Regional geochemical prospecting data contains a lot of geological information.Extracting the geological spatial distributing information contained in these data is of great significance for regional geological research and mineral prospecting.Regional geochemical data usually includes dozens of elements, which belong to high-dimensional data.Geological spatial distributing information hidden in these high-dimensional data cannot be observed directly from the data.
In order to solve this problem, we constructed a dimensionality reduction and visualization model of high-dimensional Extension Kit regional geochemical exploration data based on the t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm.t-SNE algorithm is a nonlinear dimensionality reduction method, which is especially suitable for dimensionality reduction and visualization of high-dimensional data.Select the elements that are sufficiently stable in lithology identification.Reduce the dimension of high-dimensional geochemical exploration data to 1D, 2D or 3D through the t-SNE algorithm, because the low-dimensional data less than 3D can be observed by human eyes easily.
Express the output variables of dimension reduction algorithm as raster files, and visualize them by RGB color mixing and other methods, thus the spatial distribution information of geological bodies hidden in high-dimensional geochemical exploration data can be observed directly.The regional geochemical exploration data of stream sediments in a region of southwest England are taken as an example to evaluate the t-SNE algorithm in visualization of high-dimensional geochemical exploration data.The case study shows that: (1) The high-dimensional geochemical exploration data visualization results through t-SNE algorithm can represent the spatial distribution of geological bodies in the study area very well; (2) The visualization results are tightly related to two parameters: target dimension and perplexity of the t-SNE algorithm.The higher the target dimension was be reduced in the t-SNE algorithm, the more detailed the geological spatial information displayed.
(3) The results of dimension reduction and visualization of geochemical exploration data based on the t-SNE algorithm are better than those based Bilberry on principal component analysis (PCA).The research in this paper shows that the high-dimensional geochemical exploration data visualization method based on the t-SNE algorithm can display the spatial distribution information of geological bodies, which has certain guiding significance for inferring the spatial distribution of geological bodies.