Bollettino di Geofisica Teorica e Applicata
OGS Website BGTA homepage
About the Journal
Contacts
To Authors
On-line Submission
Subscriptions
Forthcoming
On-line First
The Historical First Issue
Issues

2020 Vol. 61
Suppl. 1 / 1 / 2

2019 Vol. 60
1 / 2 / 3 / 4 / Suppl. 1 / Suppl. 2 / Suppl. 3

2018 Vol. 59
1 / 2 / 3 / 4 / Suppl. 1

2017 Vol. 58
1 / 2 / 3 / 4

2016 Vol. 57
1 / 2 / 3 / 4 / Suppl. 1

2015 Vol. 56
1 / 2 / 3 / 4

2014 Vol. 55
1 / 2 / 3 / 4

2013 Vol. 54
1 / 2 / 3 / 4 / Suppl. 1 / Suppl. 2

2012 Vol. 53
1 / 2 / 3 / 4

2011 Vol. 52
1 / 2 / 3 / 4 / Suppl. 1

2010 Vol. 51
1 / 2-3 / 4 / Suppl. 1

2009 Vol. 50
1 / 2 / 3 / 4

2008 Vol. 49
1 / 2 / 3-4 / Suppl. 1

2007 Vol. 48
1 / 2 / 3 / 4

2006 Vol. 47
1-2 / 3 / 4

2005 Vol. 46
1 / 2-3 / 4

2004 Vol. 45
1-2 / 3 / 4 / Suppl. 1 / Suppl. 2

2003 Vol. 44
1 / 2 / 3-4

2002 Vol. 43
1-2 / 3-4

2001 Vol. 42
1-2 / 3-4

2000 Vol. 41
1 / 2 / 3-4

1999 Vol. 40
1 / 2 / 3-4

1998 Vol. 39
1 / 2 / 3 / 4

1997 Vol. 38
1-2 / 3-4

1995 Vol. 37
145 / 146 / 147 / 148 / Suppl. 1

1994 Vol. 36
141-144 / Suppl. 1

1993 Vol. 35
137-138 / 139 / 140

1992 Vol. 34
133 / 134-135 / 136

1991 Vol. 33
129 / 130-131 / 132

 
 

Vol. 61, n.2, June 2020
pp. 177-198

Determining the optimum search range for 2D and 3D mapping based on kriging through quantitative analysis

O. Asghari, M. Safikhani and S. Talesh Hosseini

Received: 6 August 2019; accepted: 4 November 2019

Abstract

As the best linear estimator, kriging is now a well-established method in all types of 3D geomodelling, including geochemical mapping, rock type modelling, 2D geophysical mapping, and resource estimation. In this context, investigating kriging performance has always been of interest to numerous researchers. Evaluating kriging implementation for different applications has been a growing field of study in the last few decades. Although many authors have discussed various kriging parameters, it seems necessary to conduct more detailed studies on range searching, high and low nugget effect, as well as 2D and 3D estimations. In this paper, an optimal search range was determined using Quantitative Kriging Neighbourhood Analysis (QKNA), and the utility of this search range was explored by assessing kriging efficiency. Because of the existence of different numerical measures of search ranges in each criterion, it is difficult to define the optimal search range of an estimation process. In this research, different Multiple Criteria Decision Making (MCDM) methods were employed to determine the optimal search range via QKNA and by considering criteria which were applied to different cases. Given the unique capacity of this method in meeting this challenge, the Fuzzy-TOPSIS method, a variant of MCDM, was used in this study.



Download PDF complete



back to table of contents