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We describe the data model that we intend to use in a publicly available site profile database under development for the United States. The initial implementation of the database contains data from California. Currently, our prototype data model consists of JavaScript Object Notation (JSON) format files for storing metadata and data. For a site to be included in the database, the minimum metadata requirements are geodetic coordinates and elevation values, and the minimum data requirement is a shear-wave velocity profile. The JSON files are structured in a hierarchal manner to store metadata and data using a nested structure consisting of location, velocity profiles, dispersion curve data (for surface-wave methods), geotechnical data, and horizontal-to-vertical spectral ratios. The database schema at the current stage of the project, and as we continue to develop the data model we will consider including other relevant data, as well as evaluate other file formats to increase the efficiency of data storage and querying. In the current data model, location information includes site geodetic values (latitude, longitude, and elevation) and various site descriptors related to surface geology, geomorphic terrain category, slope gradient at various resolutions, and a geotechnical site category. Velocity data include the geophysical method(s) used to obtain the shear-wave velocity profile, type of data recorded, modeled primary- and shear-wave velocity as a function of depth, modeled profile maximum depth, and the calculated VS30 value. In the case of surface-wave based data, dispersion curve data can be recorded in data structure as phase velocity versus either wavelength or frequency. Geotechnical data includes boring logs penetration resistance, cone penetration test sounding logs, and laboratory index test results. Horizontal-to-vertical spectral ratio plots are given as a function of frequency.

We describe the data model that we intend to use in a publicly available site profile database under development for the United States. The initial implementation of the database contains data from California. Currently, our prototype data model consists of JavaScript Object Notation (JSON) format files for storing metadata and data. For a site to be included in the database, the minimum metadata requirements are geodetic coordinates and elevation values, and the minimum data requirement is a shear-wave velocity profile. The JSON files are structured in a hierarchal manner to store metadata and data using a nested structure consisting of location, velocity profiles, dispersion curve data (for surface-wave methods), geotechnical data, and horizontal-to-vertical spectral ratios. The database schema at the current stage of the project, and as we continue to develop the data model we will consider including other relevant data, as well as evaluate other file formats to increase the efficiency of data storage and querying. In the current data model, location information includes site geodetic values (latitude, longitude, and elevation) and various site descriptors related to surface geology, geomorphic terrain category, slope gradient at various resolutions, and a geotechnical site category. Velocity data include the geophysical method(s) used to obtain the shear-wave velocity profile, type of data recorded, modeled primary- and shear-wave velocity as a function of depth, modeled profile maximum depth, and the calculated VS30 value. In the case of surface-wave based data, dispersion curve data can be recorded in data structure as phase velocity versus either wavelength or frequency. Geotechnical data includes boring logs penetration resistance, cone penetration test sounding logs, and laboratory index test results. Horizontal-to-vertical spectral ratio plots are given as a function of frequency.

Frequency-dependent horizontal-to-vertical spectral ratios (HVSR) can provide information on one or more site resonant frequencies and relative levels of amplification at those frequencies. Such information is potentially useful for predicting site amplification but is not present in site databases that have been developed over the last 15–20 years for the Next-Generation Attenuation (NGA) projects, which instead use the time-averaged shear-wave velocity (VS) in the upper 30 m of the site (VS30) as the primary site parameter and are supplemented with basin depth terms where available. In order for HVSR-based parameters to be used in future versions of site databases, a publicly accessible repository of this information is needed. We adapt a relational database developed to archive and disseminate VS data to also include HVSR. Our intent with the database is to provide relevant HVSR data and supporting metadata, but not parameters derived from the data. We consider the relevant data to be the frequency-dependent HVSR, where the horizontal is taken as the median component and as a function of horizontal azimuth (referred to as polar plots). Relevant metadata includes site location information, details about the equipment used to make the measurements, and processing details related to windowing, anti-trigger routines, and filtering. We describe the database schema developed to organize and present this information. The relational database stores HVSR data, but not site parameters derived from the data. Site parameters of potential interest for modeling purposes include: (1) a binary variable indicating whether an HVSR plot contains a peak; (2) one or more peak frequencies; (3) peak amplitudes; and (4) peak widths. We describe and illustrate analysis routines to derive these parameters that are implemented in Python on a Jupyter Notebook enabled by DesignSafe-CI. These routines interact with the database via cloud computing, but are not directly part of the database.

