A statistical approach to account for azimuthal variability in single-station HVSR measurements

2020 ◽  
Vol 223 (2) ◽  
pp. 1040-1053
Author(s):  
Tianjian Cheng ◽  
Brady R Cox ◽  
Joseph P Vantassel ◽  
Lance Manuel
Keyword(s):  
Statistical Approach ◽  
Time Windows ◽  
Geometric Mean ◽  
Amplitude Spectrum ◽  
Spectral Ratio ◽  
Peak Frequency ◽  
Horizontal Component ◽  
Data Set ◽  
Single Station ◽  
Fourier Amplitude Spectrum

SUMMARY The horizontal-to-vertical spectral ratio (HVSR) of ambient noise is commonly used to infer a site's resonance frequency (${f_{0,site}}$). HVSR calculations are performed most commonly using the Fourier amplitude spectrum obtained from a single merged horizontal component (e.g. the geometric mean component) from a three-component sensor. However, the use of a single merged horizontal component implicitly relies on the assumptions of azimuthally isotropic seismic noise and 1-D surface and subsurface conditions. These assumptions may not be justified at many sites, leading to azimuthal variability in HVSR measurements that cannot be accounted for using a single merged component. This paper proposes a new statistical method to account for azimuthal variability in the peak frequency of HVSR curves (${f_{0,HVSR}}$). The method uses rotated horizontal components at evenly distributed azimuthal intervals to investigate and quantify azimuthal variability. To ensure unbiased statistics for ${f_{0,HVSR}}$ are obtained, a frequency-domain window-rejection algorithm is applied at each azimuth to automatically remove contaminated time windows in which the ${f_{0,HVSR}}$ values are statistical outliers relative to those obtained from the majority of windows at that azimuth. Then, a weighting scheme is used to account for different numbers of accepted time windows at each azimuth. The new method is applied to a data set of 114 HVSR measurements with significant azimuthal variability in ${f_{0,HVSR}}$, and is shown to reliably account for this variability. The methodology is also extended to the estimation of a complete lognormal-median HVSR curve that accounts for azimuthal variability. To encourage the adoption of this statistical approach to accounting for azimuthal variability in single-station HVSR measurements, the methods presented in this paper have been incorporated into hvsrpy, an open-source Python package for HVSR processing.

scholarly journals Detecting Site Resonant Frequency Using HVSR: Fourier versus Response Spectrum and the First versus the Highest Peak Frequency

2020 ◽  
Vol 110 (2) ◽  
pp. 427-440 ◽  
Author(s):  
Chuanbin Zhu ◽  
Fabrice Cotton ◽  
Marco Pilz
Keyword(s):  
Site Response ◽  
Response Spectrum ◽  
Amplitude Spectrum ◽  
Spectral Ratio ◽  
Peak Frequency ◽  
Fourier Amplitude ◽  
Resonant Frequencies ◽  
Overall Performance ◽  
Fourier Amplitude Spectrum ◽  
Hvsr Technique

ABSTRACT In this investigation, we examine the uncertainties using the horizontal-to-vertical spectral ratio (HVSR) technique on earthquake recordings to detect site resonant frequencies at 207 KiK-net sites. Our results show that the scenario dependence of response (pseudospectral acceleration) spectral ratio could bias the estimates of resonant frequencies for sites having multiple significant peaks with comparable amplitudes. Thus, the Fourier amplitude spectrum (FAS) should be preferred in computing HVSR. For more than 80% of the investigated sites, the first peak (in the frequency domain) on the average HVSR curve over multiple sites coincides with the highest peak. However, for sites with multiple peaks, the highest peak frequency (fp) is less susceptible to the selection criteria of significant peaks and the extent of smoothing to spectrum than the first peak frequency (f0). Meanwhile, in comparison to the surface-to-borehole spectral ratio, f0 tends to underestimate the predominant frequency (at which the largest amplification occurs) more than fp. In addition, in terms of characterizing linear site response, fp shows a better overall performance than f0. Based on these findings, we thus recommend that seismic network operators provide fp on the average HVSRFAS curve as a priority, ideally together with the average HVSRFAS curve in site characterization.

