IGNEOUS PETROLOGY

20161541 Bai Daoyuan(Hunan Institute of Geological Survey,Changsha 410016,China);Ma Tieqiu Zircon LA-ICP-MS U-Pb Dating of the Tuff of Yanmenzhai Formation inDongqiao,Pingxiang,with a Discussion on the Bottom Age of Nanhua System(Acta Petrologica et Mineralogica,ISSN1000-6524,CN11-1966/P,34(5),2015,p.637-647,5illus.,2tables,44refs.)

IGNEOUS PETROLOGY

20162308 Bai Daoyuan(Hunan Institute of Geology Survey,Changsha 410016,China);Zhong Xiang Geochemistry and Tectonic Setting of the Early Yanshanian Granites in the Miao’ershan Area,Southwest Hunan Province(Resources Survey&Environment,ISSN1671-4814,CN32-1640/N,36(4),2015,p.235-

Genesis and Geodynamic Significance of Chromitites from the Fuchuan Ophiolite,Southern China,as Evidenced by Trace Element Fingerprints of Chromite,Olivine and Pyroxene

The Fuchuan ophiolite is located in the northeasternmost segment of the Neoproterozoic Jiangnan orogen and consists mainly of harzburgites,with minor dunites,pyroxenite and gabbro veins and dykes.In order to investigate the genesis and tectonic setting of the Fuchuan ophiolite and chromitites,in situ analyses of unaltered chromites and silicates were carried out.Trace element analyses of unaltered chromites from the Fuchuan chromitites indicate the parental magma is of mid-ocean ridge basalt(MORB)-like origin,with the Ti/Fe^(3+#)–Ga/Fe^(3+#)diagram of chromites showing that the chromitites are a result of melt/rock interaction of MORB melts with mantle peridotites,and that the Fuchuan harzburgites present the dual features of MORB and supra-subduction zone peridotites(SSZP).Trace and rare earth element(REE)analyses of olivines and orthopyroxenes from the Fuchuan harzburgites hint at the possibility of mantle metasomatism influenced by SSZ-subducted fluids.Finally,integrating with previous study,the Fuchuan ophiolite and chromitites might have been formed in a back-arc spreading ridge between the Yangtze and Cathaysia blocks during the Neoproterozoic.

NORRRM: A Free Software to Calculate the CIPW Norm

This work gives an introduction to the R package NORRRM whose main purpose is to calculate the CIPW Norm. Although there are many softwares and spreadsheets available to estimate the standard mineral assemblages in igneous rocks, this package has the following advantages: a) it has the ability to be run on any operating computer systems (BSD, GNU/Linux, Mac OS X®, Windows®);b) an input argument allows choose different procedures to set the Fe-oxidation ratio;c) the options to use whole-rock major as well as minor oxides and trace elements in the computation;d) the output data yields highly consistent results achieving absolute match between the sum of the input weights of oxides recalculated on an anhydrous basis and the sum of the weights of estimated normative minerals and;e) the functions are written in R language and released under terms that guarantee users the freedom to study, adapt, modify, and distribute the software.

Petrogenesis of the Chaihulanzi Gneiss and its Tectonic Implications for the North China Craton

The Chaihulanzi area in eastern Inner Mongolia is tectonically situated on the northern margin of the North China Craton(NCC). The main Precambrian lithologies of the area have been referred to the Archean Jianping Group metasupracrustal sequences. Based on field observations, petrographic, whole-rock geochemical, and zircon U-Pb geochronological results, a magmatic origin for the units is proposed. Our results show that the Chaihulanzi gneisses are mainly of granitic, dioritic and granodioritic compositions, and show typical magmatic rock textures and mineral assemblages. The dioritic and granodioritic gneisses show Na-rich tonalite–trondhjemite–granodiorite(TTG)-like affinity with zircon U-Pb dates of ca. 2.57–2.59 Ga, representing a juvenile continental growth for the northeastern NCC. The granitic gneiss is indeed potassic granite and yielded a zircon U-Pb date of ~2.50 Ga, which is contemporaneous with the Jining–Jiaoliao microblock collision(2.53–2.49 Ga), implying another crustal growth event. The well-developed gneissosity in 2.57–2.59 Ga dioritic and granodioritic gneisses together with the 2.5 Ga potassic granite, which crosscuts the gneissosity implies a 2.57–2.50 Ga(mainly 2.53–2.51 Ga) collisional orogeny, probably related to the Jining–Jiaoliao microblock collision. Our new geology and chronological results provide new evidence for the early Precambrian tectonic evolution of the NCC.

Machine learning and tectonic setting determination:Bridging the gap between Earth scientists and data scientists

Technological progress and the rapid increase in geochemical data often create bottlenecks in many studies,because current methods are designed using limited number of data and cannot handle large datasets.In geoscience,tectonic discrimination illustrates this issue,using geochemical analyses to define tectonic settings when most of the geological record is missing,which is the case for most of the older portion of the Earth’s crust.Basalts are the primary target for tectonic discrimination because they are volcanic rocks found within all tectonic settings,and their chemical compositions can be an effective way to understand tectonics-related mantle processes.However,the classical geochemical discriminant methods have limitations as they are based on a limited number of 2 or 3-dimensional diagrams and need successive and subjective steps that often offers non-unique solutions.Also,weathering,erosion,and orogenic processes can modify the chemical composition of basalts and eliminate or obscure other complementary geotectonic records.To address those limitations,supervised machine learning techniques(a part of artificial intelligence)are being utilized more often as a tool to analyze multidimensional datasets and statistically process data to tackle big data challenges.This contribution starts by reviewing the current state of tectonic discrimination methods using supervised machine learning.Deep learning,especially Convolutional Neural Network(CNN)is the most accurate approach.However,it requires a large dataset and considerable processing time,and the gain of accuracy can be at the expense of interpretability.Therefore,this study designed guidelines for data pre-processing,tectonic setting classification and objectively evaluating the model performance.We also identify research gaps and propose potential directions for the application of supervised machine learning to tectonic discrimination research,aimed at closing the divide between earth scientists and data scientists.