932 research outputs found

    On Pads and Filters: Processing Strong-Motion Data

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    Predominant-period site classification for response spectra prediction equations in Italy

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    We propose a site-classification scheme based on the predominant period of the site, as determined from the average horizontal-to-vertical (H/V) spectral ratios of ground motion. Our scheme extends Zhao et al.(2006) classifications by adding two classes, the most important of which is defined by flat H/V ratios with amplitudes less than 2. The proposed classification is investigated by using 5%-damped response spectra from Italian earthquake records. We select a dataset of 602 three-component analog and digital recordings from 120 earthquakes recorded at 214 seismic stations within a hypocentral distance of 200 km. Selected events are in the moment-magnituderange 4.0 ≤ Mw ≤ 6.8 and focal depths from a few kilometers to 46 km. We computed H/V ratios for these data and used them to classify each site into one of six classes. We then investigate the impact of this classification scheme on empirical ground-motion prediction equations (GMPEs) by comparing its performance with that of the conventional rock/soil classification. Although the adopted approach results in only a small reduction of the overall standard deviation, the use of H/V spectral ratios in site classification does capture the signature of sites with flat frequency-response, as well as deep and shallow-soil profiles, characterized by long- and short-period resonance, respectively; in addition, the classification scheme is relatively quick and inexpensive, which is an advantage over schemes based on measurements of shear wave velocity

    Neural Network-Based Equations for Predicting PGA and PGV in Texas, Oklahoma, and Kansas

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    Parts of Texas, Oklahoma, and Kansas have experienced increased rates of seismicity in recent years, providing new datasets of earthquake recordings to develop ground motion prediction models for this particular region of the Central and Eastern North America (CENA). This paper outlines a framework for using Artificial Neural Networks (ANNs) to develop attenuation models from the ground motion recordings in this region. While attenuation models exist for the CENA, concerns over the increased rate of seismicity in this region necessitate investigation of ground motions prediction models particular to these states. To do so, an ANN-based framework is proposed to predict peak ground acceleration (PGA) and peak ground velocity (PGV) given magnitude, earthquake source-to-site distance, and shear wave velocity. In this framework, approximately 4,500 ground motions with magnitude greater than 3.0 recorded in these three states (Texas, Oklahoma, and Kansas) since 2005 are considered. Results from this study suggest that existing ground motion prediction models developed for CENA do not accurately predict the ground motion intensity measures for earthquakes in this region, especially for those with low source-to-site distances or on very soft soil conditions. The proposed ANN models provide much more accurate prediction of the ground motion intensity measures at all distances and magnitudes. The proposed ANN models are also converted to relatively simple mathematical equations so that engineers can easily use them to predict the ground motion intensity measures for future events. Finally, through a sensitivity analysis, the contributions of the predictive parameters to the prediction of the considered intensity measures are investigated.Comment: 5th Geotechnical Earthquake Engineering and Soil Dynamics Conference, Austin, TX, USA, June 10-13. (2018

    A framework for orthology assignment from gene rearrangement data

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    Abstract. Gene rearrangements have successfully been used in phylogenetic reconstruction and comparative genomics, but usually under the assumption that all genomes have the same gene content and that no gene is duplicated. While these assumptions allow one to work with organellar genomes, they are too restrictive when comparing nuclear genomes. The main challenge is how to deal with gene families, specifically, how to identify orthologs. While searching for orthologies is a common task in computational biology, it is usually done using sequence data. We approach that problem using gene rearrangement data, provide an optimization framework in which to phrase the problem, and present some preliminary theoretical results.

    The complete sequences and gene organisation of the mitochondrial genomes of the heterodont bivalves Acanthocardia tuberculata and Hiatella arctica – and the first record for a putative Atpase subunit 8 gene in marine bivalves

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    BACKGROUND: Mitochondrial (mt) gene arrangement is highly variable among molluscs and especially among bivalves. Of the 30 complete molluscan mt-genomes published to date, only one is of a heterodont bivalve, although this is the most diverse taxon in terms of species numbers. We determined the complete sequence of the mitochondrial genomes of Acanthocardia tuberculata and Hiatella arctica, (Mollusca, Bivalvia, Heterodonta) and describe their gene contents and genome organisations to assess the variability of these features among the Bivalvia and their value for phylogenetic inference. RESULTS: The size of the mt-genome in Acanthocardia tuberculata is 16.104 basepairs (bp), and in Hiatella arctica 18.244 bp. The Acanthocardia mt-genome contains 12 of the typical protein coding genes, lacking the Atpase subunit 8 (atp8) gene, as all published marine bivalves. In contrast, a complete atp8 gene is present in Hiatella arctica. In addition, we found a putative truncated atp8 gene when re-annotating the mt-genome of Venerupis philippinarum. Both mt-genomes reported here encode all genes on the same strand and have an additional trnM. In Acanthocardia several large non-coding regions are present. One of these contains 3.5 nearly identical copies of a 167 bp motive. In Hiatella, the 3' end of the NADH dehydrogenase subunit (nad)6 gene is duplicated together with the adjacent non-coding region. The gene arrangement of Hiatella is markedly different from all other known molluscan mt-genomes, that of Acanthocardia shows few identities with the Venerupis philippinarum. Phylogenetic analyses on amino acid and nucleotide levels robustly support the Heterodonta and the sister group relationship of Acanthocardia and Venerupis. Monophyletic Bivalvia are resolved only by a Bayesian inference of the nucleotide data set. In all other analyses the two unionid species, being to only ones with genes located on both strands, do not group with the remaining bivalves. CONCLUSION: The two mt-genomes reported here add to and underline the high variability of gene order and presence of duplications in bivalve and molluscan taxa. Some genomic traits like the loss of the atp8 gene or the encoding of all genes on the same strand are homoplastic among the Bivalvia. These characters, gene order, and the nucleotide sequence data show considerable potential of resolving phylogenetic patterns at lower taxonomic levels
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