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.

     

A novel approach for classification of earthquake ground-motion records

This paper presents a new clustering procedure based on K-means and self-organizing map (SOM) network algorithms for classification of earthquake ground-motion records. Six scalar indicators are used in data analysis for describing the frequency content features of earthquake ground motions, named as the average spectral period (T _avg ), the mean period (T _m ), the smoothed spectral predominant period (T ₀), the characteristic period (T _4.3), the predominant period based on velocity spectrum (T _gSv ), and the shape factor (Ω). Different clustering validity indexes were applied to determine the best estimates of the number of clusters on real and synthetic data. Results showed the high performance of proposed procedure to reveal salient features of complex seismic data. The comparison between the results of clustering analyses recommend the smoothed spectral predominant period as an effective indicator to describe ground-motion classes. The results also showed that K-means algorithm has better performance than SOM algorithm in identification and classification procedure of ground-motion records.     

Estimation of inelastic displacement ratio for base‐isolated structures

This study develops a straightforward approximate method to estimate inelastic displacement ratio, C₁ for base‐isolated structures subjected to near‐fault and far‐fault ground motions. Taking into account the inelastic behavior of isolator and superstructure, a 2 degrees of freedom model is employed. A total of 90 earthquake ground motions are selected and classified into different clusters according to the frequency content features of records represented by the peak ground acceleration to peak ground velocity ratio, A_p/V_p. A parametric study is conducted, and effective factors in C₁ (i.e., fundamental vibration period of the superstructure, T_s; postyield stiffness ratio of the superstructure, α_s; strength reduction ratio, R; vibration period of the isolator, T_b; strength of the isolator, Q; ratio of superstructure mass to total mass of the system, γ_m) are recognized. The results indicate that the practical range of C₁ values could be expected for base‐isolated structures. Subsequently, effective parameters are included in simple predictive equations. Finally, the accuracy of the proposed approximate equations is evaluated and verified through error measurement, and comparisons are made in the analyses.     

Dynamic equivalent soil characteristics identification using earthquake records

Techniques for soil property estimation can be categorized into two main groups, in-situ and laboratory methods. Previous investigations indicated that strong ground motions record provides a very useful tool to estimating the in-situ characteristics of soil. The main objective of the present work is to utilize the particle swarm optimization algorithm(PSOA) integrated with linear site response method to obtain the equivalent soil profile characteristics from the available surface and bedrock earthquake motion records. To demonstrate the numerical efficiency and the validity of this approach, the procedure is validated against an available case. Then this procedure is utilized to identify the soil properties profiles of the site by using strong ground motions data recorded during the Bam earthquake of December 26, 2003. The magnitude and PGA of Bam earthquake were MW 6.6 and 0.8 g respectively.     

Time scale effect and uncertainty in reconstruction of paleo‐hydrology

Tree‐ring‐based reconstructions of paleo‐hydrology have proved useful for better understanding the irregularities and extent of past climate changes, and therefore, for more effective water resources management. Despite considerable advances in the field, there still exist challenges that introduce significant uncertainties into paleo‐reconstructions. This study outlines these challenges and address them by developing two themes: (1) the effect of temporal scaling on the strength of the relationship between the hydrologic variables, streamflow in this study, and tree growth rates and (2) the reconstruction uncertainty of streamflow due to the dissimilarity or inconsistency in the pool of tree‐ring chronologies (predictors in reconstruction) in a basin. Based on the insight gained, a methodology is developed to move beyond only relying on the annual hydrology‐growth correlations, and to utilize additional information embedded in the annual time series at longer time scales (e.g. multi‐year to decadal time scales). This methodology also generates an ensemble of streamflow reconstructions to formally account for uncertainty in the pool of chronology sites. The major headwater tributaries of the Saskatchewan River Basin, the main source of surface water in the Canadian Prairie Provinces, are used as the case study. It is shown that the developed methodology explains the variance of streamflows to a larger extent than the conventional approach and better preserves the persistence and variability of streamflows across time scales (Hurst‐type behaviour). The resulting ensemble of paleo‐hydrologic time series is able to more credibly pinpoint the timing and extent of past dry and wet periods and provides a dynamic range of uncertainty in reconstruction. This range varies with time over the course of the reconstruction period, indicating that the utility of tree‐ring chronologies for paleo‐reconstruction differs for different time periods over the past several centuries in the history of the region. The proposed ensemble approach provides a credible range of multiple‐century‐long water availability scenarios that can be used for vulnerability assessment of the existing water infrastructure and improving water resources management. Copyright © 2015 John Wiley & Sons, Ltd.     

