지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
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이 논문의 연구 히스토리 (3)
2014
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In the present study, a few of recently developed geostatistical models are comparatively studied. The models are two-point statistics based sequential indicator simulation (SISIM) and generalized coupled Markov chain (GCMC), multi-point statistics single normal equation simulation (SNESIM), and object based model of FLUVSIM (fluvial simulation) that predicts structures of target object from the provided geometric information. Out of the models, SNESIM and FLUVSIM require additional information other than conditioning data such as training map and geometry, respectively, which generally claim demanding additional resources. For the comparative studies, three-dimensional fluvial reservoir model is developed considering the genetic information and the samples, as input data for the models, are acquired by mimicking realistic sampling (i.e. random sampling). For SNESIM and FLUVSIM, additional training map and the geometry data are synthesized based on the same information used for the objective model. For the comparisons of the predictabilities of the models, two different measures are employed. In the first measure, the ensemble probability maps of the models are developed from multiple realizations, which are compared in depth to the objective model. In the second measure, the developed realizations are converted to hydrogeologic properties and the groundwater flow simulation results are compared to that of the objective model. From the comparisons, it is found that the predictability of GCMC outperforms the other models in terms of the first measure. On the other hand, in terms of the second measure, the both predictabilities of GCMC and SNESIM are outstanding out of the considered models. The excellences of GCMC model in the comparisons may attribute to the incorporations of directional non-stationarity and the non-linear prediction structure. From the results, it is concluded that the various geostatistical models need to be comprehensively considered and comparatively analyzed for appropriate characterizations.
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참고문헌 (20)
참고문헌 신청
Arpat, G.B / 2005 / Sequential simulation with patterns
Carle, S.F / 1999 / T-PROGS: transition probability geostatistical software, Version 2.1, User Manual. Davis / Hydrologic Sciences Graduate Group, University of California :248
Carle, S.F / 1996 / Transition probability-based indicator geostatistics / Math. Geol 28(4):453~477
Deutsch, C.V / 1998 / GSLIB: Geostatistical Software Library and User’s Guide / Oxford University Press :369
Deutsch, C.V / 2002 / FLUVSIM: a program for object-based stochastic modeling of fluvial depositional systems / Comput. Geosci 28:525~535
Carle, S.F / 1999 / T-PROGS: transition probability geostatistical software, Version 2.1, User Manual. Davis / Hydrologic Sciences Graduate Group, University of California :248
Carle, S.F / 1996 / Transition probability-based indicator geostatistics / Math. Geol 28(4):453~477
Deutsch, C.V / 1998 / GSLIB: Geostatistical Software Library and User’s Guide / Oxford University Press :369
Deutsch, C.V / 2002 / FLUVSIM: a program for object-based stochastic modeling of fluvial depositional systems / Comput. Geosci 28:525~535
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