Azimuth estimation capabilities of the ARCESS regional seismic array

1993 ◽  
Vol 83 (4) ◽  
pp. 1213-1231
Author(s):  
Dorthe B. Carr
Keyword(s):  
Time Window ◽  
Source Region ◽  
Event Data ◽  
Small Aperture ◽  
Data Set ◽  
Low Frequencies ◽  
Source Regions ◽  
The Mean ◽  
Local Geology ◽  
Regional Arrays

Abstract The effect of local geology and noise conditions on the performance of a small regional array is investigated by comparing the regional Pn backazimuth estimation capabilities of the ARCESS array in northern Norway to the NORESS array. A broadband frequency-wavenumber estimator was used to calculate backazimuths from the Pn arrival for each of 203 regional events recorded at ARCESS while varying element spacing, frequency band, and time window. Most of the errors in backazimuth are less than 20° when appropriate parameter combinations are used, and mean backazimuth errors are close to zero. The best results are obtained using a 13-element configuration that has a 1.4 km aperture and a maximum station spacing of about 600 m. With the 13-element configuration and the data filtered to include frequencies between 3 and 10 Hz, the mean errors for the 203 event data set are less than 0.9°, and S.D. are as small as 16.9°. There are differences seen in the backazimuth estimation capabilities of ARCESS and NORESS with specific parameter combinations. The larger aperture configurations (10- and 17-elements) have smaller means at ARCESS, although the precision is about the same. The estimates using unfiltered data at ARCESS are poor, because of local noise conditions that increase the level of background noise at low frequencies. Overall the precision is better at NORESS, but both regional arrays have the best results using the 13-element configuration and filtering the data in the middle frequency range (3 to 10 Hz). Other factors investigated include SNR and source region. Backazimuth estimation statistics improve if only events with 5 dB of SNR are included in the data set at both ARCESS and NORESS. The mean errors move closer to zero and standard deviations decrease. The differences between the two arrays are not as pronounced. There are some path effects from different source regions around the ARCESS array. However, combinations of small aperture configurations and middle (3 to 10 Hz) frequency bands work well for events over the entire distance range of 30 to 1200 km. ARCESS and NORESS have similar backazimuth estimation capabilities even though there are differences in the local geology and noise conditions. Because a 13-element configuration produces reliable results for both arrays, it would be reasonable to reduce the number of elements in a regional array. This in turn will reduce the costs associated with building and deploying small regional arrays.

Azimuth estimation capabilities of the NORESS regional seismic array

1990 ◽  
Vol 80 (6B) ◽  
pp. 1999-2015 ◽  
Author(s):  
Dorthe A. Bame ◽  
Marianne C. Walck ◽  
Kathie L. Hiebert-Dodd
Keyword(s):  
Data Base ◽  
Frequency Band ◽  
Time Window ◽  
Source Region ◽  
Seismic Array ◽  
Small Aperture ◽  
Data Set ◽  
Frequency Bands ◽  
Wide Range ◽  
Standard Deviations

Abstract We have investigated the regional Pn backazimuth estimation capabilities of the NORESS seismic array as a function of element spacing, frequency band, and time window to determine which parameters are optimal for reducing azimuth errors. We used a broadband frequency-wavenumber estimator to calculate backazimuths from the Pn arrival for each of 274 regional events recorded at NORESS for 126 parameter combinations. The large data base provides a wide range of signal-to-noise ratio (SNR) (0 to 70 dB), distance (up to 10.5°), and azimuth characteristics, and includes identified earthquakes and explosions as well as “unknown” sources. Most of the errors in backazimuth are less than 20° when appropriate parameters are used, and mean backazimuth errors are close to zero. The best results are obtained using a 13-element array configuration that has a 1.4 km aperture and a maximum station spacing of about 600 m. With the 13-element configuration and the data filtered to include frequencies between 3 and 10 Hz, the mean errors for the 274 event data set are less than 1.4°, and standard deviations are as small as ± 11.1°. The entire array also produces good results for 3 to 10 Hz, and a 9-element configuration (two inner rings) performs well at high frequencies. The five-element B-ring (600 m aperture) appears to be important in obtaining good backazimuth estimates for regional Pn waves. Of the frequency bands considered in this study, the 3 to 6 Hz, 4 to 8 Hz, and 5 to 10 Hz bands yield the most reliable backazimuth estimates, even better than an “optimal” band that calculates the azimuth in the fixed frequency band that has the largest average SNR. The time-window length has little effect on the backazimuth estimates. Other factors investigated include SNR, source region, and phase type. We found that event backazimuth accuracy degrades if the SNR of a beamed Pn signal is less than 5 dB in the frequency band of interest. Conversely, the backazimuth estimation statistics improve if only events with 5 dB of SNR are included in the event set. These data sets yield near-zero mean errors and smaller standard deviations than the entire data set. For specific source regions, standard deviations are as low as 2° for some parameter combinations, but there can also be large biases in the backazimuth estimates. Events farther than 500 km from NORESS tend to have larger azimuth errors than the closer events, but combinations of small aperture configurations and middle (3 to 10 Hz) frequency bands work well for events over the entire distance range of 40 to 1200 km. The Pn arrival and RONAPP azimuths calculated from the Lg phase have similar accuracy statistics for a 220 event common data base, implying that the two phases work equally well for regional event backazimuth estimation. In fact, averaging of the Lg and Pn estimates provides the most accurate backazimuths relative to PDE reference information.

