Community Seismic Network Research Articles

Usability of Community Seismic Network recordings for ground-motion modeling

A source of ground-motion recordings in urban Los Angeles that has seen limited prior application is the Community Seismic Network (CSN), which uses low-cost, micro–electro–mechanical system (MEMS) sensors that are deployed at much higher densities than stations for other networks. We processed CSN data for the 29 earthquakes with M > 4 between July 2012 and January 2023 that produced CSN recordings, including selection of high- and low-pass corner frequencies ( fcHP and fcLP, respectively). Each record was classified as follows: (1) Broadband Record (BBR)—relatively broad usable frequency range from fcHP < 0.5 to fcLP > 10 Hz; (2) Narrowband Record (NBR)—limited usable frequency range relative to those for BBR; and (3) Rejected Record (REJ)—noise-dominated. We compare recordings from proximate (within 3 km) CSN and non-CSN stations (screened to only include cases of similar surface geology and favorable CSN instrument housing). We find similar high- to medium-frequency ground motions (i.e. peak ground acceleration (PGA) and Sa for T < 5 s) from CSN BBR and non-CSN stations, whereas NBRs have lower amplitudes. We examine PGA distributions for BBR and REJ records and find them to be distinguished, on average across the network, at 0.005 g, whereas 0.0015 g was found to be the threshold between usable records (BBR and NBR) and pre-event noise. Recordings with amplitudes near or below these thresholds are generally noise-dominated, reflecting environmental and anthropogenic ground vibrations and instrument noise. We find nominally higher noise levels in areas of high-population density and lower noise levels by a factor of about 1.5 in low-population density areas. By applying the 0.0015 g threshold, limiting distances for the network-average site condition, based on the expected fifth-percentile ground-motion levels, are 89, 210, 280, and 370 km for M 5, 6, 7, and 8 events, respectively.

Time-varying damping ratios and velocities in a high-rise during earthquakes and ambient vibrations from coda wave interferometry

Coda wave interferometry is applied to data from Community Seismic Network MEMS accelerometers permanently installed on nearly every floor of a 52-story steel moment-and-brace frame building in downtown Los Angeles. Wavefield data from the 2019 M7.1 Ridgecrest, California earthquake sequence are used to obtain impulse response functions, and time-varying damping ratios and shear-wave velocities are computed from them. The coda waves are used because of their increased sensitivity to changes in the building’s properties, and the approach is generalized to show that a building’s nonlinear response can be monitored through time-varying measurements of representative pseudo-linear systems in the time domain. The building was not damaged, but temporary nonlinear behavior observed during the strong motions provides a unique opportunity to test this method’s ability to map time-varying properties. Reference damping parameters and velocities are obtained from a month-long period during which no significant seismic activity had occurred. Damping ratios measured over narrow frequency bands increase by up to a factor of 4 over short time durations spanning the main shock, as well as M > 4.5 aftershocks and a foreshock. The largest damping ratio increases occur for the highest frequencies, and the increase is attributed to friction associated with structural and non-structural surface discontinuities which experience relative motion and impact during shaking, resulting in energy loss. Shear-wave velocities in the building’s east–west and north–south directions are found by applying a waveform stretching method to the direct and coda waves. The broadband velocities are reduced by as much as 10% during building shaking, and their restoration to pre-earthquake levels is found to be a function of shaking amplitudes. Until recently, these techniques had been limited by temporal and spatial sparsity of measurements, but in this study, variations of the impulse response functions are resolved over time scales of tens of seconds and on a floor-by-floor spatial scale.

