The major problems in modeling of different oceanographic and meteorological parameters are limitations in numerical methods and human incomplete knowledge in physical processes involved. As a result, significant differences between the results of these models and in situ observations of these parameters might exist. One of the powerful solutions for decreasing the forecast errors in the models is to use data assimilation technique. In this study the optimal interpolation data assimilation method is employed which is based on statistical rules. Moreover, the quick Canadian method is used to optimize the data assimilation method used in model. Model assessment is performed by comparison between running wave model with and without using data assimilation and SAR wave data in Persian Gulf. It shows that using data assimilation in WAVEWATCH-III model reduces the error in wave height predictions significantly.

The extensions of the Riemannian structure include the Finslerian one, which provided in recent years successful models in various , elds like Biology, Physics, GTR, Monolayer Nanotechnology and Geometry of Big data. The present article provides the necessary notions on tensor Spectral data and on the HO-SVD and the Candecomp tensor decompositions, and further study several aspects related to the Spectral theory of the main symmetric Finsler tensors, the fundamental and the Cartan tensor. In particular, are addressed two Finsler models used in Langmuir-Blodgett Nanotechnology and in Oncology. As well, the HO-SVD and Candecomp decompositions are exempli, ed for these models and metric extensions of the eigenproblem are proposed.

In this paper, with due respect to the original data and based on the extraction of new features by smaller dimensions, a new feature reduction technique is proposed for Hyper-Spectral data classification. For each pixel of a Hyper-Spectral image, a specific rational function approximation is developed to fit its own Spectral response curve (SRC) and the coefficients of the numerator and denominator polynomials of this function are considered as new extracted features. The method focuses on geometrical nature of SRCs and relies on the fact that the sequence discipline-ordinance of reflectance coefficients in Spectral response curve-contains some information which has not been addressed by many other existing methods based on the statistical analysis of data. Maximum likelihood classification results demonstrate that our method provides better classification accuracies in comparison with many competing feature extraction algorithms. In addition, the proposed algorithm has the possibility of being applied to all pixels of image individually and simultaneously as well.

Summary: Spectral Induced Polarization (SIP) is widely used for environmental and hydrogeophysics, but one major limitation concerns the electromagnetic (EM) coupling effect. In this study, an overview of the mutual impedance of a polarizable multilayered homogeneous half-space is done (it consist both of electromagnetic coupling and the Spectral Induced Polarization effects). Then, the effect of electrode array parameters and the subsurface electrical resistivity on mutual impedance response is investigated. The results show the EM coupling effect can be seen at frequencies less than 10 Hz. It will increase with increasing frequency of excitation current and the conductivity. So by choosing the optimal arrangement for current cable, SIP can be surveyed using electrode arrays such as Schlumberger electrode array (with higher signal to noise ratio) and the EM coupling effect is also reduced as much as possible. Introduction: Induced polarization (IP) is the main geophysical method in mineral deposits prospecting. Spectral induced polarization (SIP), an extension of the IP method, in the past few decades has been used extensively in mineral prospecting and increasingly in environmental investigations, hydro-geophysics, archaeo-geophysics, bio-geophysics. SIP measurements are very sensitive to the low frequency capacitive properties of rocks and soils. One of the major limitations of SIP method is the EM coupling effect. In SIP method, the amplitude and phase components of the earth’s resistivity are measured in a frequency range (typically 0.001 Hz to 10 kHz). At low frequencies, the inductive coupling effect may affect the spectrum ohmic responses and normal polarization effect of the subsurface material. In SIP, there are three types of the EM coupling effect: the first is the removal of the EM coupling effect from SIP field data. In the second type, the mutual impedance of the earth is calculated using Cole-Cole equation as IP dispersion of the earth. SIP data and mutual impedance are compared using an inversion algorithm in order to recover the earth IP parameters. In this method, since the SIP method employs alternative fields using grounded wires, it should be characterized as an EM method. The third uses a current cable arrangement in order to reduce the EM coupling effects from SIP data. Due to the increasing application of SIP method during the recent years, investigating and finding solutions to its limits is necessary. Because in this method, time-varying current signal is used, electromagnetic coupling occurs between the Earth, current cable and potential cable and electrical response affected by this phenomenon. So the calculation of earth's mutual impedance response and the separation of earth's electrical and electromagnetic responses are necessary. Methodology and Approaches: In this study, an overview of the electromagnetic coupling effect on the Spectral Induced Polarization data and calculating this effect on the total response using linear dipole-dipole electrode array with dipole arbitrary length on a polarizable multilayered homogeneous half-space is done. Then, the effect of electrode array parameters and the subsurface electrical resistivity on mutual impedance response is investigated. We used CR1D mod code in order to calculate the mutual impedance of the earth (it consists both of SIP and electromagnetic response of the earth). At the end, the effect of current cable arrangement on electromagnetic coupling and mutual impedance response is investigated.Results and Conclusions: The results show that the electromagnetic coupling effect can be seen at frequencies less than 10 Hertz. The induced impedance response will increase by increasing the survey's dipoles length as well as increasing the distance between the two consecutive dipoles. So we can conclude, when electrode array is pole-dipole, pole-dipole and linear Schlumbeger, the electromagnetic coupling effect will be much larger when electrode array is dipole-dipole; if the rectangular arrangement is chosen for current cable, the Schlumberger electrode array with low signal to noise ratio is used for SIP survey. On the other hand, the EM coupling effect will increase with increasing frequency of excitation current and the conductivity. So by choosing the optimal arrangement for current cable, SIP can be surveyed using electrode arrays such as Schlumberger electrode array and the EM coupling effect is also reduced as much as possible.

