Supporting data-driven exploration in NSW

被引:0
|
作者
Gates K. [1 ]
机构
[1] Geological Survey of NSW, 516 High Street, Maitland, 2320, NSW
关键词
data validation; geochemistry; interpolation;
D O I
10.1080/22020586.2019.12073240
中图分类号
学科分类号
摘要
NSW legislative changes in 2016 mean that from 1 June 2021, confidential mineral exploration company reports will become open file 5 years after submission. This will result in a large amount of previously confidential data being released on 1 June 2021. In conjunction with the data release, substantial work is being undertaken to improve the quality, accessibility and usability of the datasets. To provide the most functional regional scale geochemical datasets possible, the usability of the datasets must be assessed by following a standard workflow of data validation > exploratory data analysis (EDA) > normalisation > interpolation. Due to the large size of the datasets, programmatical solutions that expedite the validation and EDA processes are necessary. This presentation will give examples of methods used to condition the NSW geochemical data. An innovative data normalisation process will also be demonstrated, addressing the complexity of dealing with spatially overlapping and varied sample methods. An interpolation process that is suitable for use on large volume, large-scale datasets will also be demonstrated. © 2019, Taylor and Francis. All rights reserved.
引用
收藏
相关论文
共 50 条
  • [41] Recognition of aeolian soils of the Blayney district, NSW: implications for mineral exploration
    Dickson, BL
    Scott, KM
    JOURNAL OF GEOCHEMICAL EXPLORATION, 1998, 63 (03) : 237 - 251
  • [42] Real-time prediction by data-driven models applied to induction heating process
    Derouiche, Khouloud
    Daoud, Monzer
    Traidi, Khalil
    Chinesta, Francisco
    INTERNATIONAL JOURNAL OF MATERIAL FORMING, 2022, 15 (04)
  • [43] A Data-Driven Krylov Model Order Reduction for Large-Scale Dynamical Systems
    M. A. Hamadi
    K. Jbilou
    A. Ratnani
    Journal of Scientific Computing, 2023, 95
  • [44] Data-driven design approaches for hollow section columns-Database analysis and implementation
    Koh, Hyeyoung
    Blum, Hannah B.
    JOURNAL OF CONSTRUCTIONAL STEEL RESEARCH, 2025, 224
  • [45] Rapid Prediction of Flexible Deformations of a Gust Generator Vane Using Data-Driven Approaches
    Wang, Libao
    Xu, Min
    Zhao, Dongqiang
    Huang, Guoning
    JOURNAL OF AEROSPACE ENGINEERING, 2023, 36 (03)
  • [46] Real-time prediction by data-driven models applied to induction heating process
    Khouloud Derouiche
    Monzer Daoud
    Khalil Traidi
    Francisco Chinesta
    International Journal of Material Forming, 2022, 15
  • [47] A Data-Driven Krylov Model Order Reduction for Large-Scale Dynamical Systems
    Hamadi, M. A.
    Jbilou, K.
    Ratnani, A.
    JOURNAL OF SCIENTIFIC COMPUTING, 2023, 95 (01)
  • [48] Data-driven model for predicting machining cycle time in ultra-precision machining
    Zhu, Tong
    Lee, Carman K. M.
    To, Sandy Suet
    ADVANCES IN MANUFACTURING, 2025,
  • [49] Data-Driven Modeling of Weakly Nonlinear Circuits via Generalized Transfer Function Approximation
    Carlucci, Antonio
    Gosea, Ion Victor
    Grivet-Talocia, Stefano
    IEEE ACCESS, 2025, 13 : 2746 - 2762
  • [50] Data-Driven Interpolation of Sea Surface Suspended Concentrations Derived from Ocean Colour Remote Sensing Data
    Vient, Jean-Marie
    Jourdin, Frederic
    Fablet, Ronan
    Mengual, Baptiste
    Lafosse, Ludivine
    Delacourt, Christophe
    REMOTE SENSING, 2021, 13 (17)