Abstract: Basin effects is the phenomenon where earthquake waves become trapped and reverberate in concave-shaped bedrock depressions filled with soft sedimentary materials. The catastrophic effects of numerous earthquakes in the past have been attributed to basin effects, which can strongly affect the amplitude, frequency, and duration of strong ground motion and introduce spatial variability of seismic ground motion over short distances, especially near the basin edges. We here study the role of basin effects in Kathmandu Valley, Nepal, using observations of strong motion recordings and high rate GPS measurements of the 2015 Mw 7.8 Gorkha mainshock, and 2D-3D ground motion simulations. We specifically study a 2D cross section of the Kathmandu valley subjected to vertically propagating SV plane waves, a typical assumption in most published works on basin effects; and the response of the same cross section using a realistic kinematic source model to estimate the full far-field waveform. We investigate the role–if any—that nonlinearity played in the exceptionally long period ground shaking that characterized the strong motion records of the Gorkha mainshock, by using both elastic and anelastic constitutive models of the basin. Preliminary results show that coupling seismological source models with fine resolution geotechnical basin properties and anelastic sediment response, even in a place like Kathmandu where there is very little available information on the site conditions and hardly any strong motion records from past events, can significantly improve ground motion predictions compared to the rudimentary approach of 1D site response or 2D/3D simulations of basin effects with elastic sediment models.
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Abstract: This study aims to present a relatively short list of interim induced proxy ground motion models (GMMs) suitable for induced seismicity application in central and eastern North America (CENA). Induced proxy GMMs are models not established from datasets strictly made of induced events but can be used to predict ground motions from such events. For this purpose, we test a long list of GMMs against a dataset of induced earthquakes using the popular LLH method of Scherbaum et al. (2009) and its natural extension known as the multivariate logarithmic score of Mak et al. (2017). Our dataset is a subset of Rennolet et al. (2017) and at PGA is composed of 2414 time histories from 384 CENA induced events with hypocentral distances below 50 km and moment magnitudes from 3.5–5.8. Candidate GMMs are from two categories, including purely empirical models developed from the NGA-West2 database and indigenous models of CENA. The NGA-West2 database contains a large number of shallow small-to-moderate magnitude events from California that may approximate characteristic features of induced events in CENA. Some of the CENA models have considered near distance saturation for small-to-moderate magnitude range and/or have explicitly modeled source parameter as a function of focal depth that may make them reasonable induced proxy GMMs. Evaluation results indicate better performance of models of group 1 compared to CENA regional models. We recommend three models including Atkinson (2015), Chiou and Youngs (2014), and Abrahamson et al. (2014) above other candidate models and introduce them as most suitable induced proxy GMMs.
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Abstract: The characterization of high-frequency (>10 Hz) ground motions for hard rock sites in Eastern North America is a critical seismic response issue for major infrastructure, particularly nuclear power plants. The diminution of amplitudes with increasing frequency is modeled using kappa (Anderson and Hough, 1984 BSSA), which is a measure of the slope of amplitude decay at high frequencies in the spectral domain. This study examines kappa and its variability for 7 hard rock sites near Charlevoix, Quebec (Canada), using hundreds of recording from earthquakes of M>3 within 150 km. Kappa values are compared to bedrock velocities measured at the recording stations to gain insight into the relationship between kappa and physical rock properties. We also examine whether there is evidence of source or path effects on kappa.