Single-station SVD-based polarization filtering of ground roll: Perfection and investigation of limitations and pitfalls

Geophysics ◽  
2012 ◽  
Vol 77 (2) ◽  
pp. V41-V59 ◽  
Author(s):  
Olena Tiapkina ◽  
Martin Landrø ◽  
Yuriy Tyapkin ◽  
Brian Link
Keyword(s):  
Time Window ◽  
Synthetic Data ◽  
Vertical Component ◽  
Horizontal Component ◽  
Frequency Bandwidth ◽  
Polarization Filter ◽  
Data Set ◽  
Single Station ◽  
Ground Roll ◽  
Polarization Filtering

The advent of single receiver point, multi-component geophones has necessitated that ground roll be removed in the processing flow rather than through acquisition design. A wide class of processing methods for ground-roll elimination is polarization filtering. A number of these methods use singular value decomposition (SVD) or some related transformations. We focus on a single-station SVD-based polarization filter that we consider to be one of the best in the industry. The method is comprised of two stages: (1) ground-roll detection and (2) ground-roll estimation and filtering. To detect the ground roll, a special attribute dependent on the singular values of a three-column matrix formed by a sliding time window is used. The ground roll is approximated and subtracted using the first two eigenimages of this matrix. To limit the possible damage to the signal, the filter operates within the record intervals where the ground roll is detected and within the ground-roll frequency bandwidth only. We improve the ground-roll detector to make it theoretically insensitive to ambient noise and more sensitive to the presence of ground roll. The advantage of the new detector is demonstrated on synthetic and field data sets. We estimate theoretically and with synthetic data the attenuation of the underlying reflections that can be caused by the polarization filter. We show that the underlying signal always loses almost all the energy on the vertical component and on the horizontal component in the ground-roll propagation plane and within the ground-roll frequency bandwidth. The only signal component, if it exists, that can retain a significant part of its energy is the horizontal component orthogonal to the above plane. When 2D 3C field operations are conducted, the signal particle motion can deviate from the ground-roll propagation plane and can therefore retain some of its energy due to a set of offline reflections. In the case of 3D 3C seismic surveys, the reflected signal always deviates from the ground-roll propagation plane on the receiver lines that do not contain the source. This is confirmed with a 2.5D 3C synthetic data set. We discuss when the ability of the filter to effectively subtract the ground roll may, or may not, allow us to ignore the inevitable harm that is done to the underlying reflected waves.

A statistical representation and frequency-domain window-rejection algorithm for single-station HVSR measurements

2020 ◽  
Vol 221 (3) ◽  
pp. 2170-2183 ◽  
Author(s):  
Brady R Cox ◽  
Tianjian Cheng ◽  
Joseph P Vantassel ◽  
Lance Manuel
Keyword(s):  
Frequency Domain ◽  
Lognormal Distribution ◽  
Ambient Noise ◽  
Spectral Ratio ◽  
Peak Frequency ◽  
Earthquake Ground Motion ◽  
Motion Processing ◽  
Rejection Algorithm ◽  
Single Station ◽  
Study Sites

SUMMARY The horizontal-to-vertical spectral ratio (HVSR) of ambient noise measurement is commonly used to estimate a site's resonance frequency (${f_0}$). For sites with a strong impedance contrast, the HVSR peak frequency (${f_{0,\mathrm{ HVSR}}}$) has been shown to be a good estimate of ${f_0}$. However, the random nature of ambient noise (both in time and space), in conjunction with variable environmental conditions and sensor coupling issues, can lead to uncertainty in ${f_{0,\mathrm{ HVSR}}}$ estimates. Hence, it is important to report ${f_{0,\mathrm{ HVSR}}}$ in a statistical manner (e.g. as a mean or median value with standard deviation). In this paper, we first discuss widely accepted procedures to process HVSR data and estimate the variance in ${f_{0,\mathrm{ HVSR}}}$. Then, we propose modifications to improve these procedures in two specific ways. First, we propose using a lognormal distribution to describe ${f_{0,\mathrm{ HVSR}}}$ rather than the more commonly used normal distribution. The use of a lognormal distribution for ${f_{0,\mathrm{ HVSR}}}$ has several advantages, including consistency with earthquake ground motion processing and allowing for a seamless transition between HVSR statistics in terms of both frequency and its reciprocal, period. Second, we introduce a new frequency-domain window-rejection algorithm to decrease variance and enhance data quality. Finally, we use examples of 114 high-variance HVSR measurements and 77 low-variance HVSR measurements collected at two case study sites to demonstrate the effectiveness of the new rejection algorithm and the proposed statistical approach. To encourage their adoption, and promote standardization, the rejection algorithm and lognormal statistics presented in this paper have been incorporated into hvsrpy, an open-source Python package for HVSR processing.

