FuzzyKrig: a comprehensive matlab toolbox for geostatistical estimation of imprecise information

Using kriging has been accepted today as the most common method of estimating spatial data in such different fields as the geosciences. To be able to apply kriging methods, it is necessary that the data and variogram model parameters be precise. To utilize the imprecise (fuzzy) data and parameters, use is made of fuzzy kriging methods. Although it has been 30 years since different fuzzy kriging algorithms were proposed, its use has not become as common as other kriging methods (ordinary, simple, log, universal, etc.); lack of a comprehensive software that can perform, based on different fuzzy kriging algorithms, the related calculations in a 3D space can be the main reason. This paper describes an open-source software toolbox (developed in Matlab) for running different algorithms proposed for fuzzy kriging. It also presents, besides a short presentation of the fuzzy kriging method and introduction of the functions provided by the FuzzyKrig toolbox, 3 cases of the software application under the conditions where: 1) data are hard and variogram model parameters are fuzzy, 2) data are fuzzy and variogram model parameters are hard, and 3) both data and variogram model parameters are fuzzy.     

Classification of rocks radionuclide data using machine learning techniques

The aim of this study is to assess the performance of linear discriminate analysis, support vector machines (SVMs) with linear and radial basis, classification and regression trees and random forest (RF) in the classification of radionuclide data obtained from three different types of rocks. Radionuclide data were obtained for metamorphic, sedimentary and igneous rocks using gamma spectroscopic method. A P-type high-purity germanium detector was used for the radiometric study. For analysis purpose, we have determined activity concentrations of ²³²Th, ²²⁶Ra and ⁴⁰K radionuclides, published elsewhere (Rafique et al. in Russ Geol Geophys 55:1073–1082, 2014), in different rock samples and built the classification model after pre-processing the data using three times tenfold cross-validation. Using this model, we have classified the new samples into known categories of sedimentary, igneous and metamorphic. The statistics depicts that RF and SVM with radial kernel outperform as compared to other classification methods in terms of error rate, area under the curve and with respect to other performance measures.     

Review Paper: Methods of measurement for 4D seismic post-stack time shifts

The estimation of time-lapse time shifts between two, or several, repeated seismic surveys has become increasingly popular over the past eighteen years. These time shifts are a reliable and informative seismic attribute that can relate to reservoir production. Correction for these time shifts or the underlying velocity perturbations and/or subsurface displacement in an imaging sense also permits accurate evaluation of time-lapse amplitudes by attempting to decouple the kinematic component. To date, there are approximately thirty methods for time-shift estimation described in the literature. We can group these methods into three main families of mathematical development, together with several miscellaneous techniques. Here we detail the underlying bases for these methods, and the acknowledged benefits and weaknesses of each class of method highlighted. We illustrate this review with a number of time-lapse seismic examples from producing fields. No method is necessarily superior to the others, as its selection depends on ease of implementation, noise characteristics of the field data, and whether the inherent assumptions suit the case in question. However, cross-correlation stands out as the algorithm of choice based on the Pareto principle and waveform inversion the algorithm delivering best resolution. This is a companion study to the previous review of time-shift magnitudes and a discussion of their rock physics basis.     

Pinpointing the key parameters in estimating the bed-load sediment flux in reservoir engineering

When a dam is constructed on a river to store water, sediments transported by the water flow are also stored and reservoir capacity is gradually reduced by sediment accumulation. Prediction of sediment distribution in reservoirs is an important issue for dam designers to determine the reservoir active storage capacity, outlet sill elevation, dam stability, recreational facilities, and backwater conditions. The main objective of this study is to develop the most reliable parameters of sedimentation that are directly or indirectly influencing in the equations and measured dataset. For validation of the proposed parameters, data of 40 reservoir sets gathered from different reliable sources, rather than focusing entirely on bed-load equations. Artificial neural network (ANNs) method was used to validate this study. Several graphs and statistical analysis were presented to emphasize the influencing effect of those parameters that were detected by ANNs and are directly controlling the error in the bed-load sediment flux using measured particles datasets.     

Optimally locating additional drill holes in three dimensions using grade and simulated annealing

Average kriging variance is a standard tool used in optimization of the location of additional drill holes. However, this tool cannot distinguish between areas with different priorities. This limitation could be eliminated by using weighted average kriging variance. This paper extends the problem of optimal location to three dimensional cases, use grade as a weight and search optimum locations by simulated annealing. Weighted average kriging variance is used as objective function. The method is applied to a copper deposit. Results have shown that weighting of the estimation variance with ??grade?? is effective only when the difference among the grades estimated for different blocks is considerable.     

Monte Carlo analysis of the effect of spatial distribution of storms on prioritization of flood source areas

Implementation of structural and non-structural flood control measures in flood-prone watersheds is on increasing demand. Different watershed areas are not necessarily hydrologically similar and impose variable effects on the outlet flow hydrograph. Meanwhile, prioritization of watershed areas in terms of flood generation is essential for economic flood control planning. Previous works have focused on the definition of a flood index that quantifies the contribution of each subwatershed unit or grid cell to the outlet flood hydrograph through the application of unit flood response (UFR) approach. In the present research, for the first time, the effect of spatial pattern of storm events on the flood index variation was assessed via a Monte Carlo uncertainty analysis. To do so, the UFR approach was carried out for a large number of randomly generated rainfall spatial pattern. The proposed methodology was adopted to the Tangrah watershed in northern Iran. The watershed is frequently hit by floods that have historically caused loss of life and properties. The results indicated that for the more frequent flood events, the flood index is quite sensitive to the spatial distribution of rainfall such that for the highest ranked subwatershed (SW6), the standardized variation of the flood index values (i.e., the uncertainty range) decreases from 1.0 to 0.5 when the rainfall depth increases from 20 to 150 mm, respectively. The results further revealed that increasing the rainfall depth from 20 to 150 mm would cause the effect of rainfall spatial distribution on subwatersheds’ flood indices to diminish. The implications are that if flood control measures are designed for more frequent floods with lower return periods, an uncertainty analysis is required to identify the range of flood index variations.     

Evaluating the accuracy and uncertainty of atmospheric and wave model hindcasts during severe events using model ensembles

Ocean Dynamics - Various uncertainties exist in a hindcast due to the inabilities of numerical models to resolve all the complicated atmosphere-sea interactions, and the lack of certain ground...     

Normalized residual displacement spectra for post-mainshock assessment of structures subjected to aftershocks

Residual displacement, as a significant measure of structural inelasticity, is effectively used in post-earthquake seismic assessment of structures. This demand can be considered for seismic evaluation of structures under multiple earthquakes. This study introduces a simple and novel index to predict the residual displacement of mainshock-damaged structures against subsequent aftershock. The proposed index is defined as a ratio between residual displacement of damaged structures against aftershock and peak inelastic displacement of intact structures under mainshock. In this study, constantstrength spectra based on the index are developed considering the effects of important structural characteristics and also significant seismic parameters. Moreover, analytical equations are presented to predict the proposed index for bi-linear single-degree-of-freedom(SDOF) systems considering both the effects of positive and negative polarities of aftershock. Furthermore, an equation is suggested to estimate the peak inelastic displacement of intact systems under mainshock, which is required to compute the index.