scholarly journals Erratum: A spatial cross‐correlation model for ground motion spectral accelerations at multiple periods

2020 ◽  
Vol 49 (3) ◽  
pp. 315-316
Author(s):  
Christophe Loth ◽  
Jack W. Baker
Keyword(s):  
Ground Motion ◽  
Cross Correlation ◽  
Correlation Model ◽  
Spectral Accelerations

scholarly journals Ground motions in urban Los Angeles from the 2019 Ridgecrest earthquake sequence

2021 ◽  
pp. 875529302110039
Author(s):  
Filippos Filippitzis ◽  
Monica D Kohler ◽  
Thomas H Heaton ◽  
Robert W Graves ◽  
Robert W Clayton ◽  
...  
Keyword(s):  
Los Angeles ◽  
Ground Motion ◽  
Ground Motions ◽  
Earthquake Sequence ◽  
Motion Prediction ◽  
Ground Motion Prediction Equations ◽  
Prediction Equations ◽  
Ground Motion Prediction ◽  
Velocity Models ◽  
Spectral Accelerations

We study ground-motion response in urban Los Angeles during the two largest events (M7.1 and M6.4) of the 2019 Ridgecrest earthquake sequence using recordings from multiple regional seismic networks as well as a subset of 350 stations from the much denser Community Seismic Network. In the first part of our study, we examine the observed response spectral (pseudo) accelerations for a selection of periods of engineering significance (1, 3, 6, and 8 s). Significant ground-motion amplification is present and reproducible between the two events. For the longer periods, coherent spectral acceleration patterns are visible throughout the Los Angeles Basin, while for the shorter periods, the motions are less spatially coherent. However, coherence is still observable at smaller length scales due to the high spatial density of the measurements. Examining possible correlations of the computed response spectral accelerations with basement depth and Vs30, we find the correlations to be stronger for the longer periods. In the second part of the study, we test the performance of two state-of-the-art methods for estimating ground motions for the largest event of the Ridgecrest earthquake sequence, namely three-dimensional (3D) finite-difference simulations and ground motion prediction equations. For the simulations, we are interested in the performance of the two Southern California Earthquake Center 3D community velocity models (CVM-S and CVM-H). For the ground motion prediction equations, we consider four of the 2014 Next Generation Attenuation-West2 Project equations. For some cases, the methods match the observations reasonably well; however, neither approach is able to reproduce the specific locations of the maximum response spectral accelerations or match the details of the observed amplification patterns.

Responses of neurons in the auditory pathway of the barn owl to partially correlated binaural signals

1995 ◽  
Vol 74 (4) ◽  
pp. 1689-1700 ◽  
Author(s):  
Y. Albeck ◽  
M. Konishi
Keyword(s):  
Inferior Colliculus ◽  
Cross Correlation ◽  
Frequency Tuning ◽  
Neuronal Response ◽  
Interaural Time Difference ◽  
Central Nucleus ◽  
High Threshold ◽  
Correlation Model ◽  
Response Curves ◽  
The Cross

1. Extracellular single-unit recording in anesthetized barn owls was used to study neuronal response to dichotic stimuli of variable binaural correlation (BC). Recordings were made in the output fibers of nucleus laminaris (NL), the anterior division of the ventral lateral lemniscal nucleus (VLVa), the core of the central nucleus of the inferior colliculus (ICcC), the lateral shell of the central nucleus of the inferior colliculus (ICcLS), and the external nucleus of the inferior colliculus (ICx). 2. The response of all neurons sensitive to interaural time difference (ITD) varied with BC. The relationship between BC and impulse number fits a linear, a parabolic, or a ramp model. A linear or parabolic model fits most neurons in low-level nuclei. Higher order neurons in ICx did not respond to noise bursts with strong negative binaural correlation, creating a ramp-like response to BC. 3. A neuron's ability to detect ITD varied as a function of BC. Conversely, a neuron's response to BC changed with ITD. Neurons in NL, VLVa, and ICcC show almost periodic ITD response curves. In these neurons peaks and troughs of ITD response curves diminished as BC decreased, creating a flat ITD response when BC = 0. When BC was set to -1, the most favorable ITD became the least favorable one and vice versa. The ITD response curve of ICx neurons usually has a single dominant peak. The response of those neurons to a negatively correlated noise pair (BC = -1) showed two ITD peaks, flanking the position of the primary peak. 4. The parabolic BC response of NL neurons fits the prediction of the cross-correlation model, assuming half-wave rectification of the sound by the cochlea. Linear response is not predicted by the model. However, the parabolic and the linear neurons probably do not belong to two distinct groups as the difference between them is not statistically significant. Thus, the cross-correlation model provides a good description of the binaural response not only in NL but also in VLVa and ICcC. 5. Almost all ramp neurons occurred in either ICx or ICcLS where neurons are more broadly tuned to frequency than those in the lower nuclei. The synthesis of this response type requires, however, not only the convergence of different frequency channels but also inhibition between different ITD channels. We modeled the ramp response as a three-step process. First, different spectral channels converge to create broad frequency tuning. The response to variation in BC will be linear (or parabolic) because it is a sum of linear (parabolic) responses. Second, the activity in some adjacent ITD channels is subtracted by lateral inhibition. Finally, the result is rectified using a high threshold to avoid negative activity.