scholarly journals Validation of SCIAMACHY HDO/H<sub>2</sub>O measurements using the TCCON and NDACC-MUSICA networks

2015 ◽  
Vol 8 (4) ◽  
pp. 1799-1818 ◽  
Author(s):  
R. A. Scheepmaker ◽  
C. Frankenberg ◽  
N. M. Deutscher ◽  
M. Schneider ◽  
S. Barthlott ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Hydrological Cycle ◽  
Source Region ◽  
Total Carbon ◽  
Atmospheric Composition ◽  
The Tibetan Plateau ◽  
Wet Season ◽  
Data Set ◽  
The Mean ◽  
The Impact

Abstract. Measurements of the atmospheric HDO/H2O ratio help us to better understand the hydrological cycle and improve models to correctly simulate tropospheric humidity and therefore climate change. We present an updated version of the column-averaged HDO/H2O ratio data set from the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY). The data set is extended with 2 additional years, now covering 2003–2007, and is validated against co-located ground-based total column δD measurements from Fourier transform spectrometers (FTS) of the Total Carbon Column Observing Network (TCCON) and the Network for the Detection of Atmospheric Composition Change (NDACC, produced within the framework of the MUSICA project). Even though the time overlap among the available data is not yet ideal, we determined a mean negative bias in SCIAMACHY δD of −35 ± 30‰ compared to TCCON and −69 ± 15‰ compared to MUSICA (the uncertainty indicating the station-to-station standard deviation). The bias shows a latitudinal dependency, being largest (∼ −60 to −80‰) at the highest latitudes and smallest (∼ −20 to −30‰) at the lowest latitudes. We have tested the impact of an offset correction to the SCIAMACHY HDO and H2O columns. This correction leads to a humidity- and latitude-dependent shift in δD and an improvement of the bias by 27‰, although it does not lead to an improved correlation with the FTS measurements nor to a strong reduction of the latitudinal dependency of the bias. The correction might be an improvement for dry, high-altitude areas, such as the Tibetan Plateau and the Andes region. For these areas, however, validation is currently impossible due to a lack of ground stations. The mean standard deviation of single-sounding SCIAMACHY–FTS differences is ∼ 115‰, which is reduced by a factor ∼ 2 when we consider monthly means. When we relax the strict matching of individual measurements and focus on the mean seasonalities using all available FTS data, we find that the correlation coefficients between SCIAMACHY and the FTS networks improve from 0.2 to 0.7–0.8. Certain ground stations show a clear asymmetry in δD during the transition from the dry to the wet season and back, which is also detected by SCIAMACHY. This asymmetry points to a transition in the source region temperature or location of the water vapour and shows the added information that HDO/H2O measurements provide when used in combination with variations in humidity.

Modeling delay-fired explosion spectra at regional distances

1992 ◽  
Vol 82 (6) ◽  
pp. 2430-2447
Author(s):  
M. C. Chapman ◽  
G. A. Bollinger ◽  
M. S. Sibol
Keyword(s):  
Source Region ◽  
Total Duration ◽  
Recording Site ◽  
Data Set ◽  
Low Frequencies ◽  
Eastern Kentucky ◽  
Independent Amplitude ◽  
Short Period ◽  
Amplitude Spectra ◽  
Good Signal

