A new method for determining geochemical anomalies: U-N and U-A fractal models

Undoubtedly, determining the threshold of anomalies and separating geochemical anomalies from background is one of the most important stages of minerals exploration. In the discussion of the separation of geochemical anomalies from background, there are different methods that structural methods have shown much greater efficiency than nonstructural methods. Among structural methods (methods that consider the position and location of samples), U-statistic and fractal methods have a special place. In this study, by using the algorithm of the abovementioned methods and combining them, a new method as U values fractal model (U-N and U-A) is introduced for the first time. Then, the proposed method is employed to determine the boundaries of background and anomalous populations (about the gold (Au) and arsenic (As) elements in Susanvar district). Results show that in U-N and U-A fractal models, the first fracture boundary is much clearer and more accurate than previous fractal models (C-N and C-A) in the same condition. In U-N model, due to the nature of the U method algorithm, there is a discontinuity as the exact threshold between background and anomaly that in U-A model, this does not exist due to the homogenization of U values. In this method, the exact threshold between background and anomaly is determined by the U-statistic method and by its combination with the fractal method, in each population, sub-populations are identified more accurately and simply than the concentration fractal model. Finally, a lithogeochemical map of the study area is provided for Au and As which has been prepared using U-N and U-A fractal methods. In these maps (especially the prepared maps by U-A model), the delineated Au-As mineralization is closely associated with the defined Au ore indications in the study area

Dynamic vehicle routing problems: Three decades and counting

Since the late 70s, much research activity has taken place on the class of dynamic vehicle routing problems (DVRP), with the time period after year 2000 witnessing a real explosion in related papers. Our paper sheds more light into work in this area over more than 3 decades by developing a taxonomy of DVRP papers according to 11 criteria. These are (1) type of problem, (2) logistical context, (3) transportation mode, (4) objective function, (5) fleet size, (6) time constraints, (7) vehicle capacity constraints, (8) the ability to reject customers, (9) the nature of the dynamic element, (10) the nature of the stochasticity (if any), and (11) the solution method. We comment on technological vis-à-vis methodological advances for this class of problems and suggest directions for further research. The latter include alternative objective functions, vehicle speed as decision variable, more explicit linkages of methodology to technological advances and analysis of worst case or average case performance of heuristics.© 2015 Wiley Periodicals, Inc

Combination of separation methods and data mining techniques for prediction of anomalous areas in Susanvar, Central Iran

© 2017 Elsevier Ltd Structural method U-statistics is an eminent technique for delineating geochemical patterns; on the other hand, it is worthwhile to introduce Mahalanobis distance approach decreasing the background effects and intensifying the correlation factor of points as a powerful non-structural method. Undoubtedly, predicting the anomalous values could play an important role in the inchoate stages of exploration. Therefore, it is essential to find the most accurate approach to separate anomalous values from background and afterward use the results to anticipate each arbitrary sample. In this study, results of the combination between U-statistics & Mahalanobis distance algorithms are used to distinguish anomalous values from background on an accurate point of view. Then, three data mining methods will be applied to produce practical equations and finally determine anomalous values. Separation of geochemical anomalies, based on the combination of the U-statistics and the Mahalanobis distance approaches, would be done; then, under the influence of their results and the other parameters – x and y coordinates and Au and As grades - three data mining methods, K nearest neighbor (K-NN), decision tree, and naïve Bayes classifier, have been applied. For this purpose after separation of anomalous values according to the number of 603 samples by applying above combination, the data mining methods would be utilized to anticipate anomalous values for each unknown point. Finally, in order to judge about the designed networks, training samples would be considered as test samples under the application of the networks. Therefore according to the results, decision tree method would appear as the more powerful approach than the other due to far fewer number of wrong estimated samples and approving high accuracy of the designed network, that is, resubstitution error for this network is noted only 0.0232. Noteworthy is that the numbers of wrong estimated samples are 30 and 61 and the rates of error are 0.0498 and 0.1 for K-NN and naïve Bayes methods respectively. So needless to say that the combination of decision tree method and the introduced anomaly separation approach is much more remarkable as a reliable and efficient technique to approach worthwhile predictions