Approach for generating high accuracy machine learning model for high resolution geochemical map completion using remote sensing data: case study of Arizona, USA

Chenhui Huang; Akinobu Shibuya

doi:10.1117/12.2524940

3 October 2019 Approach for generating high accuracy machine learning model for high resolution geochemical map completion using remote sensing data: case study of Arizona, USA

Chenhui Huang, Akinobu Shibuya

Author Affiliations +

Proceedings Volume 11156, Earth Resources and Environmental Remote Sensing/GIS Applications X; 111560F (2019) https://doi.org/10.1117/12.2524940
Event: SPIE Remote Sensing, 2019, Strasbourg, France

Abstract

Complete high resolution geochemical maps are strongly needed for mineral exploration; however, the previously proposed methods for making geochemical maps have low accuracy. In this research, we propose a new algorithm called sample density based mixture interpolation (SADBAMIN) for high resolution geochemical map completion using remote sensing data. In the SADBAMIN algorithm, first, according to the measured copper data density on the map, the map is classified into two parts: the area for training (T area) and the area waiting to be predicted (P area). The two areas are classified by the edge of the data point set’s alpha shape. In the T area, a triangle area among three neighbourhood points is interpolated by using the kriging model. Then, remote sensing data, including advanced spaceborne thermal emission and reflection radiometer (ASTER) data, digital elevation model (DEM) data, and geophysics (magnetic) data, and copper geochemical data at all measured and partial randomly selected interpolated points are applied as training data to construct a random forest regression model. By considering the relationship between interpolation reliability and distance, a penalty on data selection probability of going into training data is given. Finally, by inputting the remote sensing data in the P area to the model, the copper data in this area can be obtained, and the completed map comprises these two parts. We use 16,000 measured points, 10-fold cross-validation, and root mean squared error (RMSE) for model evaluation. We achieved an RMSE of 293 ppm, while the RMSE of the previously proposed method is 347 ppm.

Conference Presentation

Citation Download Citation

Chenhui Huang and Akinobu Shibuya "Approach for generating high accuracy machine learning model for high resolution geochemical map completion using remote sensing data: case study of Arizona, USA", Proc. SPIE 11156, Earth Resources and Environmental Remote Sensing/GIS Applications X, 111560F (3 October 2019); https://doi.org/10.1117/12.2524940

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available