scholarly journals Is the Genotype-Phenotype Map Modular?: A Statistical Approach Using Mouse Quantitative Trait Loci Data

Genetics ◽  
2000 ◽  
Vol 156 (1) ◽  
pp. 305-311 ◽  
Author(s):  
Jason G Mezey ◽  
James M Cheverud ◽  
Günter P Wagner
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Statistical Approach ◽  
Genetic Effects ◽  
Statistical Distribution ◽  
Data Set ◽  
Alveolar Region ◽  
Phenotypic Characters ◽  
Statistical Support ◽  
Complex Characters

Abstract Various theories about the evolution of complex characters make predictions about the statistical distribution of genetic effects on phenotypic characters, also called the genotype-phenotype map. With the advent of QTL technology, data about these distributions are becoming available. In this article, we propose simple tests for the prediction that functionally integrated characters have a modular genotype-phenotype map. The test is applied to QTL data on the mouse mandible. The results provide statistical support for the notion that the ascending ramus region of the mandible is modularized. A data set comprising the effects of QTL on a more extensive portion of the phenotype is required to determine if the alveolar region of the mandible is also modularized.

scholarly journals Fluconazole MIC and the Fluconazole Dose/MIC Ratio Correlate with Therapeutic Response among Patients with Candidemia

2005 ◽  
Vol 49 (8) ◽  
pp. 3171-3177 ◽  
Author(s):  
Cornelius J. Clancy ◽  
Victor L. Yu ◽  
Arthur J. Morris ◽  
David R. Snydman ◽  
M. Hong Nguyen
Keyword(s):  
Therapeutic Response ◽  
Geometric Mean ◽  
Therapeutic Failure ◽  
Response To Therapy ◽  
Success Rates ◽  
Therapeutic Success ◽  
Data Set ◽  
In Vitro Susceptibility ◽  
Dose Dependent

ABSTRACT We tested 32 Candida isolates recovered in the early 1990s from the bloodstreams of patients with candidemia for in vitro susceptibility to fluconazole and determined if MIC and/or the daily dose of fluconazole/MIC ratio correlated with the response to therapy. This is a unique data set since 87.5% (28/32) of patients were treated with fluconazole doses now considered to be inadequate (≤200 mg), which contributed to high therapeutic failure rates (53% [17/32]). The geometric mean MIC and dose/MIC ratio for isolates associated with therapeutic failure (11.55 μg/ml and 14.3, respectively) differed significantly from values associated with therapeutic success (0.95 μg/ml and 219.36 [P = 0.0009 and 0.0004, respectively]). The therapeutic success rates among patients infected with susceptible (MIC ≤ 8 μg/ml), susceptible-dose dependent (S-DD) (MIC = 16 or 32 μg/ml), and resistant (MIC ≥ 64 μg/ml) isolates were 67% (14/21), 20% (1/5), and 0% (0/6), respectively. A dose/MIC ratio >50 was associated with a success rate of 74% (14/19), compared to 8% (1/13) for a dose/MIC ratio ≤50 (P = 0.0003). Our data suggest that both fluconazole MIC and dose/MIC ratio correlate with the therapeutic response to fluconazole among patients with candidemia. In clinical practice, dose/MIC ratio might prove easier to interpret than breakpoint MICs, since it quantitates the effects of increasing fluconazole doses that are alluded to in the S-DD designation.

