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1.
Assessment of Exploration Bias in Data-Driven Predictive Models and the Estimation of Undiscovered Resources 总被引：3，自引：0，他引：3
The spatial distribution of discovered resources may not fully mimic the distribution of all such resources, discovered and
undiscovered, because the process of discovery is biased by accessibility factors (e.g., outcrops, roads, and lakes) and by
exploration criteria. In data-driven predictive models, the use of training sites (resource occurrences) biased by exploration
criteria and accessibility does not necessarily translate to a biased predictive map. However, problems occur when evidence
layers correlate with these same exploration factors. These biases then can produce a data-driven model that predicts known
occurrences well, but poorly predicts undiscovered resources.
Statistical assessment of correlation between evidence layers and map-based exploration factors is difficult because it is
difficult to quantify the “degree of exploration.” However, if such a degree-of-exploration map can be produced, the benefits
can be enormous. Not only does it become possible to assess this correlation, but it becomes possible to predict undiscovered,
instead of discovered, resources.
Using geothermal systems in Nevada, USA, as an example, a degree-of-exploration model is created, which then is resolved into
purely explored and unexplored equivalents, each occurring within coextensive study areas. A weights-of-evidence (WofE) model
is built first without regard to the degree of exploration, and then a revised WofE model is calculated for the “explored
fraction” only. Differences in the weights between the two models provide a correlation measure between the evidence and the
degree of exploration.
The data used to build the geothermal evidence layers are perceived to be independent of degree of exploration. Nevertheless,
the evidence layers correlate with exploration because exploration has preferred the same favorable areas identified by the
evidence patterns. In this circumstance, however, the weights for the “explored” WofE model minimize this bias. Using these
revised weights, posterior probability is extrapolated into unexplored areas to estimate undiscovered deposits. 相似文献
2.
Knowledge-Driven and Data-Driven Fuzzy Models for Predictive Mineral Potential Mapping 总被引：5，自引：0，他引：5
In this paper, we describe new fuzzy models for predictive mineral potential mapping: (1) a knowledge-driven fuzzy model that uses a logistic membership function for deriving fuzzy membership values of input evidential maps and (2) a data-driven model, which uses a piecewise linear function based on quantified spatial associations between a set of evidential evidence features and a set of known mineral deposits for deriving fuzzy membership values of input evidential maps. We also describe a graphical defuzzification procedure for the interpretation of output fuzzy favorability maps. The models are demonstrated for mapping base metal deposit potential in an area in the south-central part of the Aravalli metallogenic province in the state of Rajasthan, western India. The data-driven and knowledge-driven models described in this paper predict potentially mineralized zones, which occupy less than 10% of the study area and contain at least 83% of the model and validation base metal deposits. A cross-validation of the favorability map derived from using one of the models with the favorability map derived from using the other model indicates a remarkable similarity in their results. Both models therefore are useful for predicting favorable zones to guide further exploration work. 相似文献
3.
蒸散发是水圈、大气圈和生物圈中水分循环和能量交换的纽带。在全球尺度上,蒸散发约占陆地降水总量的60%;作为其能量表达形式,潜热通量约占地表净辐射的80%。随着通量观测技术的发展,全球长期持续的观测数据得以获取和共享,近年来基于数据驱动的蒸散发遥感反演方法取得了较好的研究进展。本文针对数据驱动的蒸散发遥感反演方法和产品,从经验回归、机器学习和数据融合3个方面展开,对现有的研究进展进行了梳理、归纳和总结,并从驱动数据、反演方法、已有产品等方面指出目前仍存在的问题和不足。未来仍需开展数据驱动的高时空分辨率的蒸散发遥感反演方法的研究,有效考虑地表温度和土壤水分等可以指示地表蒸散发短期变化的重要信息,同时加强基于过程驱动的物理模型与数据驱动的模型的结合,使两类模型能互为补充、各自发挥所长,共同推动蒸散发遥感反演研究水平的进步。 相似文献
4.
将一种基于数据驱动的RegEM算法引入GPS坐标时间序列插值中,分别采用模拟不同比例连续缺失数据与实测含缺失数据,比较RegEM与拉格朗日方法、三次样条方法、正交多项式方法的插值效果与性能。结果表明,对于模拟不同比例连续缺失数据,RegEM算法插值效果均优于传统方法,且在大量数据连续缺失的情况下效果最优;对于实测含缺失数据,RegEM方法插值所得序列保留方差最大化效果最好,约为正交多项式方法的1.17倍、三次样条方法的1.38倍。 相似文献
5.
基于反馈模型的地表相关多次波消除方法SRME(Surface-Related Multiple Elimination)近年来已得到了广泛的应用.利用共聚焦点CFP(Common Focus Point)道集代替炮集,可以将该方法扩展至层间多次波的消除.地表数据驱动的层间多次波消除方法直接利用地表观测数据进行层间多次波预测,避免了构建CFP道集所需的聚焦运算,特别是与层相关的层间多次波消除方法有效提高了多次波预测的计算效率.但地表数据驱动的与层相关的层间多次波消除方法并没有从理论上被严格地推导证明,其与CFP方法之间的关系亦未被讨论.本文在CFP方法的理论基础上推导了地表数据驱动的与层相关的层间多次波消除方法,阐明了CFP方法与地表数据驱动方法之间的内在联系.并将该方法应用于模型数据和野外实际数据,应用实例表明了所提方法的有效性. 相似文献
6.