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Abstract: It is a well-known fact that critical structures are required to be designed for the vertical effect of ground motions as well as the horizontal effects. We present a much-needed new model for the spectral ratio of vertical to horizontal component of earthquakes (V/H ratio) for Central and Eastern North America (CENA). The V/H ratio model has the advantage of considering the earthquake magnitude, source to site distance, and shear-wave velocity of soil deposits in the upper 30m of the site for PGA and a wide range of periods (0.001 to 10.0 seconds). The model evaluation will be based on a comprehensive set of regression analysis of the newly compiled Next Generation Attenuation (NGA-East) database of available CENA recordings with M ≥ 3.0 and RRUP < 1000 km. The median value of the geometric mean of the orthogonal horizontal motions rotated through all possible nonredundant rotation angles, known as the GMRotD50 (Boore et al., 2006), will be used along with the vertical component to perform regression using the maximum likelihood method. The earthquakes and recording stations in the Gulf Coast region are excluded due to their different ground-motion attenuation (Dreiling et al., 2014). However, the excluded data are evaluated in a parallel study to see if they can be represented by the same source parameters but different anelastic attenuation. The predicted ratios from the proposed model will be compared with recently published V/H ratio models and can be used to develop the vertical response spectra for CENA sites.
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Abstract: Cotopaxi is an active stratovolcano located in Ecuador, which has previously presented 5 eruptive periods since 1533. The aim of this work is to develop an automatic recognition system based on machine learning techniques for both detection and classification of seismic events from Cotopaxi volcano. Our primary goal is an automatic recognition system that may help experts to make more appropriate decisions towards a real time system, which may allow authorities to launch effective early warnings. This work is divided in two stages: in the detection stage, a non-overlapping segmentation was used, with 15-sec windows; in each window several features were extracted in the time, frequency, and scale domains by using methods such as moving average, power spectral density, and wavelet transforms, 84 features were extracted: 13 in the time domain, 21 in the frequency domain and 50 in the scale domain; this stage allows to identify the starting and ending point of the microseism; by using the Moving Average method, a threshold equal to 0.2 of the normalized signal was identified for the detector to allow the identification of the existence of a microseisms. The best result was obtained by using Support Vector Machine techniques, it presents a performance metrics with a Nu = 0.5 obtaining 99.72% accuracy with a BER of 0.0014.The following Machine Learning techniques were used for classification of seismic events: Decision Trees (DT), Linear and Non-linear Support Vector Machines (SVM). An analysis of the BER was performed, varying the percentage of the training and test matrices for the different Machine Learning techniques. This variation improves the performance measures. The system was trained to have the ability to identify Long Period (LP) and Volcano-Tectonic (VT) events; the best result (BER equal to 0.1125 with a 94.29% accuracy) were obtained with non-linear SVM.
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Abstract: An unusual, ongoing, earthquake swarm is currently active in a non-volcanic area in western Saudi Arabia near the town of Umm-Lujj. Since February 2017, hundreds of earthquakes have been recorded continuously, with a maximum magnitude reaching MI 3.7. Although this earthquake swarm occurs only about 75 km NW of the volcanic area of Harrat Lunayyir, it does not appear to be directly associated with it. The latter experienced a magmatic dike intrusion in 2009 with intense seismic activity (including a surface rupturing Mw 5.7 earthquake). We use seismological data, recorded by the Saudi Geological Survey (SGS) that operates 45 broadband seismic stations. We analyze the continuous seismic waveform data provided by the SGS broadband stations to map the spatial and temporal distribution of seismicity in an effort to gain insights on the underlying physical processes that are responsible for this swarm. We use double-difference algorithm to determine the relative distances between earthquakes. The relocation results using both P- and S- phases greatly improve the earthquake locations. We identified 3 events clusters, each at a different depth level. These clusters are concentrated within a well-defined rock volume of roughly 10 km3. The dominant cluster of these events is shallow (4-8 km depth). The deepest cluster includes events occur as deep as 16 km. The results may reveal an image of the NW-SE Najd fault system (a complex set of strike-slip faults and shear zones across the Precambrian of Arabia). Using the well-relocated events, we assemble the body wave arrival times that are inverted for the local velocity and attenuation models. The focal-mechanism solutions of the largest earthquakes (> Ml 2.0) vary widely, indicative of a highly fractured complex fault system and without a clear correlation to the regional stress field of Red Sea. This observation is in agreement with the complex local tectonics of the shear system which accommodates the swarm sequence.
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