Detection of pulse-like ground motions based on continues wavelet transform

This paper implements a quantitative approach to detect pulse-like ground motions based on continues wavelet transform, which is able to clearly identify sudden jumps in time history of earthquake records by considering contribution of different levels of frequency. These analyses were performed on a set of time series records obtained in near-fault regions of Iran. Pulse-like ground motions frequently resulted from directivity effects in near-fault area and are of interest in the field of seismology and also earthquake engineering for seismic performance evaluation of structures. The results of this study basically help us to establish a suitable platform for selecting pulse-like records, while performance evaluation of structure in near-fault area will need to account. The period of velocity pulses as a key parameter that significantly affects structural response is simply determined by using a pseudo-period of the mother wavelets. In addition, the efficiency of different types of mother wavelets on classification performance and the features of detected pulse are investigated by applying seven different kinds of mother wavelets. The analyses indicate that the selection of most appropriate mother wavelet plays a significant role in effective extraction of ground motion features and consequently in estimation of velocity pulse period. As a result, the user should be aware of what is selected as a mother wavelet in the analysis. The comparisons given here among different mother wavelets also show the better performance of BiorSpline (bior1.3) basis from biorthognal wavelet families for the preferred purpose in this paper.     

Geochemistry and genesis of sulphide ore deposits in Sharosh Village,Qandil Series,Kurdistan Region,NE Iraq

The low-grade base metal sulphide Cu–Zn–Pb and Fe mineralization of Qandil Series develop in shear zones that occur in formations of the north-western part of the Zagros Orogen. This sulphide mineralization occurs either as quartz vein type or disseminated type associated with metamorphic rocks (marbles and phyllites). This study aims to characterize these sulphide-rich ores by means of their mineralogical and geochemical features, including also the features of the corresponded host formations and those of marbles (calcitic and dolomitic) and phyllites. Petrographical data indicate the presence of Cu, Zn, Pb and Fe sulphides in hydrothermal quartz (±calcite) veins of different generations. Geochemical data of surface samples indicate enrichment of Cu and Fe in shear zones with low concentrations in Zn and Pb. The REE data indicate that the genesis of these sulphide ores took place in a hydrothermal system and was generally attributed to high temperature (> 250 °C).The mineralization seems to be fault-controlled, which is favoured by the significant tectonic deformation of the area.     

Predicting mechanical properties and ultimate shear strength of gypsum,limestone and sandstone rocks using Vipulanandan models

ABSTRACT

In this study, over 1000 data from the literature was used to characterize and compare the density, strengths, modulus, fracture toughness, porosity and the ultimate shear strengths of the gypsum, limestone and sandstone rocks. The compressive modulus and Mode-1 fracture toughness of the gypsum rock, limestone rock and sandstone rocks varied from 0.7 GPa to 70 GPa, and from 0.03 MPa.m^0.5 to 2.6 MPa.m^0.5  respectively. Vipulanandan correlation model was effective in relating the modulus of elasticity, fracture toughness with the relevant strengths of the rocks. A new nonlinear Vipulanandan failure criterion was developed to quantify the tensile strength, pure shear (cohesion) strength and to predict the maximum shear strength limit with applied normal stress on the gypsum, limestone and sandstone rocks. The Vipulanandan failure model predicts the maximum shear strength limit was, as the Mohr-Coulomb failure model does not have a limit on the maximum shear strength. With the Vipulanandan failure model based on the available data, the maximum shear strengths predicted for the gypsum, limestone and sandstone rocks were 64 MPa, 114 MPa and 410 MPa respectively.     

Comparative performance of new hybrid ANFIS models in landslide susceptibility mapping

Natural Hazards - Many landslides occur in the Karun watershed in the Zagros Mountains. In the present study, we employed a novel comparative approach for spatial modeling of landslides given the...     

Macroseismic intensity attenuation in Iran

Macroseismic intensity data plays an important role in the process of seismic hazard analysis as well in developing of reliable earthquake loss models. This paper presents a physical-based model to predict macroseismic intensity attenuation based on 560 intensity data obtained in Iran in the time period 1975–2013. The geometric spreading and energy absorption of seismic waves have been considered in the proposed model. The proposed easy to implement relation describes the intensity simply as a function of moment magnitude, source to site distance and focal depth. The prediction capability of the proposed model is assessed by means of residuals analysis. Prediction results have been compared with those of other intensity prediction models for Italy, Turkey, Iran and central Asia. The results indicate the higher attenuation rate for the study area in distances less than 70 km.     

A new site classification approach based on neural networks

Site classification is an important procedure for a reliable site-specific seismic hazard assessment. On the other hand, the site conditions at strong-motion stations are essential for accurate interpretation and analysis of the recorded ground motion data obtained from different regions of the world. For some countries with insufficient data on the subsurface geological settings, the required site condition information is not available. This paper presents a new and efficient approach for site classification based on artificial neural networks (ANN) along with a selected set of representative horizontal to vertical spectral ratio (HVSR) curves for four site classes. The nonlinear nature of ANN and their ability to learn in a complex environment make it highly suitable for function approximation and solving complicated engineering problems. Two types of radial basis function (RBF) neural networks, namely, probabilistic neural networks (PNN) and generalized regression neural networks (GRNN) were chosen in this study, as no separate training phase is required, rendering them particularly suitable for site classification. The proposed approach has been tested using data of the Chi-Chi, Taiwan, earthquake (Mw=7.6) recorded from 87 stations at which the site conditions are known. Analyses show that both the PNN and the GRNN perform very well with similar accuracy in estimating site conditions, with successful rates of 78% and 75%, respectively.     

Bayesian estimation of b-value in Gutenberg–Richter relationship: a sample size reduction approach

Natural Hazards - One of the most widely used relationships for prediction of b-value is the Aki–Utsu relationship, which is based on the maximum likelihood estimating. This method has some...     

Aftershock ground motion characteristics during the 2012 Varzaghan–Ahar doublet events,northwest of Iran

The characteristics of aftershocks can be quite different from those of mainshocks. However, most of the works in the past have generally focused on the aftershocks of strong earthquakes, while some moderate earthquakes can cause aftershocks with magnitudes close to the mainshocks. This paper focuses on the aftershocks characteristics of the 2012 Varzaghan–Ahar Doublet event (Mw?=?6.4, 6.2), northwest of Iran. Numerous aftershocks with magnitudes 3.7–5.5 recorded by the local seismic stations provide useful data to study the spectral characteristics at various periods. The predictive capability of the 2008 Next Generation Attenuation along with two regional models has been examined by several aftershock records obtained during these events. This paper is the first attempt made to compare the predictive capability of modern models based on significant aftershock data obtained for these two moderate events. Results confirmed that the ground motion models which have been derived based on larger-magnitude events should not directly be extrapolated to predict aftershock ground motions with magnitude smaller than 5.5, especially when we use relationships that developed without considering aftershock data. The residual analysis showed that the model of Abrahamson and Silva (Earthq Spectra, 24(1): 67–97, 2008) performed the best overall predictions in this study. However, this model performs poorly in the short period of 0.2 s at distances greater than 50 km which underestimates the spectral values for this case.