Translator Disclaimer
Presentation + Paper
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
Author Affiliations +
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
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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
PROCEEDINGS
10 PAGES + PRESENTATION

SHARE
Advertisement
Advertisement
Back to Top