Frequency-dependent horizontal-to-vertical spectral ratios (HVSR) can provide information on one or more site resonant frequencies and relative levels of amplification at those frequencies. Such information is potentially useful for predicting site amplification but is not present in site databases that have been developed over the last 15–20 years for the Next-Generation Attenuation (NGA) projects, which instead use the time-averaged shear-wave velocity (VS) in the upper 30 m of the site (VS30) as the primary site parameter and are supplemented with basin depth terms where available. In order for HVSR-based parameters to be used in future versions of site databases, a publicly accessible repository of this information is needed. We adapt a relational database developed to archive and disseminate VS data to also include HVSR. Our intent with the database is to provide relevant HVSR data and supporting metadata, but not parameters derived from the data. We consider the relevant data to be the frequency-dependent HVSR, where the horizontal is taken as the median component and as a function of horizontal azimuth (referred to as polar plots). Relevant metadata includes site location information, details about the equipment used to make the measurements, and processing details related to windowing, anti-trigger routines, and filtering. We describe the database schema developed to organize and present this information. The relational database stores HVSR data, but not site parameters derived from the data. Site parameters of potential interest for modeling purposes include: (1) a binary variable indicating whether an HVSR plot contains a peak; (2) one or more peak frequencies; (3) peak amplitudes; and (4) peak widths. We describe and illustrate analysis routines to derive these parameters that are implemented in Python on a Jupyter Notebook enabled by DesignSafe-CI. These routines interact with the database via cloud computing, but are not directly part of the database.

Peaks in horizontal-to-vertical spectral ratios (HVSRs) of Fourier amplitudes from three-component recordings of ground vibrations without undue local anthropogenic influence are used to identify site resonances, which are an important component of site response. We address two topics: (1) how should HVSR peaks be identified and (2) are there appreciable differences in HVSR derived using different instruments recording microtremors and seismic strong ground motions? We propose identifying peaks by considering peak amplitudes relative to neighboring ordinates and peak width. The procedure incorporates a regression tree algorithm that can be tuned to conform with user preferences toward relatively "conservative" or "liberal" peak identification (producing few or many sites with peaks, respectively). We then investigate the consistency of microtremor-based HVSRs (mHVSRs) derived from seismometers and accelerometers, which show a high rate of false negatives (missed peaks) from accelerometers with a full scale of ± 2g or greater. In contrast, mHVSRs derived from collocated temporary and permanent seismometers (optimized to record teleseismic signals) have about 60%-80% consistency (with no apparent bias in peak attributes). This indicates that mHVSRs from accelerometers having a broad full scale are unreliable but that mHVSRs can be reliably obtained from temporary or permanent seismometers. Finally, we compare seismometer-based HVSR from microtremor and earthquake (eHVSRs) sources. Results are consistent for 60%-70% of sites (i.e., both either do or do not have significant peaks, and when peaks are present, they occur at similar frequencies, <20% change). For sites with an mHVSR peak, the rate of corresponding eHVSR peaks is nearly 50%, whereas for sites without an mHVSR peak the eHVSR peak rate is low (about 20%). The mismatch rate for mHVSR peak sites is sufficiently high that the use of eHVSR to derive site response models is likely too optimistic (overestimates model effectiveness); mHVSR is preferred for consistency with information available in forward applications. [ABSTRACT FROM AUTHOR]

Site response is a critical consideration when assessing earthquake hazards. Site characterization is key to understanding site effects as influenced by seismic site conditions of the local geology. Thus, a number of geophysical site characterization methods were developed to meet the demand for accurate and cost-effective results. As a consequence, a number of studies have been administered periodically as blind trials to evaluate the state-of-practice on-site characterization. We present results from the Consortium of Organizations for Strong Motion Observation Systems (COSMOS) blind trials, which used data recorded from surface-based microtremor array methods (MAM) at four sites where geomorphic conditions vary from deep alluvial basins to an alpine valley. Thirty-four invited analysts participated. Data were incrementally released to 17 available analysts who participated in all four phases: (1) two-station arrays, (2) sparse triangular arrays, (3) complex nested triangular or circular arrays, and (4) all available geological control site information including drill hole data. Another set of 17 analysts provided results from two sites and two phases only. Although data from one site consisted of recordings from three-component sensors, the other three sites consisted of data recorded only by vertical-component sensors. The sites cover a range of noise source distributions, ranging from one site with a highly directional microtremor wave field to others with omni-directional (azimuthally distributed) wave fields. We review results from different processing techniques (e.g., beam-forming, spatial autocorrelation, cross-correlation, or seismic interferometry) applied by the analysts and compare the effectiveness between the differing wave field distributions. We define the M index as a quality index based on estimates of the time-averaged shear-wave velocity of the upper 10 (VS10), 30 (VS30), 100 (VS100), and 300 (VS300) meters and show its usefulness in quantitative comparisons of VS profiles from multiple analysts. Our findings are expected to aid in building an evidence-based consensus on preferred cost-effective arrays and processing methodology for future studies of seismic site effects. [ABSTRACT FROM AUTHOR]

