Machine learning for source identification of dust on the Chinese Loess Plateau

Abstract:

The provenance of voluminous aeolian dust on the Chinese Loess Plateau (CLP) is still highly debated. Here we apply machine learning methods of support vector machine and convolutional neural network to train models using element compositions of surface sediments from eight potential source regions, accordingly, to determine the dust sources and contributions by classifying the last glacial loess and present interglacial sediments on the CLP. The trained models succeed in differentiating major secondary sources and quantitatively estimating the contributions of both primary and secondary sources at least during the last glacial - interglacial cycle. The understanding that a constant dust source despite changing climate conditions agrees with those derived from Sr-Nd isotopes and U-Pb age spectra. Our observations demonstrate that big geochemical data sets coupled with machine learning technology is fully capable of tracing sources.