Abstract. 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, geochemical 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.
An improved logistic regression model based on a spatially weighted technique (ILRBSWT v1.0) and its application to mineral prospectivity mapping