Abstract The objectives of this study are to model the observed seismic spectra from large industrial explosions using information obtained from blaster's logs and to compare the explosion spectra with those of small earthquake signals from the same source region. The data set consists of digital waveforms from four mining explosions (200,000 + lb. of explosives each) and two earthquakes (M = 3.5 and 4.0) in eastern Kentucky. The data were recorded on a short-period regional network at distances ranging from 180 to 400 km and have good signal-to-noise ratios at frequencies from 0.5 to 10 Hz. The explosion amplitude spectra differ markedly from those of the earthquakes, by exhibiting strong time-independent amplitude modulations. This spectral modulation is directly attributable to the explosive charge geometry and firing sequence and is largely independent of source-station path and recording site. Modeling of the explosion source spectra shows that the major contributor to the modulated character of the spectra are amplitude minima at frequencies related to the total duration of the explosion sequence. Another important effect is amplitude reinforcement at low frequencies (e.g., 5 Hz) due to the comparatively long delay (0.2 sec) between the firing of individual rows of explosives. These features dominate both Pg and Lg amplitude spectra at frequencies less than 7 Hz. Accurate modeling of the observed spectra at frequencies greater than a few Hertz requires that the azimuth of the recording site be taken into account. Also, the spectra at higher frequencies become sensitive to random variations in the firing times of any of the various subexplosions.

scholarly journals New constants for Sampson-Lieske theory of the Galilean satellites of Jupiter from mutual occultation data

1996 ◽  
Vol 172 ◽  
pp. 145-150 ◽  
Author(s):  
R. Vasundhara ◽  
J.E. Arlot ◽  
P. Descamps
Keyword(s):  
Event Data ◽  
Galilean Satellites ◽  
Data Set ◽  
Secular Variations ◽  
Satellites Of Jupiter ◽  
The Mean ◽  
Occultation Data ◽  
Mutual Occultation

New constants for Sampson-Lieske theory of the Galilean Satellites have been derived using 6360 individual photographic positions (1891–1990) and 438 pseudo astrometric positions from mutual occultations during 1973, 1979, 1985 and 1991. Using these new sets of constants, significant improvement is noticed in the O-C values of the sky plane coordinates of the mutual event data set and residuals in longitude for Io and Europa are found to improve. Problems concerning the inclusion of mutual event data in attempting evaluation of secular variations of the mean motions of the satellites are discussed.

scholarly journals Relationship between large-scale ionospheric field-aligned currents and electron/ion precipitations: DMSP observations

2020 ◽  
Vol 72 (1) ◽  
Author(s):  
Chao Xiong ◽  
Claudia Stolle ◽  
Patrick Alken ◽  
Jan Rauberg
Keyword(s):  
Time Distribution ◽  
Large Scale ◽  
Particle Flux ◽  
Particle Energy ◽  
Lower Latitude ◽  
Particle Precipitation ◽  
Data Set ◽  
The Mean ◽  
Two Parameters ◽  
Meteorological Satellite

Abstract In this study, we have derived field-aligned currents (FACs) from magnetometers onboard the Defense Meteorological Satellite Project (DMSP) satellites. The magnetic latitude versus local time distribution of FACs from DMSP shows comparable dependences with previous findings on the intensity and orientation of interplanetary magnetic field (IMF) By and Bz components, which confirms the reliability of DMSP FAC data set. With simultaneous measurements of precipitating particles from DMSP, we further investigate the relation between large-scale FACs and precipitating particles. Our result shows that precipitation electron and ion fluxes both increase in magnitude and extend to lower latitude for enhanced southward IMF Bz, which is similar to the behavior of FACs. Under weak northward and southward Bz conditions, the locations of the R2 current maxima, at both dusk and dawn sides and in both hemispheres, are found to be close to the maxima of the particle energy fluxes; while for the same IMF conditions, R1 currents are displaced further to the respective particle flux peaks. Largest displacement (about 3.5°) is found between the downward R1 current and ion flux peak at the dawn side. Our results suggest that there exists systematic differences in locations of electron/ion precipitation and large-scale upward/downward FACs. As outlined by the statistical mean of these two parameters, the FAC peaks enclose the particle energy flux peaks in an auroral band at both dusk and dawn sides. Our comparisons also found that particle precipitation at dawn and dusk and in both hemispheres maximizes near the mean R2 current peaks. The particle precipitation flux maxima closer to the R1 current peaks are lower in magnitude. This is opposite to the known feature that R1 currents are on average stronger than R2 currents.