化石数据是了解地球历史以及深时生命演化的重要信息来源。通过数百年的积累,古生物学家已经发表了海量的古生物学数据。过去三、四十年里,随着计算机、数据库和互联网技术的快速发展,国内外涌现出大量的古生物学数据库,彼此间的目标、体系架构、数据组织方式和服务对象通常存在显著差异,呈现百花齐放的特点。文章系统介绍了古生物学领域主要数据库的发展历史、数据表结构、数据特征和数据量等建设情况,对比分析了其数据整理方式、核心在线功能、数据共享特点和数据质量控制措施。同时,结合近年来数据驱动下的古生物学领域的科学研究实例,提出一站式全生态链数据平台的建设设想,为深时数字地球（DDE）建设多学科融合、数据开放与共享的大数据平台提供参考。 相似文献
7.
ABSTRACT“Panta Rhei – Everything Flows” is the science plan for the International Association of Hydrological Sciences scientific decade 2013–2023. It is founded on the need for improved understanding of the mutual, two-way interactions occurring at the interface of hydrology and society, and their role in influencing future hydrologic system change. It calls for strategic research effort focused on the delivery of coupled, socio-hydrologic models. In this paper we explore and synthesize opportunities and challenges that socio-hydrology presents for data-driven modelling. We highlight the potential for a new era of collaboration between data-driven and more physically-based modellers that should improve our ability to model and manage socio-hydrologic systems. Crucially, we approach data-driven, conceptual and physical modelling paradigms as being complementary rather than competing, positioning them along a continuum of modelling approaches that reflects the relative extent to which hypotheses and/or data are available to inform the model development process.
EDITOR D. Koutsoyiannis; ASSOCIATE EDITOR not assigned 相似文献
8.
ABSTRACTAn appropriate streamflow forecasting method is a prerequisite for implementation of efficient water resources management in the water-limited, arid regions that occupy much of Iran. In the current research, monthly streamflow forecasting was combined with three data-driven methods based on large input datasets involving 11 precipitation stations, a natural streamflow, and four climate indices through a long period. The major challenges of rainfall–runoff modelling are generally attributed to complex interacting processes, the large number of variables, and strong nonlinearity. The sensitivity of data-driven methods to the dimension of input/output datasets would be another challenge, so large datasets should be compressed into independently standardized principal components. In this study, three pre-processing techniques were applied: singular value decomposition (SVD) provided more efficient forecasts in comparison to principal component analysis (PCA) and average values of inputs in all networks. Among the data-driven methods, the multi-layer perceptron (MLP) with 1-month lag-time outperformed radial basis and fuzzy-based networks. In general, an increase in monthly lag-time of streamflow forecasting resulted in a decline in forecasting accuracy. The results reveal that SVD was highly effective in pre-processing of data-driven evaluations. 相似文献
9.
大数据技术在地学领域的应用越来越广泛,大数据思维为地学研究开辟了新的思路。从数据出发,以数据驱动模式去分析地质问题,在元素分布特征分析、矿床地化异常识别等方面较传统地学分析方法有着明显的优势。文章基于便携式X荧光分析仪（pXRF）测试的原位、无损、快捷、多元素分析等优点,对宣城矿集区内的荞麦山铜多金属矿床岩芯进行高密度的全数据原位采集,使用主成分分析（PCA）、多元逐步线性回归等方法进行量化分析。2种方法均表明,荞麦山铜多金属矿床中元素Mg、Ca、Mn、Co、As、Se、Ag、Hg、U和成矿元素Cu、Fe、S、W存在正相关关系;钻孔矿化和蚀变特征、铜硫矿石、钨矿石、石英砂岩均表现出不同的元素组合,特别是主成分综合得分（PCA）和第一主成分（PC1）元素对钻孔成矿区具有较好的指示效果;逐步多元线性回归分析进一步量化了元素富集规律,对成矿元素的拟合能够较好地与钻孔信息形成对应。因此,pXRF高密度的原位测量能够快速获取全面、准确的元素数据,分析结果能够直观反映荞麦山铜多金属矿床各元素的深度空间分布情况及量化相关关系,对深部地球化学特征的恢复和找矿提供帮助。 相似文献
10.
ABSTRACT“Panta Rhei – Everything Flows” is the science plan for the International Association of Hydrological Sciences scientific decade 2013–2023. It is founded on the need for improved understanding of the mutual, two-way interactions occurring at the interface of hydrology and society, and their role in influencing future hydrologic system change. It calls for strategic research effort focused on the delivery of coupled, socio-hydrologic models. In this paper we explore and synthesize opportunities and challenges that socio-hydrology presents for data-driven modelling. We highlight the potential for a new era of collaboration between data-driven and more physically-based modellers that should improve our ability to model and manage socio-hydrologic systems. Crucially, we approach data-driven, conceptual and physical modelling paradigms as being complementary rather than competing, positioning them along a continuum of modelling approaches that reflects the relative extent to which hypotheses and/or data are available to inform the model development process.
EDITOR D. Koutsoyiannis; ASSOCIATE EDITOR not assigned 相似文献