A probabilistic tsunami hazard assessment for the Makran subduction zone at the northwestern Indian Ocean

A probabilistic tsunami hazard assessment is performed for the Makran subduction zone (MSZ) at the northwestern Indian Ocean employing a combination of probability evaluation of offshore earthquake occurrence and numerical modeling of resulting tsunamis. In our method, we extend the Kijko and Sellevoll’s (1992) probabilistic analysis from earthquakes to tsunamis. The results suggest that the southern coasts of Iran and Pakistan, as well as Muscat, Oman are the most vulnerable areas among those studied. The probability of having tsunami waves exceeding 5 m over a 50-year period in these coasts is estimated as 17.5%. For moderate tsunamis, this probability is estimated as high as 45%. We recommend the application of this method as a fresh approach for doing probabilistic hazard assessment for tsunamis. Finally, we emphasize that given the lack of sufficient information on the mechanism of large earthquake generation in the MSZ, and inadequate data on Makran’s paleo and historical earthquakes, this study can be regarded as the first generation of PTHA for this region and more studies should be done in the future.     

A new empirical estimator of coseismic landslide displacement for Zagros Mountain region (Iran)

Earthquake-induced landslides are responsible worldwide for significant socioeconomic losses and historically have a prominent position in the list of natural hazards affecting the Iran plateau. As a step toward the development of tools for the assessment and the management of this kind of hazard at regional scale, an empirical estimator of coseismic displacements along potential sliding surfaces was obtained through a regression analysis for the Zagros region, a mountainous Iranian region subjected to earthquake-induced landslides. This estimator, based on the Newmark’s model, allows to evaluate the expected permanent displacement (named “Newmark displacement”) induced by seismic shaking of defined energy on potential sliding surface characterized by a given critical acceleration. To produce regression models for Newmark displacement estimators, a data set was constructed for different critical acceleration values on the basis of 108 accelerometric recordings from 80 Iranian earthquakes with moment magnitudes between 3.6 and 7. The empirical estimator has a general form, proposed by Jibson (Eng Geol 91:209–218, 2007), relating Newmark displacement to Arias intensity (as parameter representing the energy of the seismic forces) and to critical acceleration (as parameter representing the dynamic shear resistance of the sliding mass). As an example of application, this relation was employed to provide a basic document for earthquake-induced landslide hazard assessment at regional scale, according to a method proposed by Del Gaudio et al. (Bull Seismol Soc Am 93:557–569, 2003), applied to the whole Iranian territory, including Zagros region. This method consists in evaluating the shear resistance required to slopes to limit the occurrence of seismically induced failures, on the basis of the Newmark’s model. The obtained results show that the exposure to landslide seismic induction is maximum in the Alborz Mountains region, where critical accelerations up to ~0.1 g are required to limit the probability of seismic triggering of coherent type landslides within 10% in 50 years.     

Microbiostratigraphy of Kazhdumi Formation in the Northwestern Shiraz (Southwest Iran) on the Basis of Foraminifera and Calcareous Algae

The middle Cretaceous Kazhdumi Formation,with a thickness of 222 m,belongs to the Bangestan Group and occurs in the Zagros folded zone in southwest Iran.The lower boundary with the Dariyan Formation is disconformable,which is recognized by iron oxides and glauconite.The recognized microfossils are Valvulammina sp.,Scandonea sp.,Daxia cenomana,Choffatela sp., Pseudolituonella reicheli and calcareous algae-Lithocodium aggregation(which belongs to the Sarvak Formation),representing the beginning of Cenomani...     

A hybrid computational approach to formulate soil deformation moduli obtained from PLT

In this study, new empirical equations were developed to predict the soil deformation moduli utilizing a hybrid method coupling genetic programming and simulated annealing, called GP/SA. The proposed models relate secant (E_s), unloading (E_u) and reloading (E_r) moduli obtained from plate load–settlement curves to the basic soil physical properties. Several models with different combinations of the influencing parameters were developed and checked to select the best GP/SA models. The database used for developing the models was established upon a series of plate load tests (PLT) conducted on different soil types at various depths. The validity of the models was tested using parts of the test results that were not included in the analysis. The validation of the models was further verified using several statistical criteria. A traditional GP analysis was performed to benchmark the GP/SA models. The contributions of the parameters affecting E_s, E_u and E_r were analyzed through a sensitivity analysis. The proposed models are able to estimate the soil deformation moduli with an acceptable degree of accuracy. The E_s prediction model has a remarkably better performance than the models developed for predicting E_u and E_r. The simplified formulations for E_s, E_u and E_r provide significantly better results than the GP-based models and empirical models found in the literature.     