Approximate Bayesian inference for joint linear and partially linear modeling of longitudinal zero-inflated count and time to event data

2021 ◽  
pp. 096228022110028
Author(s):  
T Baghfalaki ◽  
M Ganjali
Keyword(s):  
Linear Model ◽  
Joint Modeling ◽  
Partially Linear Model ◽  
Event Data ◽  
Time To Event ◽  
Data Set ◽  
Partially Linear ◽  
Time To Event Data ◽  
Count Response ◽  
Approximate Bayesian

Joint modeling of zero-inflated count and time-to-event data is usually performed by applying the shared random effect model. This kind of joint modeling can be considered as a latent Gaussian model. In this paper, the approach of integrated nested Laplace approximation (INLA) is used to perform approximate Bayesian approach for the joint modeling. We propose a zero-inflated hurdle model under Poisson or negative binomial distributional assumption as sub-model for count data. Also, a Weibull model is used as survival time sub-model. In addition to the usual joint linear model, a joint partially linear model is also considered to take into account the non-linear effect of time on the longitudinal count response. The performance of the method is investigated using some simulation studies and its achievement is compared with the usual approach via the Bayesian paradigm of Monte Carlo Markov Chain (MCMC). Also, we apply the proposed method to analyze two real data sets. The first one is the data about a longitudinal study of pregnancy and the second one is a data set obtained of a HIV study.

scholarly journals Pseudosparse neural coding in the visual system of primates

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Sidney R. Lehky ◽  
Keiji Tanaka ◽  
Anne B. Sereno
Keyword(s):  
Neural Coding ◽  
Macaque Monkey ◽  
Implicit Assumption ◽  
Data Set ◽  
Efficient Coding ◽  
Population Responses ◽  
Lateral Intraparietal Cortex ◽  
Neural Populations ◽  
Neurophysiological Data ◽  
The Mean

AbstractWhen measuring sparseness in neural populations as an indicator of efficient coding, an implicit assumption is that each stimulus activates a different random set of neurons. In other words, population responses to different stimuli are, on average, uncorrelated. Here we examine neurophysiological data from four lobes of macaque monkey cortex, including V1, V2, MT, anterior inferotemporal cortex, lateral intraparietal cortex, the frontal eye fields, and perirhinal cortex, to determine how correlated population responses are. We call the mean correlation the pseudosparseness index, because high pseudosparseness can mimic statistical properties of sparseness without being authentically sparse. In every data set we find high levels of pseudosparseness ranging from 0.59–0.98, substantially greater than the value of 0.00 for authentic sparseness. This was true for synthetic and natural stimuli, as well as for single-electrode and multielectrode data. A model indicates that a key variable producing high pseudosparseness is the standard deviation of spontaneous activity across the population. Consistently high values of pseudosparseness in the data demand reconsideration of the sparse coding literature as well as consideration of the degree to which authentic sparseness provides a useful framework for understanding neural coding in the cortex.

scholarly journals Numerical simulations of spreading of the Persian Gulf outflow into the Oman Sea

Ocean Science ◽  
2010 ◽  
Vol 6 (4) ◽  
pp. 887-900 ◽  
Author(s):  
M. Ezam ◽  
A. A. Bidokhti ◽  
A. H. Javid
Keyword(s):  
Persian Gulf ◽  
Bottom Topography ◽  
World Ocean ◽  
Monthly Precipitation ◽  
Boundary Current ◽  
Surface Salinity ◽  
Data Set ◽  
Open Boundaries ◽  
The Mean ◽  
The Persian Gulf

Abstract. A three dimensional numerical model namely POM (Princeton Ocean Model) and observational data are used to study the Persian Gulf outflow structure and its spreading pathways during 1992. In the model, the monthly wind speed data were taken from ICOADS (International Comprehensive Ocean-Atmosphere Data Set) and the monthly SST (sea surface temperatures) were taken from AVHRR (Advanced Very High Resolution Radiometer) with the addition of monthly net shortwave radiations from NCEP (National Center for Environmental Prediction). The mean monthly precipitation rates from NCEP data and the calculated evaporation rates are used to impose the surface salinity fluxes. At the open boundaries the temperature and salinity were prescribed from the mean monthly climatological values from WOA05 (World Ocean Atlas 2005). Also the four major components of the tide were prescribed at the open boundaries. The results show that the outflow mainly originates from two branches at different depths in the Persian Gulf. The permanent branch exists during the whole year deeper than 40 m along the Gulf axis and originates from the inner parts of the Persian Gulf. The other seasonal branch forms in the vicinity of the shallow southern coasts due to high evaporation rates during winter. Near the Strait of Hormuz the two branches join and form the main outflow source water. The results of simulations reveal that during the winter the outflow boundary current mainly detaches from the coast well before Ras Al Hamra Cape, however during summer the outflow seems to follow the coast even after this Cape. This is due to a higher density of the colder outflow that leads to more sinking near the coast in winter. Thus, the outflow moves to a deeper depth of about 500 m (for which some explanations are given) while the main part detaches and spreads at a depth of about 300 m. However in summer it all moves at a depth of about 200–250 m. During winter, the deeper, stronger and wider outflow is more affected by the steep topography, leading to separation from the coast. While during summer, the weaker and shallower outflow is less influenced by bottom topography and so continues along the boundary.