Seismic attenuation and the associated quality factor (Q) have long been studied in various sub-disciplines of seismology, ranging from observational and engineering seismology to near-surface geophysics and soil/rock dynamics with particular emphasis on geotechnical earthquake engineering and engineering seismology. Within the broader framework of seismic site characterization, various experimental techniques have been adopted over the years to measure the near-surface shear-wave quality factor (QS). Common methods include active- and passive-source recording techniques performed at the free surface of soil deposits and within boreholes, as well as laboratory tests. This paper intends to provide an in-depth review of what Q is and, in particular, how QS is estimated in the current practice. After motivating the importance of this parameter in seismology, we proceed by recalling various theoretical definitions of Q and its measurement through laboratory tests, considering various deformation modes, most notably QP and QS. We next provide a review of the literature on QS estimation methods that use data from surface and borehole sensor recordings. We distinguish between active- and passive-source approaches, along with their pros and cons, as well as the state-of-the-practice and state-of-the-art. Finally, we summarize the phenomena associated with the high-frequency shear-wave attenuation factor (kappa) and its relation to Q, as well as other lesser-known attenuation parameters. [ABSTRACT FROM AUTHOR]

One of the biggest challenges in computer vision and deep learning is recognizing the emotion based on facial expression. Besides the challenge of model development in Deep Learning (DL), the dataset is considered one of the main factors that plays a crucial role in producing highly accurate deep learning frameworks for pattern classification, features extraction, and emotion recognition from images. Despite the existence of several dedicated datasets for the FER, the desired accuracy was not being achieved due to the enormous size of the available dataset, where some of such are unfeasible for regular computers or cloud computing services. In contrast, there is a lack of the other datasets' size, generalization, and quality. The objectives of this paper are two-fold. First, reviews the existing datasets. Second, presents a significant new dataset for facial expression called Emot-FE. This dataset contains 276,305 images of facial expressions formulated in a single file in the form of xlsx format. Emot-FE dataset has been filtered, pre-processed, labeled, and classified based on the seven labels (emotion's expressions). Thus, this dataset will be the largest in such format with high-quality images. This new dataset has been evaluated using VGGNet Convolutional Neural Networks where the recognition accuracy outperforms the previously achieved accuracies of the previous studies. This dataset is available for distribution to the researchers. [ABSTRACT FROM AUTHOR]

New seismic stations added to a regional seismic network cannot be used to calculate local magnitude (⁠ML⁠) until a revised regionwide amplitude decay function is developed. Each station must record a minimum number of local and regional earthquakes that meet specific amplitude requirements prior to recalibration of the amplitude decay function. Station component adjustments (⁠dML⁠; Uhrhammer et al., 2011) are then calculated after inverting for a new regional amplitude decay function, constrained by the sum of dML for long‐running stations. Therefore, there can be significant delay between when a new station starts contributing real‐time waveform packets and when data can be included in magnitude determinations. We propose the use of known estimates of seismic site conditions such as the time‐averaged shear‐wave velocity (⁠VS⁠) of the upper 30 m (⁠VS30⁠) and the site dominant frequency (⁠fd⁠) to calculate dML⁠. Previously established dML⁠, measured VS30⁠, and fd data are available for between 126 and 458 horizontal components (east-west and north-south) at 81 seismic stations in the California Integrated Seismic Network; dML data range from -1.10 to 0.39, VS30 values range from 202 to 1464  m/s⁠, and 440 fd values are compiled from earthquake and microseismic records that range from 0.13 to 21 Hz. We find VS30 and dML exhibit a positive coefficient of determination (⁠R=0.59⁠), indicating that as VS30 increases, dML increases. This implies that greater site amplification (lower VS30⁠) results in smaller dML⁠. fd and dML also generally exhibit a positive correlation (⁠R²<0.56⁠), which implies lower dML values are related to site resonance at depth‐dependent frequencies. Using the developed relationships, VS30 or fd measurements can be used to establish a provisional dML for newly established stations. This procedure allows new stations to contribute to regional network ML determinations immediately without the need to wait until a minimum set of earthquake data has been recorded. [ABSTRACT FROM AUTHOR]

In engineering seismology, the time-averaged shear-wave velocity (VS) of the upper 30 m of the crust (VS30) is the primary parameter used in ground-motion models to predict seismic site effects. VS30 is typically derived from in situ recordings of VS, although proxy-based approaches (using geologic and/or geomorphometric classifications) are provisionally adopted when measurement-based VS30 are sparse or not readily available. Despite the acceptance of proxy approaches, there are limited studies that examine the empirical relationships between VS30 and topographic attributes measured from various length scales and different resolutions of the digital elevation model. In this study, we examine the relationships between compiled VS30 measurements from 218 sites in southern California and topographic metrics of slope and relief measured over various length scales. We find that the correlations between topographic metrics and VS30 are weak but statistically significant. The correlations are improved when topographic slopes and relief are measured over length scales longer than typical hillslopes and VS30 sites are separated by different geologic groups. This is likely because VS30, especially on the rock sites, is better reflected in topographic metrics that capture large-scale topographic relief, as well as landscape positions such as hilltops and valley bottoms. [ABSTRACT FROM AUTHOR]