CHDS: conflict handling in direct sampling for stochastic simulation of spatial variables

Mohammadi, Hesam; Abdollahifard, Mohammad Javad; Ardejani, Faramarz Doulati

doi:10.1007/s00477-020-01801-4

Cited by 3 publications

(2 citation statements)

References 49 publications

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“…In 2019, Zuo et al [34] suggested that adopting a correlation-driven DS algorithm will improve the accuracy of simulated pixel values. In 2020, Mohammadi et al [35] and Zuo et al [36] had, respectively, developed a conflict-handling DS and a tree-based DS. A conflict is defined as a data event (pattern) from the target image that has no perfect match in the TI.…”

Section: Introductionmentioning

confidence: 99%

A Modified Version of the Direct Sampling Method for Filling Gaps in Landsat 7 and Sentinel 2 Satellite Imagery in the Coastal Area of Rhone River

Farhat,

Manakos,

Sylaios

et al. 2023

Remote Sensing

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Earth Observation (EO) data, such as Landsat 7 (L7) and Sentinel 2 (S2) imagery, are often used to monitor the state of natural resources all over the world. However, this type of data tends to suffer from high cloud cover percentages during rainfall/snow seasons. This has led researchers to focus on developing algorithms for filling gaps in optical satellite imagery. The present work proposes two modifications to an existing gap-filling approach known as the Direct Sampling (DS) method. These modifications refer to ensuring the algorithm starts filling unknown pixels (UPs) that have a specified minimum number of known neighbors (Nx) and to reducing the search area to pixels that share similar reflectance as the Nx of the selected UP. Experiments were performed on images acquired from coastal water bodies in France. The validation of the modified gap-filling approach was performed by imposing artificial gaps on originally gap-free images and comparing the simulated images with the real ones. Results indicate that satisfactory performance can be achieved for most spectral bands. Moreover, it appears that the bi-layer (BL) version of the algorithm tends to outperform the uni-layer (UL) version in terms of overall accuracy. For instance, in the case of B04 of an L7 image with a cloud percentage of 27.26%, accuracy values for UL and BL simulations are, respectively, 64.05 and 79.61%. Furthermore, it has been confirmed that the introduced modifications have indeed helped in improving the overall accuracy and in reducing the processing time. As a matter of fact, the implementation of a conditional filling path (minNx = 4) and a targeted search (n2 = 200) when filling cloud gaps in L7 imagery has contributed to an average increase in accuracy of around 35.06% and an average gain in processing time by around 78.18%, respectively.

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Section: Introductionmentioning

confidence: 99%

A Modified Version of the Direct Sampling Method for Filling Gaps in Landsat 7 and Sentinel 2 Satellite Imagery in the Coastal Area of Rhone River

Farhat,

Manakos,

Sylaios

et al. 2023

Remote Sensing

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“…Characterization of connectivity is key to quantify the degree of flow heterogeneity and the ensuing early (or late) breakthrough of dissolved chemicals at critical targets (Cvetkovic et al 2014;Henri et al 2015;Zinn and Harvey 2003), with direct implications in several contexts, including, e.g., remediation of contaminated sites and environmental risk assessment. Increasing efforts have been devoted to investigate the effects of various geostatistical approaches on connectivity metrics (Bakshevskaia and Pozdniakov 2016;Dell'Arciprete et al 2012Lee et al 2007;Mohammadi et al 2020;Sharifzadehlari et al 2018;Vassena et al 2010). Other studies addressed the impact different geostatistical methods on the accuracy of estimated facies distribution (He et al 2009, Kessler et al 2013Marini et al 2019;Park et al 2007), hydraulic head and flux fields (Lee et al 2007;Bianchi et al 2015), or spreading of dissolved chemicals migrating in aquifer systems (Maghrebi et al 2015;Siirila-Woodburn and Maxwell 2015).…”

Section: Introductionmentioning

confidence: 99%

Impact of geostatistical reconstruction approaches on model calibration for flow in highly heterogeneous aquifers

Siena

Riva

2020

Stoch Environ Res Risk Assess

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Our study is aimed at assessing the extent at which relying on differing geostatistical approaches may affect characterization of the connectivity of geomaterials (or facies) and, in turn, model calibration outputs in highly heterogeneous aquifers. We set our study within a probabilistic framework, by relying on a numerical Monte Carlo (MC) approach. The reconstruction of the spatial distribution of geomaterials and flow simulations are patterned after a field scenario corresponding to the aquifer system serving the city of Bologna (Northern Italy). Two collections of MC realizations of facies distributions, conditional on available lithological data, are generated through two alternative geostatistically-based techniques, i.e., Sequential Indicator and Transition-Probability simulation. Hydraulic conductivity values of the least- and most-conductive facies are estimated within each MC simulation in the context of a Maximum Likelihood (ML) approach by considering available piezometric data. We provide evidence that the choice of the facies reconstruction technique (1) impacts the degree of connectivity of facies whose proportions are close to the percolation threshold while (2) is not sensibly affecting the connectivity associated with facies whose proportions are much larger than the percolation threshold. By relying on the unique (lithological and hydrological) data-set at our disposal, we also explore the performance of ML-based model identification criteria to (1) discriminate amongst competitive facies reconstruction geostatistical models and (2) quantify the (posterior probabilistic) weight associated with each model. We then show that ML-based model averaging provides estimates of hydraulic heads which are slightly more in agreement with available data when compared to the best-performing realization in the T-PROGS set than considering its counterpart associated with the SISIM-based collection.

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A tutorial on reproducing a predefined autocovariance function through AR models: application to stationary homogeneous isotropic turbulence

Gallego-Castillo

Cuerva-Tejero

Elagamy

et al. 2021

Stoch Environ Res Risk Assess

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Sequential methods for synthetic realisation of random processes have a number of advantages compared with spectral methods. In this article, the determination of optimal autoregressive (AR) models for reproducing a predefined target autocovariance function of a random process is addressed. To this end, a novel formulation of the problem is developed. This formulation is linear and generalises the well-known Yule-Walker (Y-W) equations and a recent approach based on restricted AR models (Krenk-Møller approach, K-M). Two main features characterise the introduced formulation: (i) flexibility in the choice for the autocovariance equations employed in the model determination, and (ii) flexibility in the definition of the AR model scheme. Both features were exploited by a genetic algorithm to obtain optimal AR models for the particular case of synthetic generation of homogeneous stationary isotropic turbulence time series. The obtained models improved those obtained with the Y-W and K-M approaches for the same model parsimony in terms of the global fitting of the target autocovariance function. Implications for the reproduced spectra are also discussed. The formulation for the multivariate case is also presented, highlighting the causes behind some computational bottlenecks.

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CHDS: conflict handling in direct sampling for stochastic simulation of spatial variables

Cited by 3 publications

References 49 publications

A Modified Version of the Direct Sampling Method for Filling Gaps in Landsat 7 and Sentinel 2 Satellite Imagery in the Coastal Area of Rhone River

A Modified Version of the Direct Sampling Method for Filling Gaps in Landsat 7 and Sentinel 2 Satellite Imagery in the Coastal Area of Rhone River

Impact of geostatistical reconstruction approaches on model calibration for flow in highly heterogeneous aquifers

A tutorial on reproducing a predefined autocovariance function through AR models: application to stationary homogeneous isotropic turbulence

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