Seismic waves are nonstationary signals because their frequency contents are changed when they propagate through layers of different elastic properties. Hydrocarbon reservoirs and fault zone are two major sources of attenuating high frequency components of a seismic wave. Dropping of higher frequency components of a seismic wave while passing through a hydrocarbon reservoir appears as low frequency shadow zone on a seismic section.In this study, we used instantaneous Spectral attributes of real seismic data in time-frequency domain in order to trace the existence of hydrocarbon reservoir and fault zone in the study area. For this purpose, we used short time Fourier transform and continuous wavelet transform. In order to reduce the run time, the instantaneous frequency was calculated directly from scalograms.

Introduction: Understanding the spatial distribution of soil organic carbon (SOC) is one of the practical tools in determining sustainable land management strategies. Over the past two decades, the use of data mining approaches in spatial modeling of soil organic carbon using machine learning techniques to investigate the amount of carbon to soil using remote sensing data has been widely considered. Accordingly, the aim of this study was to investigate the feasibility of estimating soil organic matter using satellite imagery and to assess the ability of Spectral and terrestrial data to model the amount of soil organic matter. Materials and Methods: The study area is located in Lorestan province, and Sarab Changai area. This area has hot and dry summers and cold and wet winters and the wet season starts in November and ends in May. A total of 156 samples of surface soil (0-30 cm) were collected using random sampling pattern. data were categorized into two categories: 80% (117 points) for training and 20% (29 points) for validation. Three machine learning algorithms including Random Forest (RF), Cubist, and Partial least squares regression (PLSR) were used to prepare the organic soil carbon map. In the present study, auxiliary variables for predicting SOC included bands related to Lands 8 OLI measurement images, and in order to reduce the volume of data, the principle component analysis method (PCA) was used to select the features that have the greatest impact on quality. Results and Discussion: The results of descriptive statistics showed that soil organic carbon from 0. 02 to 2. 34% with an average of 0. 56 and a coefficient of variation of 69. 64% according to the Wilding standard was located in a high variability class (0. 35). According to the average amount of soil organic carbon, it can be said that the amount of soil organic carbon in the region is low. At the same time, the high value of organic carbon change coefficient confirms its high spatial variability in the study area. These drastic changes can be attributed to land use change, land management, and other environmental elements in the study area. In other words, the low level of soil organic carbon can be attributed to the collection of plant debris and their non-return to the soil. Another factor in reducing the amount of organic carbon is land use change, which mainly has a negative impact on soil quality and yield. In general, land use, tillage operations, intensity and frequency of cultivation, plowing, fertilizing, type of crop, are effective in reducing and increasing the amount of soil organic carbon. Based on the analysis of effective auxiliary variables in predicting soil organic carbon, based on the principle component analysis for remote sensing data, it led to the selection of 4 auxiliary variables TSAVI, RVI, Band10, and Band11 as the most effective environmental factors. Comparison of different estimation approaches showed that the random forest model with the values of coefficient of determination (R 2 ), root mean square error (RMSE) and mean square error (MSE) of 0. 74, 0. 17, and 0. 