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楼主: Destruct
发表于 2025-3-30 10:43:54 | 显示全部楼层
,Die Drüsen mit innerer Sekretion,conductivity data) and information about the state of the system (e.g., piezometric head or concentration data). The problem faced is how to generate multiple realizations conditioned (to parameter data) and inverse-conditioned (to dependent state data) over a large domain with high resolution. Trad
发表于 2025-3-30 14:37:28 | 显示全部楼层
发表于 2025-3-30 17:51:05 | 显示全部楼层
,Die Drüsen mit innerer Sekretion,-point geostatistics is addressed. This study demonstrates how a training image can be constructed based on geological and hydrogeological field data and how multiple-point geostatistics can be applied to determine the impact of complex geological heterogeneity on groundwater flow and transport in a
发表于 2025-3-30 22:30:39 | 显示全部楼层
发表于 2025-3-31 01:09:41 | 显示全部楼层
https://doi.org/10.1007/978-3-662-30055-8n the contamination is related with depositional sediments in water channels. Hence, this paper proposes using bi-point statistics stochastic simulation with local anisotropy trends to simulate continuous variables inside predefined channels. To accomplish this objective, the Direct Sequential Simul
发表于 2025-3-31 05:32:31 | 显示全部楼层
发表于 2025-3-31 11:40:12 | 显示全部楼层
Meldepflicht von Erbkrankheiten,ticular related with natural resources management and anthropogenic pollution characterization. Contaminated sites, air pollution and polluted water characterization are among the most serious environmental problems for which characterization and management, geostatistical methods play an important
发表于 2025-3-31 14:53:30 | 显示全部楼层
发表于 2025-3-31 18:37:33 | 显示全部楼层
发表于 2025-4-1 01:21:17 | 显示全部楼层
Geostatistical Modelling of Wildlife Populations: A Non-stationary Hierarchical Model for Count Datl structures (i.e. trends, patchiness and random fluctuations). A common characteristic of animal count data is a distribution that is both zero-inflated and heavy tailed. In such cases, empirical variograms are no more robust and most structural analyses result in poor and noisy estimated spatial v
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