Uncertainty quantification of ground motion time series generated at uninstrumented sites

Following an earthquake, ground motion time series are needed to carry out site-specific nonlinear response history analysis. However, the number of currently available recording instruments is sparse; thus, the ground motion time series at uninstrumented sites must be estimated. Tamhidi et al. developed a Gaussian process regression (GPR) model to generate ground motion time series given a set of recorded ground motions surrounding the target site. This GPR model interpolates the observed ground motions’ Fourier Transform coefficients to generate the target site’s Fourier spectrum and the corresponding time series. The robustness of the optimized hyperparameter of the model depends on the surrounding observation density. In this study, we carried out sensitivity analysis and tuned the hyperparameter of the GPR model for various observation densities. The 2019 M7.1 Ridgecrest and 2020 M4.5 South El Monte earthquake data sets recorded by the Community Seismic Network and California Integrated Seismic Network in Southern California are used to demonstrate the process. To provide a tool to quantify the uncertainty of the generated motions, a methodology to develop realizations of ground motion time series is also incorporated. The results illustrate that the uncertainty of the generated motions is lower at longer periods. It is shown that the observation density in the proximity of the target site plays a vital role in both error and uncertainty reduction of the generated time series. To demonstrate the concept, the effect of additional observations from combined recording networks is investigated.

Empirical Map-Based Nonergodic Models of Site Response in the Greater Los Angeles Area

ABSTRACTWe develop empirical estimates of site response at seismic stations in the Los Angeles area using recorded ground motions from 414 M 3–7.3 earthquakes in southern California. The data are from a combination of the Next Generation Attenuation-West2 project, the 2019 Ridgecrest earthquakes, and about 10,000 newly processed records. We estimate site response using an iterative mixed-effects residuals partitioning approach, accounting for azimuthal variations in anelastic attenuation and potential bias due to spatial clusters of colocated earthquakes. This process yields site response for peak ground acceleration, peak ground velocity, and pseudospectral acceleration relative to a 760 m/s shear-wave velocity (VS) reference condition. We employ regression kriging to generate a spatially continuous site response model, using the linear site and basin terms from Boore et al. (2014) as the background model, which depend on VS30 and depth to the 1 km/s VS isosurface. This is different from past approaches to nonergodic models, in which spatially varying coefficients are regressed. We validate the model using stations in the Community Seismic Network (CSN) that are in the middle of our model spatial domain but were not considered in model development, finding strong agreement between the interpolated model and CSN data for long periods. Our model could be implemented in regional seismic hazard analyses, which would lead to improvements especially at long return periods. Our site response model also has potential to improve both ground-motion accuracy and warning times for the U.S. Geological Survey ShakeAlert earthquake early warning (EEW) system. For a point-source EEW simulation of the 1994 M 6.7 Northridge earthquake, our model produces ground motions more consistent with the ground-truth ShakeMap and would alert areas with high population density such as downtown Los Angeles at lower estimated magnitudes (i.e., sooner) than an ergodic model for a modified Mercalli intensity 4.5 alerting threshold.

Parsimonious Velocity Inversion Applied to the Los Angeles Basin, CA

AbstractThe proliferation of dense arrays promises to improve our ability to image geological structures at the scales necessary for accurate assessment of seismic hazard. However, combining the resulting local high‐resolution tomography with existing regional models presents an ongoing challenge. We developed a framework based on the level‐set method that infers where local data provide meaningful constraints beyond those found in regional models ‐ for example the Community Velocity Models (CVMs) of southern California. This technique defines a volume within which updates are made to a reference CVM, with the boundary of the volume being part of the inversion rather than explicitly defined. By penalizing the complexity of the boundary, a minimal update that sufficiently explains the data is achieved. To test this framework, we use data from the Community Seismic Network, a dense permanent urban deployment. We inverted Love wave dispersion and amplification data, from the Mw 6.4 and 7.1 2019 Ridgecrest earthquakes. We invert for an update to CVM‐S4.26 using the Tikhonov Ensemble Sampling scheme, a highly efficient derivative‐free approximate Bayesian method. We find the data are best explained by a deepening of the Los Angeles Basin with its deepest part south of downtown Los Angeles, along with a steeper northeastern basin wall. This result offers new progress toward the parsimonious incorporation of detailed local basin models within regional reference models utilizing an objective framework and highlights the importance of accurate basin models when accounting for the amplification of surface waves in the high‐rise building response band.