02, respectively, was the best performance ratio another study used to estimate the organic carbon content of surface soil in the study area. Conclusion: In this study, considering the importance of soil organic carbon, the efficiency of three different digital mapping models to prepare soil organic carbon map in Khorramabad plain soils was evaluated. The results showed that auxiliary variables such as TSAVI, RVI, Band 10, and Band11 are the most important variables in estimating soil organic carbon in this area. The wide range of soil organic carbon changes can be affected by land use and farmers' managerial behaviors. Also, the results indicated that different models had different accuracy in estimating soil organic carbon and the random forest model was superior to the other models. On the other hand, it can be said that the use of remote sensing and satellite imagery can overcome the limitations of traditional methods and be used as a suitable alternative to study carbon to soil changes with the possibility of displaying results at different time and space scales. Due to the determination of soil organic carbon content and their spatial distribution throughout the region, the present results can be a scientific basis as well as a suitable database and data for the implementation of any field operations, management of agricultural inputs, and any study in sustainable agriculture with soil properties in this area. In general, the results of this study indicated the ability of remote sensing techniques and random forest learning model in simultaneous estimation of soil organic carbon location. Therefore, this method can be used as an alternative to conventional laboratory methods in determining some soil characteristics, including organic carbon.

One of the apparent characteristics of soil is color which shows high correlation with soil characteristics and Spectral reflectance. Soil color is identified using visual comparison of sample and colored chips of Munsell color charts .In arid regions due to the prolonged sunny days, low soil moisture, sparse vegetation cover and close relation between land units and soils, there is an ideal condition for application of remote sensing data especially for study of relation between satellite data and color of surface features. The soil color and the most effective factors on color and Spectral reflectance of soil are explained in brief. Color composite images produced from TM7, TM4 and TM2 as red, green and blue respectively used in order to choose sample sites. The 20 sample sites were chosen based on resample 3×3 pixels (90×90 m) . In each site, the soil surface conditions and the munsell color of the soil surface were investigated in the field. Some physico-chemical properties of soil samples were also determined in the laboratory.The results of this study indicates that munsell notation of hue, value and Chroma are significantly correlated to the visible bands of Landsat (TM) data. From this study it may be concluded that visible reflectance of Landsat can be used to estimate soil color, if very precise result is not expected .More investigation are necessary in order to improve the obtained results.

In this paper the attenuation of Iranian strong motions is studied using Iranian strong motions database. This database comprises more than 6000 well recorded three-components data (analog and digital) for which the teleseismic source parameters were available, or calculated by the records. Here, the one-step regression method is used in order to develop the attenuation model. The Spectral values of the recorded strong motions in Iran are used to derive the empirical attenuation laws for different response Spectral ordinates at different site conditions. The empirical relationships are established for the Spectral acceleration as the function of moment magnitude, hypocentral distances, and constant parameter representing the site conditions. The data set consists of 87 three component accelerograms, all recorded in 1975-2003. In this paper the attenuation coefficients are in general accordance with the previous attenuation coefficients established for Iran. However, the Spectral values, obtained here, are greater in comparison with those gained by the previous studies (1999 and 2006). The difference might be due to selecting greater motions, recorded in the distances nearer to the seismic source.