Local cross-correlation model of stereo correspondence

2005 ◽  
Author(s):  
Martin S. Banks ◽  
Sergei Gepshtein ◽  
Heather F. Rose
Keyword(s):  
Cross Correlation ◽  
Correlation Model ◽  
Stereo Correspondence

scholarly journals Simulation of Coherent Radar Imaging Using Continuous Wave Noise Radar

2009 ◽  
Vol 26 (9) ◽  
pp. 1956-1967
Author(s):  
Jing Xu ◽  
M. W. Hoffman ◽  
B. L. Cheong ◽  
R. D. Palmer
Keyword(s):  
Theoretical Analysis ◽  
Time Domain ◽  
Cross Correlation ◽  
Continuous Wave ◽  
Radar Imaging ◽  
Brute Force ◽  
Correlation Model ◽  
Noise Radar ◽  
Force Time ◽  
Coherent Radar

Abstract A computationally simple cross-correlation model for multiple backscattering from a continuous wave (CW) noise radar is developed and verified with theoretical analysis and brute-force time-domain simulations. Based on this cross-correlation model, a modification of an existing numerical method originally developed by Holdsworth and Reid for spaced antenna (SA) pulsed radar is used to simulate the estimated cross correlation corresponding to atmospheric backscattering using a coherent CW noise radar. Subsequently, coherent radar imaging (CRI) processing comparisons between the CW noise radar and a conventional pulsed radar are presented that verify the potential of CW noise radar for atmospheric imaging.

Modeling spatial cross-correlation of multiple ground motion intensity measures (SAs, PGA, PGV, Ia, CAV, and significant durations) based on principal component and geostatistical analyses

2020 ◽  
pp. 875529302095244
Author(s):  
Wenqi Du ◽  
Chao-Lie Ning
Keyword(s):  
Ground Motion ◽  
Principal Components ◽  
Cross Correlation ◽  
Principal Component ◽  
Peak Ground Velocity ◽  
Large Area ◽  
Intensity Measures ◽  
Spatially Correlated ◽  
Cumulative Absolute Velocity ◽  
Proposed Model

Ground motion intensity measures (IMs) were observed to be spatially correlated during past earthquakes. In this article, a new spatial cross-correlation model for a vector-IM, which consists of spectral acceleration (SA) ordinates at 17 periods and six non-SA IMs (e.g. peak ground velocity, Arias intensity, cumulative absolute velocity, and significant durations), is proposed using principal component analysis (PCA) and geostatistical analysis. A total of 3797 ground motion records are selected from the NGA-West2 database for such analyses. PCA is used to transform the spatially correlated within-event residuals into uncorrelated principal components; a permissible function is then proposed to fit the empirical semivariograms calculated by the principal components. It is evident that the proposed model performs well in capturing the spatial variability characteristics of the multiple ground motion IMs. A simple example is presented to illustrate the use of the proposed model in realizing spatially correlated ground motion residuals of multiple IMs. The model developed enables one to simulate spatially cross-correlated IMs over a large area in a rapid way.

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