On a new robust workflow for the statistical and spatial analysis of fracture data collected with scanlines (or the importance of stationarity)

被引:22
|
作者
Bistacchi, Andrea [1 ]
Mittempergher, Silvia [2 ]
Martinelli, Mattia [1 ]
Storti, Fabrizio [3 ]
机构
[1] Univ Milano Bicocca, Dipartimento Sci Ambiente & Terra, Piazza Sci 4, I-20126 Milan, Italy
[2] Univ Modena & Reggio Emilia, Dipartimento Sci Chim & Geol, Via G Campi 106, I-41125 Modena, Italy
[3] Univ Parma, Dipartimento Sci Chim Vita & Sostenibilita Ambien, NEXT Nat & Expt Tecton Res Grp, Parco Area Sci 157-A, I-43124 Parma, Italy
关键词
KOLMOGOROV-SMIRNOV TEST; FAULT SYSTEM; FLUID-FLOW; EVOLUTION; ZONE; STRESS; ORIGIN; JOINTS; ROCKS;
D O I
10.5194/se-11-2535-2020
中图分类号
P3 [地球物理学]; P59 [地球化学];
学科分类号
0708 ; 070902 ;
摘要
We present an innovative workflow for the statistical analysis of fracture data collected along scanlines, composed of two major stages, each one with alternative options. A prerequisite in our analysis is the assessment of stationarity of the dataset, which is motivated by statistical and geological considerations. Calculating statistics on non-stationary data can be statistically meaningless, and moreover the normalization and/or sub-setting approach that we discuss here can greatly improve our understanding of geological deformation processes. Our methodology is based on performing non-parametric statistical tests, which allow detecting important features of the spatial distribution of fractures, and on the analysis of the cumulative spacing function (CSF) and cumulative spacing derivative (CSD), which allows defining the boundaries of stationary domains in an objective way. Once stationarity has been analysed, other statistical methods already known in the literature can be applied. Here we discuss in detail methods aimed at understanding the degree of saturation of fracture systems based on the type of spacing distribution, and we evidence their limits in cases in which they are not supported by a proper spatial statistical analysis.
引用
收藏
页码:2535 / 2547
页数:13
相关论文
共 50 条
  • [1] Workflow analysis based on estimate of fuzzy and statistical data
    Tian, Feng
    Xing, Keyi
    Li, Renhou
    ICEBE 2006: IEEE INTERNATIONAL CONFERENCE ON E-BUSINESS ENGINEERING, PROCEEDINGS, 2006, : 203 - +
  • [3] A NEW ROBUST STATISTICAL MODEL FOR RADIOCARBON DATA
    Andres Christen, J.
    Perez E, Sergio
    RADIOCARBON, 2009, 51 (03) : 1047 - 1059
  • [4] A robust CETSA data analysis automation workflow for routine screening
    Weidinger, Juan Daniel Florez
    Pfreundschuh, Moritz
    Zoerb, Diana
    Yee, Ada
    Heyse, Stephan
    Baerenz, Felix
    Steigele, Stephan
    SLAS DISCOVERY, 2024, 29 (05)
  • [5] EXPLORING SURFACE COLLECTED DATA WITH SPATIAL ANALYSIS METHODS
    Bradbury, Andrew P.
    Allgood, Jessica L.
    Anderson, Jason M.
    Richmond, Michael D.
    Rotman, Deborah L.
    NORTH AMERICAN ARCHAEOLOGIST, 2008, 29 (01) : 13 - 36
  • [6] Integrating the statistical analysis of spatial data in ecology
    Liebhold, AM
    Gurevitch, J
    ECOGRAPHY, 2002, 25 (05) : 553 - 557
  • [7] Detecting outliers in irregularly distributed spatial data sets by locally adaptive and robust statistical analysis and GIS
    Liu, HX
    Jezek, KC
    O'Kelly, ME
    INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE, 2001, 15 (08) : 721 - 741
  • [8] STATISTICAL-ANALYSIS OF FRACTURE-TOUGHNESS DATA
    NEVILLE, DJ
    ENGINEERING FRACTURE MECHANICS, 1987, 27 (02) : 143 - 155
  • [9] Preparing glycomics data for robust statistical analysis with GlyCompareCT
    Zhang, Yujie
    Krishnan, Sridevi
    Bao, Bokan
    Chiang, Austin W. T.
    Sorrentino, James T.
    Schinn, Song-Min
    Kellman, Benjamin P.
    Lewis, Nathan E.
    STAR PROTOCOLS, 2023, 4 (02):
  • [10] Statistical analysis of a spatio-temporal model with location-dependent parameters and a test for spatial stationarity
    Rao, Suhasini Subba
    JOURNAL OF TIME SERIES ANALYSIS, 2008, 29 (04) : 673 - 694