scholarly journals Complex networks of earthquakes and aftershocks

2005 ◽  
Vol 12 (1) ◽  
pp. 1-11 ◽  
Author(s):  
M. Baiesi ◽  
M. Paczuski
Keyword(s):  
Complex Networks ◽  
Time Windows ◽  
Scaling Law ◽  
Relative Strength ◽  
Modern Theory ◽  
Significant Information ◽  
Data Set ◽  
Scale Free ◽  
Main Shocks ◽  
Aftershock Sequences

Abstract. We invoke a metric to quantify the correlation between any two earthquakes. This provides a simple and straightforward alternative to using space-time windows to detect aftershock sequences and obviates the need to distinguish main shocks from aftershocks. Directed networks of earthquakes are constructed by placing a link, directed from the past to the future, between pairs of events that are strongly correlated. Each link has a weight giving the relative strength of correlation such that the sum over the incoming links to any node equals unity for aftershocks, or zero if the event had no correlated predecessors. A correlation threshold is set to drastically reduce the size of the data set without losing significant information. Events can be aftershocks of many previous events, and also generate many aftershocks. The probability distribution for the number of incoming and outgoing links are both scale free, and the networks are highly clustered. The Omori law holds for aftershock rates up to a decorrelation time that scales with the magnitude, m, of the initiating shock as tcutoff~10β m with β~-3/4. Another scaling law relates distances between earthquakes and their aftershocks to the magnitude of the initiating shock. Our results are inconsistent with the hypothesis of finite aftershock zones. We also find evidence that seismicity is dominantly triggered by small earthquakes. Our approach, using concepts from the modern theory of complex networks, together with a metric to estimate correlations, opens up new avenues of research, as well as new tools to understand seismicity.

scholarly journals Procedural Revision to the AOAC Germicidal Spray Products as Disinfectants Test Method: Establishment of Minimum and Maximum Log Density Values for Test Microbes on Inoculated Carriers

2013 ◽  
Vol 96 (3) ◽  
pp. 567-572 ◽  
Author(s):  
Rebecca M Pines ◽  
Stephen F Tomasino ◽  
Michele P Cottrill ◽  
Gordon C Hamilton ◽  
Albert E Parker
Keyword(s):  
Count Data ◽  
Geometric Mean ◽  
Test Method ◽  
Official Method ◽  
Data Set ◽  
Label Claim ◽  
Density Values ◽  
Good Repeatability ◽  
The Mean ◽  
Test Microbe

Abstract The AOAC Germicidal Spray Products as Disinfectants test method (AOAC Official Method 961.02) is used to measure the efficacy of spray products on hard inanimate surfaces; however, the method does not provide procedures to determine the population of the test microbe on inoculated glass slide carriers (e.g., carrier counts reported as CFU/carrier). Without a method to measure and monitor carrier counts, the associated efficacy data may not be reliable and repeatable. This report provides a standardized procedure to address this issue and, based on carrier count data collected by four laboratories from 2000 to 2010, proposes a specific range for the mean log density per carrier as a requirement. Laboratory-based carrier count data were collected concurrently with 116 Method 961.02 efficacy tests conducted on spray products bearing claims against Pseudomonas aeruginosa and Staphylococcus aureus. For many of the tests a soil load (SL) was added to the inoculum (as specified on the product label claim). Six carriers were assayed per test for a total of 696 carriers. All but two of the 116 mean log densities were at least 5.0 (a geometric mean of 1.0 × 105 CFU/carrier). Across the four combinations of microbes and SL treatments, the mean TestLD (mean log density across all enumerated carriers in a test) ranged from approximately 6.0 (a geometric mean of 0.9 × 106 CFU/carrier) to 6.3 (a geometric mean of 2.0 × 106 CFU/carrier). Across all microbes and SL treatments, the mean log density (±SEM) was 6.2 (±0.07) per carrier (a geometric mean of 1.5 × 106 CFU/carrier). The mean log density for six carriers per test showed good repeatability (0.32) and reproducibility (0.34). The proposed requirement for S. aureus tests and P. aeruginosa tests is a mean log density (across six carriers) between 5.0 and 6.5. A separate 2009 study at three laboratories was conducted to evaluate the persistence of P. aeruginosa, S. aureus, and Salmonella enterica on glass carriers. Based on the persistence data, a 2 h use period is proposed for using the inoculated carriers post drying. The persistence data set was also used to assess the carrier counts for S. enterica. The carrier counts were approximately one log lower for S. enterica compared to S. aureus and P. aeruginosa; a range of 4.0 to 5.5 logs is proposed as a requirement for S. enterica tests.

