Geoscience Frontiers 2018 Annual Convention

The 2018 Geoscience Frontiers Annual Convention was held at the China University of Geosciences(Beijing),China,during September 20,2018(Fig.1).This convention assembled earth scientists from five countries,including Australia(Dr.Christopher Spencer),India(Dr.Mu.Ramkumar),USA(Dr.Richard Damian Nance and Dr.Joseph Meert),UK(Dr.Nick Roberts),Turkey(Prof.Yener Eyuboglu),China,and also representative from Elsevier(Beijing).     

Geoscience Frontiers 2019 Annual Convention

The 2019 Geoscience Frontiers Annual Convention was held at the China University of Geosciences(Beijing),China on September 20,2019(Fig.1).This convention assembled earth scientists from four countries,including Australia(Dr.Christopher Spencer),Italy(Dr.Andrea Festa),UK(Dr.Pieter Vermeesch),China,and also representative from Elsevier(Beijing).The Convention started with the introduction by Dr.Lily Wang,Editorial Assistant at Geoscience Frontiers,followed by the inaugural address by Prof.M.Santosh,Editorial Advisor of Geoscience Frontiers.     

实验地球科学的前沿与发展战略

实验地球科学是利用实验装置和技术模拟地球内部的高温高压条件,开展地球内部物质的物理和化学属性与地球内部过程研究.我国的实验地球科学在过去10年得到了快速发展,已成为国际高温高压实验领域的一支重要研究力量.主要介绍了实验地球科学的定义与战略价值,简述了我国实验地球科学的发展现状与薄弱环节,提出了未来学科发展的思路和重要举措,并展望了学科未来的优先发展方向.     

《现代地质》2017年(第31卷)总目次

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Probability Aggregation Methods in Geoscience

The need for combining different sources of information in a probabilistic framework is a frequent task in earth sciences. This is a need that can be seen when modeling a reservoir using direct geological observations, geophysics, remote sensing, training images, and more. The probability of occurrence of a certain lithofacies at a certain location for example can easily be computed conditionally on the values observed at each source of information. The problem of aggregating these different conditional probability distributions into a single conditional distribution arises as an approximation to the inaccessible genuine conditional probability given all information. This paper makes a formal review of most aggregation methods proposed so far in the literature with a particular focus on their mathematical properties. Exact relationships relating the different methods is emphasized. The case of events with more than two possible outcomes, never explicitly studied in the literature, is treated in detail. It is shown that in this case, equivalence between different aggregation formulas is lost. The concepts of calibration, sharpness, and reliability, well known in the weather forecasting community for assessing the goodness-of-fit of the aggregation formulas, and a maximum likelihood estimation of the aggregation parameters are introduced. We then prove that parameters of calibrated log-linear pooling formulas are a solution of the maximum likelihood estimation equations. These results are illustrated on simulations from two common stochastic models for earth science: the truncated Gaussian model and the Boolean. It is found that the log-linear pooling provides the best prediction while the linear pooling provides the worst.