Crustal structure of Northwest Zagros (Kermanshah) and Central Iran (Yazd and Isfahan) using teleseismic Ps converted phases

Receiver functions are widely employed to detect P-to-S converted waves and are especially useful to image seismic discontinuities in the crust. In this study we used the P receiver function technique to investigate the velocity structure of the crust beneath the Northwest Zagros and Central Iran and map out the lateral variation of the Moho boundary within this area. Our dataset includes teleseismic data (M _b ≥ 5.5, epicentral distance from 30° to 95°) recorded at 12 three-component short-period stations of Kermanshah, Isfahan and Yazd telemetry seismic networks. Our results obtained from P receiver functions indicate clear Ps conversions at the Moho boundary. The Moho depths were firstly estimated from the delay time of the Moho converted phase relative to the direct P wave beneath each network. Then, we used the P receiver function inversion to find the properties of the Moho discontinuity such as depth and velocity contrast. Our results obtained from PRF are in good agreement with those obtained from the P receiver function modeling. We found an average Moho depth of about 42 km beneath the Northwest Zagros increasing toward the Sanandaj-Sirjan Metamorphic Zone and reaches 51 km, where two crusts (Zagros and Central Iran) are assumed to be superposed. The Moho depth decreases toward the Urmieh-Dokhtar Cenozoic volcanic belt and reaches 43 km beneath this area. We found a relatively flat Moho beneath the Central Iran where, the average crustal thickness is about 42 km. Our P receiver function modeling revealed a shear wave velocity of 3.6 km/s in the crust of Northwest Zagros and Central Iran increasing to 4.5 km/s beneath the Moho boundary. The average shear wave velocity in the crust of UDMA as SSZ is 3.6 km/s, which reaches to 4.0 km/s while in SSZ increases to 4.3 km/s beneath the Moho.     

Dynamics of hourly sea level at Hillarys Boat Harbour,Western Australia: a chaos theory perspective

Water level forecasting using recorded time series can provide a local modelling capability to facilitate local proactive management practices. To this end, hourly sea water level time series are investigated. The records collected at the Hillarys Boat Harbour, Western Australia, are investigated over the period of 2000 and 2002. Two modelling techniques are employed: low-dimensional dynamic model, known as the deterministic chaos theory, and genetic programming, GP. The phase space, which describes the evolution of the behaviour of a nonlinear system in time, was reconstructed using the delay-embedding theorem suggested by Takens. The presence of chaotic signals in the data was identified by the phase space reconstruction and correlation dimension methods, and also the predictability into the future was calculated by the largest Lyapunov exponent to be 437 h or 18 days into the future. The intercomparison of results of the local prediction and GP models shows that for this site-specific dataset, the local prediction model has a slight edge over GP. However, rather than recommending one technique over another, the paper promotes a pluralistic modelling culture, whereby different techniques should be tested to gain a specific insight from each of the models. This would enable a consensus to be drawn from a set of results rather than ignoring the individual insights provided by each model.     

Seismic design factors for RC special moment resisting frames in Dubai, UAE

This study investigates the seismic design factors for three reinforced concrete (RC) framed buildings with 4, 16 and 32-stories in Dubai, UAE utilizing nonlinear analysis. The buildings are designed according to the response spectrum procedure defined in the 2009 International Building Code (IBC’09). Two ensembles of ground motion records with 10% and 2% probability of exceedance in 50 years (10/50 and 2/50, respectively) are used. The nonlinear dynamic responses to the earthquake records are computed using IDARC-2D. Key seismic design parameters are evaluated; namely, response modification factor (R), deflection amplification factor (Cd), system overstrength factor (Ωo), and response modification factor for ductility (Rd) in addition to inelastic interstory drift. The evaluated seismic design factors are found to significantly depend on the considered ground motion (10/50 versus 2/50). Consequently, resolution to the controversy of Dubai seismicity is urged. The seismic design factors for the 2/50 records show an increase over their counterparts for the 10/50 records in the range of 200%-400%, except for the Ωo factor, which shows a mere 30% increase. Based on the observed trends, period-dependent R and Cd factors are recommended if consistent collapse probability (or collapse prevention performance) in moment frames with varying heights is to be expected.     

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).