共被期刊论文引用了2次
您的检索式：您选中1篇文献正在查看引证文献汇总
|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | The petrogenesis of modern and ophiolitic lavas reconsidered:Ti-V and Nb-Th显示文摘Tectonic discrimination diagrams are a key tool for understanding ancient volcanic rock origins.In this contribution we compile over 15,000 whole rock compositions to re-evaluate the Ti-V discrimination diagram and compare it to another commonly used tool,the Nb/Yb-Th/Yb diagram.We have reformulated the Ti-V diagram into a log-log plot to distinguish samples more clearly at lower concentrations.The compilation shows that MORB are dominated by Ti/V=20-43,whereas juvenile arc tholeiites and boninites are characterized by Ti/V<20 generally,although there is minor overlap at the boundary(Ti/V=20-22).Plume-related volcanic rocks(ocean island basalts,oceanic plateaux,and continental flood basalts)generally have Ti/V>43,although there may be significant overlap with MORB-like ratios for ridgecentered OIB and for some oceanic plateaux.About 56%of alkaline OIB have Ti/V>70.Back-arc basins are dominantly MORB-like.Melt models show that MORB and juvenile arc volcanics most likely formed under different fO;conditions,but are permissive of similar fO_(2)if the arc rocks form by much higher melt fractions.The Nb/Yb vs.Th/Yb plot clearly distinguishes most oceanic basalts(MORB,plateaux,OIB)from subduction-related volcanic rocks(boninite,juvenile arc tholeiite,calc-alkaline)and from flood basalts.We propose here a new two-proxy diagram of Ti/V vs.Th/Nb,which incorporates the advantages of both. | John W.Shervais | 2022 | Geoscience Frontiers2022，13，2： | 1 |
| 2 | Machine learning and tectonic setting determination:Bridging the gap between Earth scientists and data scientists显示文摘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. | Pratchaya Takaew Jianhong Cecilia Xia Luc S.Doucet | 2024 | Geoscience Frontiers2024，15，1： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员