Modelling of earthquake hazard and secondary effects for loss assessment in Marmara (Turkey)

This study proposes the methodology for an innovative Earthquake Risk Assessment (ERA) framework to calculate seismic hazard maps in regions where limited seismo-tectonic information exists. The tool calculates the seismic hazard using a probabilistic seismic hazard analysis (PSHA) based on a MonteCarlo approach, which generates synthetic earthquake catalogues by randomizing key hazard parameters in a controlled manner. All the available data was transferred to GIS format and the results are evaluated to obtain a hazard maps that consider site amplification, liquefaction susceptibility and landslide hazard. The effectiveness of the PSHA methodology is demonstrated by carrying out the hazard analysis of Marmara region (Turkey), for which benchmark maps already exist. The results show that the hazard maps for Marmara region compare well with previous PSHA studies and with the National Building Code map. The proposed method is particularly suitable for generating hazard maps in developing countries, where data is not available or easily accessible

Derivation of consistent hard rock (1000<Vs<3000 m/s) GMPEs from surface and down-hole recordings: Analysis of KiK-net data

A key component in seismic hazard assessment is the estimation of ground motion for hard rock sites, either for applications to installations built on this site category, or as an input motion for site response computation. Empirical ground motion prediction equations (GMPEs) are the traditional basis for estimating ground motion while VS30 is the basis to account for site conditions. As current GMPEs are poorly constrained for VS30 larger than 1000 m/s, the presently used approach for estimating hazard on hard rock sites consists of “host-to-target” adjustment techniques based on VS30 and κ0 values. The present study investigates alternative methods on the basis of a KiK-net dataset corresponding to stiff and rocky sites with 500 < VS30 < 1350 m/s. The existence of sensor pairs (one at the surface and one in depth) and the availability of P- and S-wave velocity profiles allow deriving two “virtual” datasets associated to outcropping hard rock sites with VS in the range [1000, 3000] m/s with two independent corrections: 1/down-hole recordings modified from within motion to outcropping motion with a depth correction factor, 2/surface recordings deconvolved from their specific site response derived through 1D simulation. GMPEs with simple functional forms are then developed, including a VS30 site term. They lead to consistent and robust hard-rock motion estimates, which prove to be significantly lower than host-to-target adjustment predictions. The difference can reach a factor up to 3–4 beyond 5 Hz for very hard-rock, but decreases for decreasing frequency until vanishing below 2 Hz

Understanding single-station ground motion variability and uncertainty (sigma) – Lessons learnt from EUROSEISTEST

Accelerometric data from the well-studied valley EUROSEISTEST are used to investigate ground motion uncertainty and variability. We define a simple local ground motion prediction equation (GMPE) and investigate changes in standard deviation (σ) and its components, the between-event variability (τ) and within-event variability (φ). Improving seismological metadata significantly reduces τ (30-50%), which in turn reduces the total σ. Improving site information reduces the systematic site-to-site variability, φS2S (20-30%), in turn reducing φ, and ultimately, σ. Our values of standard deviations are lower than global values from literature, and closer to path-specific than site-specific values. However, our data have insufficient azimuthal coverage for single-path analysis. Certain stations have higher ground-motion variability, possibly due to topography, basin edge or downgoing wave effects. Sensitivity checks show that 3 recordings per event is a sufficient data selection criterion, however, one of the dataset’s advantages is the large number of recordings per station (9-90) that yields good site term estimates. We examine uncertainty components binning our data with magnitude from 0.01 to 2 s; at smaller magnitudes, τ decreases and φSS increases, possibly due to κ and source-site trade-offs Finally, we investigate the alternative approach of computing φSS using existing GMPEs instead of creating an ad hoc local GMPE. This is important where data are insufficient to create one, or when site-specific PSHA is performed. We show that global GMPEs may still capture φSS, provided that: 1. the magnitude scaling errors are accommodated by the event terms; 2. there are no distance scaling errors (use of a regionally applicable model). Site terms (φS2S) computed by different global GMPEs (using different site-proxies) vary significantly, especially for hard-rock sites. This indicates that GMPEs may be poorly constrained where they are sometimes most needed, i.e. for hard rock

Earthquake source parameters and scaling relationships in Hungary (central Pannonian basin)

Abstract Fifty earthquakes that occurred in Hungary (central part of the Pannonian basin) with local magnitude ML ranging from 0.8 to 4.5 have been analyzed. The digital seismograms used in this study were recorded by six permanent broad-band stations and twenty short-period ones at hypocentral distances between 10 and 327 km. The displacement spectra for P- and SH-waves were analyzed according to Brune’s source model. Observed spectra were corrected for path-dependent attenuation effects using an independent regional estimate of the quality factor QS. To correct spectra for near-surface attenuation, the k parameterwas calculated, obtaining it fromwaveforms recorded at short epicentral distances. The values of the k parameter vary between 0.01 to 0.06 s with a mean of 0.03 s for P-waves and between 0.01 to 0.09 s with a mean of 0.04 s for SH-waves. After correction for attenuation effects, spectral parameters (corner frequency and low-frequency spectral level) were estimated by a grid search algorithm. The obtained seismic moments range from4.21×1011 to 3.41×1015 Nm (1.7≤Mw ≤4.3). The source radii are between 125 and 1343 m. Stress drop values vary between 0.14 and 32.4 bars with a logarithmic mean of 2.59 bars (1 bar = 105 Pa). From the results, a linear relationship between local andmomentmagnitudes has been established. The obtained scaling relations show slight evidence of self-similarity violation. However, due to the high scatter of our data, the existence of self-similarity cannot be excluded

The complete mitochondrial genome of the oriental fruit moth Grapholita molesta (Busck) (Lepidoptera: Tortricidae)

The oriental fruit moth, Grapholita molesta (Busck) (Lepidoptera: Tortricidae) currently is one of the economically most destructive pest species of stone and pome fruits worldwide. Here we sequenced the complete mitochondrial genome of this pest. This genome is 15,776 bp long, with an A + T content of 81.24%, containing 37 typical animal mitochondrial genes and an A + T-rich region. All gene are arranged as hypothesized ancestral gene order of insects except for trnM, which was shuffled from 3′ downstream of trnQ to 5′ upstream of trnI. cox1 gene uses unusual CGA start codon, as that in all other sequenced lepidopteran mitochondrial genome. The secondary structures for the two rRNA genes were predicted. All helices typically present in insect mitochondrial rRNA genes are generated. A microsatellite sequence was inserted into the region of H2347 in rrnL in G. molesta and two other sequenced tortricid mitochondrial genomes, indicating that the insertion event in this helix might occurred anciently in family Tortricidae. All of the 22 typical animal tRNA genes have a typical cloverleaf structure except for trnS2, in which the D-stem pairings in the DHU arm are absent. An intergenic sequence is present between trnQ and nad2 as well as in other sequenced lepidopteran mitochondrial genomes, which was presumed to be a remnant of trnM gene and its boundary sequences after the duplication of trnM to the upstream of trnI in Lepidoptera. The A + T-rich region is 836 bp, containing six repeat sequences of “TTATTATTATTATTAAATA(G)TTT.