration and resource assessment
- Environmental monitoring and land management
- Geological survey and mapping

Problems Solved:
- Limited availability of data for accurate metallogenic prediction
- Time-consuming and labor-intensive traditional methods
- Uncertainty in target area delineation

Benefits:
- Efficient and accurate metallogenic prediction
- Cost-effective exploration and resource management
- Improved decision-making in mineral exploration projects

Commercial Applications:
- "Metallogenic Prediction Method Using Multi-source Heterogeneous Information" can be used in the mining industry for identifying potential mineral deposits and optimizing exploration efforts. This technology can also be applied in environmental monitoring and land management for sustainable resource development.

Prior Art:
- Researchers and geoscientists have used various methods for mineral exploration and metallogenic prediction, including geological mapping, geochemical analysis, and remote sensing techniques. However, the integration of multi-source heterogeneous information with machine learning algorithms is a novel approach in this field.

Frequently Updated Research:
- Ongoing research in the field of geoscience and machine learning is continuously improving the accuracy and efficiency of metallogenic prediction methods. Stay updated on the latest advancements in this technology for optimal results.

Questions about Metallogenic Prediction Using Multi-source Heterogeneous Information:

1. How does this method compare to traditional metallogenic prediction techniques?
- This method outperforms traditional techniques by integrating multi-source geoscience information and machine learning algorithms for more accurate and efficient results.

2. What are the potential limitations of using multi-source heterogeneous information for metallogenic prediction?
- Some limitations may include data integration challenges, the need for advanced computational resources, and the complexity of optimizing machine learning models for specific geological settings.


==Original Abstract Submitted==

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.



[[Category:G01V99/00]]
[[Category:G06N3/045]]
[[Category:G06N3/08]]
[[Category:CPC_G01V20/00]]