Articles | Volume 15, issue 6
© Author(s) 2022. This work is distributed underthe Creative Commons Attribution 4.0 License.
SciKit-GStat 1.0: a SciPy-flavored geostatistical variogram estimation toolbox written in Python
- Final revised paper (published on 25 Mar 2022)
- Preprint (discussion started on 27 Jul 2021)
Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor |
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RC1: 'Comment on gmd-2021-174', Anonymous Referee #1, 10 Oct 2021
- AC1: 'Reply on RC1', Mirko Mälicke, 14 Oct 2021
- AC2: 'Reply on RC1', Mirko Mälicke, 19 Oct 2021
RC2: 'Comment on gmd-2021-174', Anonymous Referee #2, 13 Jan 2022
- AC3: 'Reply on RC2', Mirko Mälicke, 13 Jan 2022
RC3: 'Comment on gmd-2021-174', Anonymous Referee #3, 18 Jan 2022
- AC4: 'Reply on RC3', Mirko Mälicke, 19 Jan 2022
Peer review completion
AR: Author's response | RR: Referee report | ED: Editor decision
AR by Mirko Mälicke on behalf of the Authors (04 Feb 2022)  Author's response Author's tracked changes Manuscript
ED: Publish as is (16 Feb 2022) by Rohitash Chandra
The author presents a variogram estimation Python package SciKit-GStat based on SciPy. Variogram is the core function for geostatistical methods to describe spatial covariance of the data and is the determination factor for the application of geostatistics such as Kriging interpolation. SciKit-GStat includes many commonly used variogram related algorithms of variogram estimators, theoretical spatial models and distance lag binning, also advanced models of directional variogram and space-time variogram are implemented. Although the limitation of Kriging methods implementation exists in the package, the interface to other geostatistical packages such as gstools is provided for final steps of geostatistical analysis. SciKit-GStat was well coded and documented as an efficient and ease-used variogram toolbox.
A major revision is needed to improve the manuscript before the publication.