scholarly journals EPID-22. CHARACTERIZATION OF RADIATION-INDUCED MENINGIOMAS IN A BRAIN TUMOR DATABASE

2020 ◽  
Vol 22 (Supplement_2) ◽  
pp. ii83-ii83
Author(s):  
Nilan Vaghjiani ◽  
Andrew Schwieder ◽  
Sravya Uppalapati ◽  
Zachary Kons ◽  
Elizabeth Kazarian ◽  
...  
Keyword(s):  
African American ◽  
Brain Tumor ◽  
Radiation Treatment ◽  
Latency Period ◽  
Data Set ◽  
Who Grade ◽  
Significant Difference ◽  
Radiation Induced ◽  
Grade Ii ◽  
The Mean

Abstract PURPOSE Radiation-induced meningiomas (RIMs) are associated with previous exposure to therapeutic irradiation. RIMs are rare and have not been well characterized relative to spontaneous meningiomas (SMs). METHODS 1003 patients with proven or presumed meningiomas were identified from the VCU brain tumor database. Chart review classified RIM patients and their characteristics. RESULTS Of the 1003 total patients, 76.47% were female with a mean ± SD age of 67.55 ± 15.50 years. 15 RIM patients were identified (66.67% female), with a mean ± SD age of 52.67 ± 15.46 years, 5 were African American and 10 were Caucasian. The incidence of RIMs was 1.49% in our data set. The mean age at diagnosis was 43.27 ± 15.06 years. The mean latency was 356.27 ± 116.96 months. The mean initiating dose was 44.28 ± 14.68 Gy. There was a significant difference between mean latency period and ethnicity, 258.3 months for African American population, and 405.2 months for Caucasian population (p = 0.003). There was a significant difference between the mean number of lesions in females (2.8) versus males (1.2; p = 0.046). Of the RIMs with characterized histology, 6 (55%) were WHO grade II and 5 (45%) were WHO grade I, demonstrating a prevalence of grade II tumors approximately double that found with SMs. RIMs were treated with combinations of observation, surgery, radiation, and medical therapy. Of the 8 patients treated with radiation, 4 demonstrated response. 8 of the 15 patients (53%) demonstrated recurrence/progression despite treatment. CONCLUSION RIMs are important because of the associated higher grade histology, gender, and ethnic incidences, and increased recurrence/progression compared to SMs. Despite the presumed contributory role of prior radiation, RIMs demonstrate a significant rate of responsiveness to radiation treatment.

scholarly journals Monitoring Vegetation Greenness in Response to Climate Variation along the Elevation Gradient in the Three-River Source Region of China

2021 ◽  
Vol 10 (3) ◽  
pp. 193
Author(s):  
Zhaoqi Wang ◽  
Xiang Liu ◽  
Hao Wang ◽  
Kai Zheng ◽  
Honglin Li ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Source Region ◽  
Elevation Gradient ◽  
Climate Variation ◽  
Mean Annual Precipitation ◽  
Near Infrared Reflectance ◽  
Area Index ◽  
Vegetation Greenness ◽  
The Mean ◽  
The Impact

The Three-River Source Region (TRSR) is vital to the ecological security of China. However, the impact of global warming on the dynamics of vegetation along the elevation gradient in the TRSR remains unclear. Accordingly, we used multi-source remote sensing vegetation indices (VIs) (GIMMS (Global Inventory Modeling and Mapping Studies) LAI (Leaf Area Index), GIMMS NDVI (Normalized Difference Vegetation Index), GLOBMAP (Global Mapping) LAI, MODIS (Moderate Resolution Imaging Spectroradiometer) EVI (Enhanced Vegetation Index), MODIS NDVI, and MODIS NIRv (near-infrared reflectance of vegetation)) and digital elevation model data to study the changes of VGEG (Vegetation Greenness along the Elevation Gradient) in the TRSR from 2001 to 2016. Results showed that the areas with a positive correlation of vegetation greenness and elevation accounted for 36.34 ± 5.82% of the study areas. The interannual variations of VGEG showed that the significantly changed regions were mainly observed in the elevation gradient of 4–5 km. The VGEG was strongest in the elevation gradient of 4–5 km and weakest in the elevation gradient of >5 km. Correlation analysis showed that the mean annual temperature was positively correlated with VIs, and the effect of the mean annual precipitation on VIs was more obvious at low altitude than in high altitude. This study contributes to our understanding of the VGEG variation in the TRSR under global climate variation and also helps in the prediction of future carbon cycle patterns.

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