期刊
GEOSCIENTIFIC MODEL DEVELOPMENT
卷 11, 期 6, 页码 2525-2539出版社
COPERNICUS GESELLSCHAFT MBH
DOI: 10.5194/gmd-11-2525-2018
关键词
-
资金
- National Natural Science Foundation of China [41602336, 71503200]
- China Postdoctoral Science Foundation [2017T100773, 2016M592840]
- Shaanxi Provincial Natural Science Foundation [2017JQ7010]
- Fundamental Research from Northwest AF University [2017RWYB08]
The combination of complex, multiple minerogenic stages and mineral superposition during geological processes has resulted in dynamic spatial distributions and nonstationarity of geological variables. For example, geo-chemical elements exhibit clear spatial variability and trends with coverage type changes. Thus, bias is likely to occur under these conditions when general regression models are applied to mineral prospectivity mapping (MPM). In this study, we used a spatially weighted technique to improve general logistic regression and developed an improved model, i.e., the improved logistic regression model, based on a spatially weighted technique (ILRBSWT, version 1.0). The capabilities and advantages of ILRBSWT are as follows: (1) it is a geographically weighted regression (GWR) model, and thus it has all advantages of GWR when managing spatial trends and nonstationarity; (2) while the current software employed for GWR mainly applies linear regression, ILRBSWT is based on logistic regression, which is more suitable for MPM because mineralization is a binary event; (3) a missing data processing method borrowed from weights of evidence is included in ILRBSWT to extend its adaptability when managing multisource data; and (4) in addition to geographical distance, the differences in data quality or exploration level can be weighted in the new model.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据