Ιδρυματικό Αποθετήριο
Πολυτεχνείο Κρήτης
Πολυτεχνείο Κρήτης
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| URI | http://purl.tuc.gr/dl/dias/5B2CC9A9-DF61-4706-89BC-F815B48770BF | - |
| Αναγνωριστικό | https://doi.org/10.1007/s11004-021-09957-7 | - |
| Αναγνωριστικό | https://link.springer.com/article/10.1007/s11004-021-09957-7 | - |
| Γλώσσα | en | - |
| Μέγεθος | 43 pages | en |
| Τίτλος | Stochastic local interaction model: an alternative to kriging for massive datasets | en |
| Δημιουργός | Christopoulos Dionysios | en |
| Δημιουργός | Χριστοπουλος Διονυσιος | el |
| Δημιουργός | Pavlidis Andreas | en |
| Δημιουργός | Παυλιδης Ανδρεας | el |
| Δημιουργός | Agou Vasiliki | en |
| Δημιουργός | Αγου Βασιλικη | el |
| Δημιουργός | Gafa Panagiota | en |
| Δημιουργός | Γκαφα Παναγιωτα | el |
| Εκδότης | Springer | en |
| Περίληψη | Classical 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 |
| Τύπος | Peer-Reviewed Journal Publication | en |
| Τύπος | Δημοσίευση σε Περιοδικό με Κριτές | el |
| Άδεια Χρήσης | http://creativecommons.org/licenses/by/4.0/ | en |
| Ημερομηνία | 2022-11-04 | - |
| Ημερομηνία Δημοσίευσης | 2021 | - |
| Θεματική Κατηγορία | Fast interpolation | en |
| Θεματική Κατηγορία | Big data | en |
| Θεματική Κατηγορία | Kernel function | en |
| Θεματική Κατηγορία | Statistical learning | en |
| Θεματική Κατηγορία | Gaussian Markov random fields | en |
| Θεματική Κατηγορία | Natural resources estimation | en |
| Βιβλιογραφική Αναφορά | D. 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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