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

作     者：Gayantha R.L.Kodikara Lindsay J.McHenry Ian G.Stanistreet Harald Stollhofen Jackson K.Njau Nicholas Toth Kathy Schick 

作者机构：Department of GeosciencesUniversity of Wisconsin-Milwaukee3209 NMaryland AveMilwaukeeWI53211USA Department of EarthOcean and Ecological SciencesUniversity of LiverpoolBrownlow StreetLiverpoolL693GPUK The Stone Age InstituteBloomingtonIN47407-5097USA GeoZentrum NordbayernFriedrich-Alexander-University(FAU)Erlangen-NümbergSchloβgarten 591054ErlangenGermany Department of Earth and Atmospheric SciencesIndiana University1001 East 10th StreetBloomingtonIN47405-1405USA 

出 版 物：《Artificial Intelligence in Geosciences》 (地学人工智能（英文）)

年 卷 期：2024年第5卷第1期

页      面：244-256页

核心收录：

学科分类：070903[理学-古生物学与地层学(含：古人类学)] 0709[理学-地质学] 07[理学] 

基　　金：supported by the National Science Foundation (BCS grant#1623884 to Njau and McHenry) Computational work was also supported by NASA SSW grant NNH20ZDA001N 

主　　题：Paleo Pleistocene XRF 

摘      要：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.