Fast direct sampling for multiple-point stochastic simulation

Multiple-point statistics simulation has recently attracted significant attention for the simulation of complex geological structures. In this paper, a fast direct sampling (FDS) algorithm is presented based on a fast gradient descent pattern matching strategy. The match is directly extracted from the training image (TI) and so the method does not require intensive preprocessing and database storage. The initial node of the search path is selected randomly but the following nodes are selected in a principled manner so that the path is conducted to the right match. Each node is selected based on the matching accuracy and the behavior of the TI in the previous node. A simple initialization strategy is presented in this paper which significantly accelerates the matching process at the expense of a very naïve preprocessing stage. The proposed simulation algorithm has several outstanding advantages: it needs no (or very limited) preprocessing, does not need any database storage, searches for the match directly in the TI, is not limited to fixed size patterns (the pattern size can be easily changed during simulation), is capable of handling both continuous and categorical data, is capable of handling multivariate data, and finally and more importantly, is a fast method while maintaining high standards for the matching quality. Experiments on different TIs reveal that the simulation results of FDS and DS are comparable in terms of pattern reproduction and connectivity while FDS is far faster than DS.     

Hierarchical approach to hydrological model calibration

Hydrological models have been widely used for water resources management. Successful application of hydrological models depends on careful calibration and uncertainty analysis. Spatial unit of water balance calculations may differ widely in different models from grids to hydrological response units (HRU). The Soil and Water Assessment Tool (SWAT) software uses HRU as the spatial unit. SWAT simulates hydrological processes at sub-basin level by deriving HRUs by thresholding areas of soil type, land use, and slope combinations. This may ignore some important areas, which may have great impact on hydrological processes in the watershed. In this study, a hierarchical HRU approach was developed in order to increase model performance and reduce computational complexity simultaneously. For hierarchical optimization, HRUs are first divided into two-HRU types and are optimized with respect to some relevant influence parameters. Then, each HRU is further divided into two. Each child HRU inherits the optimum parameter values of the parent HRU as its initial value. This approach decreases the total calibration time while obtaining a better result. The performance of the hierarchical methodology is demonstrated on two basins, namely Sarisu-Eylikler and Namazgah Dam Lake Basins in Turkey. In Sarisu-Eylikler, we obtained good results by a combination of curve number (CN2), soil hydraulic conductivity, and slope for generating HRUs, while in Namazgah use of only CN2 gave better results.     

Hyperspectral image betweenness centrality clustering based adaptive spatial and spectral neighborhood approach for anomaly detection

Segmentation-based anomaly detectors proceeds to the clustering of the hyperspectral image as a first step. However, most of the well-known clustering methods cluster anomalous pixels as a part of the background. This paper presents a new hyperspectral image clustering approach based on the betweenness centrality measure. The proposed approach starts by the construction of an adaptive spatial and spectral neighborhood for each pixel. This neighborhood is based on the selection of the nearest spectral and spatial neighbors in multiple windows around each pixel to allow well-suited representation of the image features. In the next step, this neighborhood is clustered based on the edge betweenness measure algorithm that splits the image into regions sharing similar features. This approach (1) allows the reduction of intercluster relationship, (2) favors intracluster relations, and (3) preserves small clusters that can hold anomalous pixels. Experimental results show that the proposed approach is efficient for clustering and overcomes the state of the art approaches.     

Shoreline change detection using DSAS technique: Case of North Sinai coast,Egypt

This study focuses on the shoreline change detection along the North Sinai coast in Egypt using geographic information system and digital shoreline analysis system (DSAS) during the elapsed period from 1989 to 2016. The measurement of shoreline variation is mainly described for three zones: zone I, El-Tinah plain bay; zone II, El-Bardawil Lake; zone III, El-Arish valley. The rates of shoreline changes in the form of erosion and accretion patterns are automatically quantified by four statistical parameters functioned in DSAS namely endpoint rate, net shoreline movement, linear regression rate (LRR), and least median of squares. LRR results elucidate that the western seaside of El-Tinah plain bay has experienced an extremely dynamic feature with an average erosion rate of ?8.17?m/year. The littoral drifts have been driven by eastward alongshore currents toward the east side of the bay to be accreted with an average rate of +4.28?m/year. Moreover, the shoreline has progressed west of El-Bardawil inlet (1), El-Bardawil inlet (2), and El-Arish harbor. Subsequently, the corresponding average beach growth rates are found to be +2.7, +8.5, and +6.5?m/year, respectively. In contrast, the shoreline on the down-drift side to the east has negatively retreated, and the corresponding beaches have regressed at rates of ?4.5, ?8.65, and ?2.9?m/year, respectively.     

Finite strain analysis for deformed Neoproterozoic rock associations of the Al Amar fault zone,Eastern Arabian Shield,Saudi Arabia

Arabian Journal of Geosciences - In this work, quantitative analysis of coastal zone landform morphology is carried out through the integration of landscape index, remote sensing (RS) and...     

Long-term changes in fish communities of the Ria Formosa coastal lagoon (southern Portugal) based on two studies made 20 years apart

Fish communities of the Ria Formosa coastal lagoon (south Portugal) were sampled on a monthly basis with a beach seine at 4 sites, during two different time periods: 1980–1986 and 2001–2002. Community indices, species ranking and multivariate analysis were used in order to identify changes in the fish community between the two time periods. A total of 153,511 fish representing 57 taxa were recorded. Although species composition was very similar for both sampling periods, multivariate analysis performed on annual species abundance in number and weight revealed differences in fish community structure between the two periods. Structural changes in fish community were related mostly to a sharp decrease in the abundance of Mugilidae from 1980–1986 to 2001–2002. These changes were probably associated to a decrease in organic matter contents and nutrients concentrations due to improvements in sewage treatment and better water circulation inside the lagoon. The changes in fish community structure are more evident in the inner areas of the lagoon than near the inlet. The association between changes in sewage patterns and changes in the ichthyofaunal community structure reinforces the importance of fish communities as a biological indicator of human induced changes in marine systems.