Due to the increasing information and the detailed analysis of them, the clustering problems that detect the hidden patterns lie in the data are still of great importance. On the other hand, clustering of high-dimensional data using previous traditional methods has many limitations. In this study, a semi-supervised ensemble clustering method is proposed for a set of high-dimensional medical data. In the proposed method of this study, little information is available as prior knowledge using the information on similarity or dissimilarity (as a number of pairwise constraints). Initially using the transitive property, we generalize the pairwise constraints to all data. Then we divide the feature space into a number of sub-spaces, and to find the optimal clustering solution, the feature space is divided into an unequal number of sub-spaces randomly. A semi-supervised Spectral clustering based on the p-Laplacian graph is performed at each sub-space independently. Specifically, to increase the accuracy of Spectral clustering, we have used the Spectral clustering method based on the p-Laplacian graph. The p-Laplacian graph is a nonlinear generalization of the Laplacian graph. The results of any clustering solutions are compared with the pairwise constraints and according to the level of matching, a degree of confidence is assigned to each clustering solution. Based on these degrees of confidence, an ensemble adjacency matrix is formed, which is the result of considering the results of all clustering solutions for each sub-space. This ensemble adjacency matrix is used in the final Spectral clustering algorithm to find the clustering solution of the whole sub-space. Since the sub-spaces are generated randomly with an unequal number of features, clustering results are strongly influenced by different initial values. Therefore, it is necessary to find the optimal sub-space set. To this end, a search algorithm is designed to find the optimal sub-space set. The search process is initialized by forming several sets (we call each set an environment) consisting of several numbers of sub-spaces. An optimal environment is the one that has the best clustering results. The search algorithm utilized three search operators to find the optimal environment. The search operators search all the environments and the consequent sub-spaces both locally and globally. These operators combine two environments and/or replace an environment with a newly generated one. Each search operator tries to find the best possible environment in the entire search space or in a local space. We evaluate the performance of our proposed clustering schema on 20 cancer gene datasets. The normalized mutual information (NMI) criterion and the adjusted rand index (ARI) are used to evaluate the performance evaluation. We first examine the effect of a different number of pairwise constraints. As expected, with increasing the number of pairwise constraints, the efficiency of the proposed method also increases. For example, the NMI value increases from 0. 6 to 0. 9 on the Khan-2001 dataset, when the number of pairwise constraints increases from 20 to 100. More number of pairwise constraints means more information is available, which helps to improve the performance of the clustering algorithm. Furthermore, we examine the effect of the number of random subspaces. It is observed that increasing the number of random subspaces has a positive effect on clustering performance with respect to the NMI value. In most datasets, when the number of sub-spaces reaches 20, the performance of the proposed method does not change much and is stable. Examining the effect of sampling rate for random subspace generation shows that the proposed method has the best performance in most cancer datasets, such as Armstrong-2002-v3, and Bredel-2005 datasets, when the random subspace generation rate is 0. 5, and by deviating the rate from 0. 5, the level of satisfaction decreases. Then, the results of the proposed idea are compared with the results of the method proposed in the reference [21] according to ARI and we see that our proposed method has performed better in 12 data sets out of 20 data sets than the method proposed in the reference [21]. Finally, the proposed idea is compared with some metric learning approaches with respect to NMI. We have observed that the proposed method obtained the best results compared to other compared methods on 11 datasets out of 20 datasets. It also achieved the second-best result on 6 out of 20 datasets. For example, the value NMI obtained in the proposed method is 0. 1042 more than the reference [21] and it is 0. 1846 more than RCA and it is 0. 4 more than ITML and also it is 0. 468 more than DCA on the Bredel-2005 dataset. Utilizing ensemble clustering methods besides the confidence factor improves the ability of the proposed algorithm to achieve better results. Also, utilizing the transitive operators as well as the selection of random subspaces of unequal sizes play an important role in achieving better performance for the proposed algorithm. Using the p-Laplacian Spectral clustering method produces a better, more balanced, and normal volume of clusters compared to the standard Spectral clustering. Another effective approach to the performance of the proposed method is to use search operators to find the best subspace, which leads to better results.