Scaling property of regional floods in New South Wales Australia

Regional flood frequency analysis (RFFA) is often used in hydrology to estimate flood quantiles when there is a limitation of at-site recorded flood data. One of the commonly used RFFA methods is the index flood method, which is based on the assumptions that a region satisfies criterion of simple scaling and it can be treated homogeneous. Another RFFA method is quantile regression technique where prediction equations are developed for flood quantiles of interest as function of catchment characteristics. In this paper, the scaling property of regional floods in New South Wales (NSW) State in Australia is investigated. The results indicate that the annual maximum floods in NSW satisfy a simple scaling assumption. The application of a heterogeneity test, however, reveals that NSW flood data set does not satisfy the criteria for a homogeneous region. Finally, a set of prediction equations are developed for NSW using quantile regression technique; an independent test shows that these equations can provide reasonably accurate design flood estimates with a median relative error of about 27%.     

Combining AHP with GIS for Predictive Cu Porphyry Potential Mapping: A Case Study in Ahar Area (NW,Iran)

Using the analytic hierarchy process (AHP) method for multi-index evaluation has special advantages, while the use of geographic information systems (GIS) is suitable for spatial analysis. Combining AHP with GIS provides an effective approach for studies of mineral potential mapping evaluation. Selection of potential areas for exploration is a complex process in which many diverse criteria are to be considered. In this article, AHP and GIS are used for providing potential maps for Cu porphyry mineralization on the basis of criteria derived from geologic, geochemical, and geophysical, and remote sensing data including alteration and faults. Each criterion was evaluated with the aid of AHP and the result mapped by GIS. This approach allows the use of a mixture of quantitative and qualitative information for decision-making. The results of application in this article provide acceptable outcomes for copper porphyry exploration.     

Burden prediction in blasting operation using rock geomechanical properties

Burden prediction is a vital task in the production blasting. Both the excessive and insufficient burden can significantly affect the result of blasting operation. The burden which is determined by empirical models is often inaccurate and needs to be adjusted experimentally. In this paper, an attempt was made to develop an artificial neural network (ANN) in order to predict burden in the blasting operation of the Mouteh gold mine, using considering geomechanical properties of rocks as input parameters. As such here, network inputs consist of blastability index (BI), rock quality designation (RQD), unconfined compressive strength (UCS), density, and cohesive strength. To make a database (including 95 datasets), rock samples are used from Iran’s Mouteh goldmine. Trying various types of the networks, a neural network, with architecture 5-15-10-1, was found to be optimum. Superiority of ANN over regression model is proved by calculating. To compare the performance of the ANN modeling with that of multivariable regression analysis (MVRA), mean absolute error (E _a), mean relative error (E _r), and determination coefficient (R ²) between predicted and real values were calculated for both the models. It was observed that the ANN prediction capability is better than that of MVRA. The absolute and relative errors for the ANN model were calculated 0.05 m and 3.85%, respectively, whereas for the regression analysis, these errors were computed 0.11 m and 5.63%, respectively. Moreover, determination coefficient of the ANN model and MVRA were determined 0.987 and 0.924, respectively. Further, a sensitivity analysis shows that while BI and RQD were recognized as the most sensitive and effective parameters, cohesive strength is considered as the least sensitive input parameters on the ANN model output effective on the proposed (burden).     

Lee yang theory for cosmological many body problem

We study the phase transition in a gravitating system by analyzing grand canonical partition function as a function of complex fugacity. We extend the Yang-Lee theory to study phase transitions in the gravitational galaxy clustering of galaxies having a variety of masses. This generalizes our previous work based on the same theory for the single-component system to a multicomponent system. We find that galaxy clustering is sensitive to masses and number densities of individual galaxies at early stages while at later stages collective behavior of the particles is more pronounced. This validates our earlier work obtained from different considerations.     

A new approach for developing comprehensive agricultural drought index using satellite-derived biophysical parameters and factor analysis method

The accurate assessment of drought and its monitoring is highly depending on the selection of appropriate indices. Despite the availability of countless drought indices, due to variability in environmental properties, a single universally drought index has not been presented yet. In this study, a new approach for developing comprehensive agricultural drought index from satellite-derived biophysical parameters is presented. Therefore, the potential of satellite-derived biophysical parameters for improved understanding of the water status of pistachio (Pistachio vera L.) crop grown in a semiarid area is evaluated. Exploratory factor analysis with principal component extraction method is performed to select the most influential parameters from seven biophysical parameters including surface temperature (T _s), surface albedo (α), leaf area index (LAI), soil heat flux (G _o), soil-adjusted vegetation index (SAVI), normalized difference vegetation index (NDVI), and net radiation (R _n). T _s and G _o were found as the most effective parameters by this method. However, T _s, LAI, α, and SAVI that accounts for 99.6 % of the total variance of seven inputs were selected to model a new biophysical water stress index (BPWSI). The values of BPWSI were stretched independently and compared with the range of actual evapotranspiration estimated through well-known METRIC (mapping evapotranspiration at high resolution with internal calibration) energy balance model. The results showed that BPWSI can be efficiently used for the prediction of the pistachio water status (RMSE of 0.52, 0.31, and 0.48 mm/day on three image dates of April 28, July 17, and August 2, 2010). The study confirmed that crop water status is accounted by several satellite-based biophysical parameters rather than single parameter.