On nonequilibrium models of spontaneous countercurrent imbibition

Spontaneous displacement of the non-wetting phase by a wetting phase in a porous medium, known as spontaneous imbibition, is an important mechanism of oil recovery from fractured reservoirs. In this paper, we consider the nonequilibrium model, proposed by Aryana and Kovscek, where consitutive relationships for multiphase flow in porous media are functions of a locally moving time-average saturation, and allow relaxation time to be an explicit function of local saturation. We obtain asymptotic self-similar solutions for early and late times. At very early stages, the time-scale of the process characterizing the cumulative volume of displaced fluid is a power function with an exponent of \(\frac {1}{2}+\frac {1}{2r+1}\) where r is the inverse of pore size distribution index of the medium in question. Additionally, the cumulative volume of displaced fluid at late times is independent of relaxation time, and this volume approaches the square root of time asymptotically. Finally, the late-time solution for recovery is compared with experimental observations.     

Compilation simulation of surface water and groundwater resources using the SWAT-MODFLOW model for a karstic basin in Iran

Modeling of karstic basins can provide a better understanding of the interactions between surface water and groundwater, a more accurate estimation of infiltrated water amount, and a more reliable water balance calculation. In this study, the hydrological simulation of a karstic basin in a semiarid region in Iran was performed in three different stages. In the first stage, the original SWAT model was used to simulate surface-water flow. Then, the SWAT-MODFLOW conjunctive model was implemented according to the groundwater characteristics of the study area. Finally, due to the karstic characteristics of the region and using the CrackFlow (CF) package, the SWAT-MODFLOW-CF conjunctive model was developed to improve the simulation results. The coefficient of determination (R²) and the Nash-Sutcliffe efficiency coefficient (NSE) as error evaluation criteria were calculated for the models, and their average values were 0.63 and 0.57 for SWAT, 0.68 and 0.61 for SWAT-MODFLOW, 0.73 and 0.7 for SWAT-MODFLOW-CF, respectively. Moreover, the mean absolute error (MAE) and root mean squared error (RMSE) of the calibration for groundwater simulation using the SWAT-MODFLOW model were 1.23 and 1.77 m, respectively. These values were 1.01 and 1.33 m after the calibration of the SWAT-MODFLOW-CF model. After modifying the CF code and keeping the seams and cracks open in both dry and wet conditions, the amount of infiltrated water increased and the aquifer water level rose. Therefore, the SWAT-MODFLOW-CF conjunctive model can be proposed for use in karstic areas containing a considerable amount of both surface water and groundwater resources.

     

Assessment of seismic amplification factor of excavation with support system

Retaining walls have been used in many construction projects such as for road and inclined surfaces protection. The damage caused by an earthquake depends on the fundamental frequency, amplitude and the duration of the seismic motion. These parameters strongly depend on the seismic properties of the layers that are near the surface. In the study of retaining walls, in addition to the influence of soil, the influence of topography is also important. In the present study, site response analysis is performed by using finite element software PLAXIS to obtain the effect of various factors such as embedded length of the sheet pile, underground water table, length and angle of the nail, shear wave velocity of soil on site effect and dynamic response. Moreover, for better understanding of the effect of the above parameters, the stability analysis was performed by using shear reduction method. The results show that an increase in the embedded length of the sheet pile and the length of nailing causes an increase in the amplification factor. Moreover, for shear-wave velocity in the range of 200-600 m/s, the amplification factor increases with increase of the shear-wave velocity due to the decrease of nonlinear behavior.     

Long‐lead seasonal rainfall forecasting using time‐delay recurrent neural networks: a case study

A temporal artificial neural network‐based model is developed and applied for long‐lead rainfall forecasting. Tapped delay lines and recurrent connections are two different components that are used along with a static multilayer perceptron network to design a time‐delay recurrent neural network. The proposed model is, in fact, a combination of time‐delay and recurrent neural networks. The model is applied in three case studies of the Northwest, West, and Southwest basins of Iran. In addition, an autoregressive moving average with exogenous inputs (ARMAX) model is used as a baseline in order to be compared with the time‐delay recurrent neural networks developed in this study. Large‐scale climate signals, such as sea‐level pressure, that affect the rainfall of the study area are used as the predictors in the models, as well as the persistence between rainfall data. The results of winter‐spring rainfall forecasts are discussed thoroughly. It is demonstrated that in all cases the proposed neural network results in better forecasts in comparison with the statistical ARMAX model. Moreover, it is found that in two of three case studies the time‐delay recurrent neural networks perform better than either recurrent or time‐delay neural networks. The results demonstrate that the proposed method can significantly improve the long‐lead forecast by utilizing a non‐linear relationship between climatic predictors and rainfall in a region. Copyright © 2007 John Wiley & Sons, Ltd.