scholarly journals Time Windows Voting Classifier for COVID-19 Mortality Prediction

2021 ◽  
Author(s):  
TIONG GOH ◽  
MengJun Liu
Keyword(s):  
Time Window ◽  
Majority Vote ◽  
Time Windows ◽  
Mortality Prediction ◽  
Data Set ◽  
Risk Of Mortality ◽  
Machine Learning Model ◽  
Partitioned Data ◽  
Considerable Impact ◽  
Extended Duration

The ability to predict COVID-19 patients' level of severity (death or survival) enables clinicians to prioritise treatment. Recently, using three blood biomarkers, an interpretable machine learning model was developed to predict the mortality of COVID-19 patients. The method was reported to be suffering from performance stability because the identified biomarkers are not consistent predictors over an extended duration. To sustain performance, the proposed method partitioned data into three different time windows. For each window, an end-classifier, a mid-classifier and a front-classifier were designed respectively using the XGboost single tree approach. These time window classifiers were integrated into a majority vote classifier and tested with an isolated test data set. The voting classifier strengthens the overall performance of 90% cumulative accuracy from a 14 days window to a 21 days prediction window. An additional 7 days of prediction window can have a considerable impact on a patient's chance of survival. This study validated the feasibility of the time window voting classifier and further support the selection of biomarkers features set for the early prognosis of patients with a higher risk of mortality.

Volcanic Tremor Extraction and Earthquake Detection Using Music Information Retrieval Algorithms

2021 ◽  
Author(s):  
Zahra Zali ◽  
Matthias Ohrnberger ◽  
Frank Scherbaum ◽  
Fabrice Cotton ◽  
Eva P. S. Eibl
Keyword(s):  
Amplitude Spectrum ◽  
Volcanic Tremor ◽  
Volcanic Eruptions ◽  
Detection Algorithm ◽  
Transient Signal ◽  
Volcanic Origin ◽  
Time Frequency ◽  
Reconstructed Signal ◽  
Single Station ◽  
Transient Events

Abstract Volcanic tremor signals are usually observed before or during volcanic eruptions and must be monitored to evaluate the volcanic activity. A challenge in studying seismic signals of volcanic origin is the coexistence of transient signal swarms and long-lasting volcanic tremor signals. Separating transient events from volcanic tremors can, therefore, contribute to improving upon our understanding of the underlying physical processes. Exploiting the idea of harmonic–percussive separation in musical signal processing, we develop a method to extract the harmonic volcanic tremor signals and to detect transient events from seismic recordings. Based on the similarity properties of spectrogram frames in the time–frequency domain, we decompose the signal into two separate spectrograms representing repeating (harmonic) and nonrepeating (transient) patterns, which correspond to volcanic tremor signals and earthquake signals, respectively. We reconstruct the harmonic tremor signal in the time domain from the complex spectrogram of the repeating pattern by only considering the phase components for the frequency range in which the tremor amplitude spectrum is significantly contributing to the energy of the signal. The reconstructed signal is, therefore, clean tremor signal without transient events. Furthermore, we derive a characteristic function suitable for the detection of transient events (e.g., earthquakes) by integrating amplitudes of the nonrepeating spectrogram over frequency at each time frame. Considering transient events like earthquakes, 78% of the events are detected for signal-to-noise ratio = 0.1 in our semisynthetic tests. In addition, we compared the number of detected earthquakes using our method for one month of continuous data recorded during the Holuhraun 2014–2015 eruption in Iceland with the bulletin presented in Ágústsdóttir et al. (2019). Our single station event detection algorithm identified 84% of the bulletin events. Moreover, we detected a total of 12,619 events, which is more than twice the number of the bulletin events.

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