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Wide & deep learning for predicting relative mineral compositions of sediment cores solely based on XRF scans, a case study from Pleistocene Paleolake Olduvai, Tanzania

Artificial Intelligence in Geosciences

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Artificial Intelligence in Geosciences 2024年第1期5卷 244-256页

作者： Gayantha R.L.Kodikara Lindsay J.McHenry Ian G.Stanistreet harald stollhofen Jackson K.Njau Nicholas Toth Kathy Schick Department of Geosciences University of Wisconsin-Milwaukee3209 NMaryland AveMilwaukeeWI53211USA Department of Earth Ocean and Ecological SciencesUniversity of LiverpoolBrownlow StreetLiverpoolL693GPUK The Stone Age Institute BloomingtonIN47407-5097USA GeoZentrum Nordbayern Friedrich-Alexander-University(FAU)Erlangen-NümbergSchloβgarten 591054ErlangenGermany Department of Earth and Atmospheric Sciences Indiana University1001 East 10th StreetBloomingtonIN47405-1405USA

This study develops a method to use deep learning models to predict the mineral assemblages and their relative abundances in paleolake cores using high-resolution XRF core scan elemental data and X-ray diffraction(XRD)mineralogical results from the same core taken at coarser *** uses the XRF core scan data along with published mineralogical information from the Olduvai Gorge Coring Project(OGCP)2014 sediment cores 1A,2A,and 3A from Paleolake Olduvai,*** regression and classification models were developed using a Keras deep learning framework to assess the predictability of mineral assemblages with their relative abundances(in regression models)or at least the mineral assemblages(in classification models)using XRF core scan *** were created using the Sequential class and Functional API with different model *** correlation matrix of element ratios calculated from XRF element intensity records from the cores and XRD-derived mineralogical information was used to select the most useful features to train the models.1057 training data records were used for the *** classes were also used for some models using Wide&Deep neural networks since those combine the benefits of memorization and generalization for mineral *** results were validated using 265 validation data records unseen by the model and discuss the accuracy of models using six test *** optimized Deep Neural Network(DNN)classification model achieved over 86%binary accuracy while the regression models were also able to predict the relative mineral abundances of samples with high ***,the study shows the efficacy of a carefully crafted Deep Learning(DL)model for predicting mineral assemblages and abundances using high-resolution XRF core scan data.

关键词： Paleo Pleistocene XRF

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