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Stochastic local interaction model: an alternative to kriging for massive datasets

Christopoulos Dionysios, Pavlidis Andreas, Agou Vasiliki, Gafa Panagiota

Απλή Εγγραφή


URIhttp://purl.tuc.gr/dl/dias/5B2CC9A9-DF61-4706-89BC-F815B48770BF-
Αναγνωριστικόhttps://doi.org/10.1007/s11004-021-09957-7-
Αναγνωριστικόhttps://link.springer.com/article/10.1007/s11004-021-09957-7-
Γλώσσαen-
Μέγεθος43 pagesen
ΤίτλοςStochastic local interaction model: an alternative to kriging for massive datasetsen
ΔημιουργόςChristopoulos Dionysiosen
ΔημιουργόςΧριστοπουλος Διονυσιοςel
ΔημιουργόςPavlidis Andreasen
ΔημιουργόςΠαυλιδης Ανδρεαςel
ΔημιουργόςAgou Vasilikien
ΔημιουργόςΑγου Βασιλικηel
ΔημιουργόςGafa Panagiotaen
ΔημιουργόςΓκαφα Παναγιωταel
ΕκδότηςSpringeren
ΠερίληψηClassical geostatistical methods face serious computational challenges if they are confronted with large spatial datasets. The stochastic local interaction (SLI) approach does not require matrix inversion for parameter estimation, spatial prediction, and uncertainty estimation. This leads to better scaling of computational complexity and storage requirements with data size than standard (i.e., without size-reducing modifications) kriging. This contribution presents a simplified SLI model that can handle large data. The SLI method constructs a spatial interaction matrix (precision matrix) that adjusts with minimal user input to the data values, their locations, and sampling density variations. The precision matrix involves compact kernel functions which permit the use of sparse matrix methods. It is proved that the precision matrix of the proposed SLI model is strictly positive definite. In addition, parameter estimation based on likelihood maximization is formulated, and computationally relevant properties of the likelihood function are studied. The interpolation performance of the SLI method is investigated and compared with ordinary kriging using (i) synthetic non-Gaussian data and (ii) coal thickness measurements from approximately 11,500 drill holes (Campbell County, Wyoming, USA).en
ΤύποςPeer-Reviewed Journal Publicationen
ΤύποςΔημοσίευση σε Περιοδικό με Κριτέςel
Άδεια Χρήσηςhttp://creativecommons.org/licenses/by/4.0/en
Ημερομηνία2022-11-04-
Ημερομηνία Δημοσίευσης2021-
Θεματική ΚατηγορίαFast interpolationen
Θεματική ΚατηγορίαBig dataen
Θεματική ΚατηγορίαKernel functionen
Θεματική ΚατηγορίαStatistical learningen
Θεματική ΚατηγορίαGaussian Markov random fieldsen
Θεματική ΚατηγορίαNatural resources estimationen
Βιβλιογραφική ΑναφοράD. T. Hristopulos, A. Pavlides, V. D. Agou, and P. Gkafa, “Stochastic local interaction model: an alternative to kriging for massive datasets,” Math. Geosci., vol. 53, no. 8, pp. 1907–1949, Nov. 2021, doi: 10.1007/s11004-021-09957-7.en

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