Search

Spectral parameter kappa ([Math Processing Error]) is widely used to model the decay slope of the Fourier acceleration spectrum at high frequencies. This study presents estimates of [Math Processing Error], often referred to as the site-related contribution in [Math Processing Error], for the Iranian strong-motion dataset. For our analysis, we use 2798 acceleration time histories from 325 earthquakes (between 1976 and 2020, inclusive) in the magnitude range [Math Processing Error] 3-7.4 and recorded over 285 stations at distances up to 100 km. The main objective of this study is to estimate [Math Processing Error] and associated uncertainties at Iranian strong-motion stations. We investigate different sources of uncertainty that contribute to the higher scatter in the station-specific measurements of [Math Processing Error]. According to our results, the choice of frequency range used to approximate linear decay of acceleration spectrum contributes significantly to measurement-related uncertainty. Moreover, our measurements exhibit regional variations in (average) [Math Processing Error] within Iran; in particular, for Zagros, the average [Math Processing Error] is found to be lower than that for northern Iran ([Math Processing Error]) and central-eastern Iran ([Math Processing Error]). In terms of between-station variability, there is a weak negative correlation between [Math Processing Error] and [Math Processing Error]. The within-station uncertainty (i.e., standard deviation of [Math Processing Error] values at individual stations) is also found to be decreasing with increasing [Math Processing Error]. The fundamental frequency of the site ([Math Processing Error]) also appears to affect both measurement-related uncertainty and between-station variability at stations that exhibit significant 1D soil response behavior. [ABSTRACT FROM AUTHOR]

Recently, several local ground-motion prediction equations (GMPEs) for peak ground acceleration (PGA) and peak ground velocity (PGV) have been developed in northern Iran, most of which are for the horizontal component of seismic motion. There are also few relations of the vertical component of seismic motion. However, there is not any relation for prediction of peak ground displacement (PGD) in this region. In this article, 463 three-component strong-motion records from 107 events occurred between 1976 and 2016 with 4.5≤Mw≤7.4 and epicentral distances up to 100 km are selected from a list recorded by the Building and Housing Research Center (BHRC) of Iran, the Disaster and Emergency Management Presidency of Turkey (AFAD), and the Consortium of Organizations for Strong-Motion Observation Systems (COSMOS) data for deriving GMPEs for both horizontal and vertical motions. Several affecting parameters such as the earthquake magnitude, source-to-site distance, site class, and faulting mechanism for different components of peak ground motions are considered as the predictor variables of GMPEs. An evolutionary algorithm, namely Prefix Gene Expression Programming (PGEP), which can present GMPEs for PGA, PGV, and PGD without any predetermined regression models, is used. These presented models are evaluated by residuals, errors, and goodness-of-fit measures and then compared with the available GMPEs in the literature, which proves the derived GMPEs relying on PGEP models are consistent with the other relations previously developed with the use of the local and regional data. However, the present equations are more complete and thus can measure the site ground-motion parameters for the predesign goals more reliably. [ABSTRACT FROM AUTHOR]

The spatial autocorrelation (SPAC) method is a useful tool for interpreting ambient noise, and several theoretical tools based on this method are available for data collection, processing, and analysis. The method uses the three components of seismic microtremors recorded simultaneously with arrays of different configurations and yields dispersion curves of Rayleigh and Love waves. Existing theories of the SPAC method are based on an idealized circular array with an infinite number of stations (the continuous model). Errors due to the use of a finite number of stations have been derived theoretically for vertically polarized ambient noise. This paper presents an extension of that error theory to horizontally polarized ambient noise so that it is valid for a circular array with M uniformly spaced stations. The quantities that are obtained as the output of the SPAC method, however, depend on the ratio of the power spectral densities (PSDs) of Rayleigh and Love waves, which are difficult to identify beforehand. Instead we introduce a quantity, n, that does not depend on the PSD ratio and therefore is more convenient for separating the properties of Rayleigh and Love waves. We derive an expression of n for a finite, M-station circular array (κM) and compare it with what it should be in the continuous model (κT). We illustrate κM and κT using field data from a site in Tehran and discuss their discrepancies. Finally, we demonstrate how we have estimated Love-wave phase velocities using the calculated κM. [ABSTRACT FROM AUTHOR]