Conditioned Simulation of Ground-Motion Time Series at Uninstrumented Sites Using Gaussian Process Regression

ABSTRACTGround-motion time series are essential input data in seismic analysis and performance assessment of the built environment. Because instruments to record free-field ground motions are generally sparse, methods are needed to estimate motions at locations with no available ground-motion recording instrumentation. In this study, given a set of observed motions, ground-motion time series at target sites are constructed using a Gaussian process regression (GPR) approach, which treats the real and imaginary parts of the Fourier spectrum as random Gaussian variables. Model training, verification, and applicability studies are carried out using the physics-based simulated ground motions of the 1906 Mw 7.9 San Francisco earthquake and Mw 7.0 Hayward fault scenario earthquake in northern California. The method’s performance is further evaluated using the 2019 Mw 7.1 Ridgecrest earthquake ground motions recorded by the Community Seismic Network stations located in southern California. These evaluations indicate that the trained GPR model is able to adequately estimate the ground-motion time series for frequency ranges that are pertinent for most earthquake engineering applications. The trained GPR model exhibits proper performance in predicting the long-period content of the ground motions as well as directivity pulses.

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

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.

2019 Ridgecrest Earthquake Reveals Areas of Los Angeles That Amplify Shaking of High-Rises

AbstractThe populace of Los Angeles, California, was startled by shaking from the M 7.1 earthquake that struck the city of Ridgecrest located 200 km to the north on 6 July 2019. Although the earthquake did not cause damage in Los Angeles, the experience in high-rise buildings was frightening in contrast to the shaking felt in short buildings. Observations from 560 ground-level accelerometers reveal large variations in shaking in the Los Angeles basin that occurred for more than 2 min. The observations come from the spatially dense Community Seismic Network (CSN), combined with the sparser Southern California Seismic Network and California Strong Motion Instrumentation Program networks. Site amplification factors for periods of 1, 3, 6, and 8 s are computed as the ratio of each station’s response spectral values combined for the two horizontal directions, relative to the average of three bedrock sites. Spatially coherent behavior in site amplification emerges for periods ≥3 s, and the maximum calculated site amplifications are the largest, by factors of 7, 10, and 8, respectively, for 3, 6, and 8 s periods. The dense CSN observations show that the long-period amplification is clearly, but only partially, correlated with the depth to basement. Sites with the largest amplifications for the long periods (≥3 s) are not close to the deepest portion of the basin. At 6 and 8 s periods, the maximum amplifications occur in the western part of the Los Angeles basin and in the south-central San Fernando Valley sedimentary basin. The observations suggest that the excitation of a hypothetical high-rise located in an area characterized by the largest site amplifications could be four times larger than in a downtown Los Angeles location.

Detection of Building Damage Using Helmholtz Tomography

High‐rise buildings with dense permanent installations of continuously recording accelerometers offer a unique opportunity to observe temporal and spatial variations in the propagation properties of seismic waves. When precise, floor‐by‐floor measurements of frequency‐dependent travel times can be made, accurate models of material properties (e.g., stiffness or rigidity) can be determined using seismic tomographic imaging techniques. By measuring changes in the material properties, damage to the structure can be detected and localized after shaking events such as earthquakes. Here, seismic Helmholtz tomography is applied to simulated waveform data from a high‐rise building, and its feasibility is demonstrated. A 52‐story dual system building—braced‐frame core surrounded by an outrigger steel moment frame—in downtown Los Angeles is used for the computational basis. It is part of the Community Seismic Network and has a three‐component accelerometer installed on every floor. A finite‐element model of the building based on structural drawings is used for the computation of synthetic seismograms for 60 damage scenarios in which the stiffness of the building is perturbed in different locations across both adjacent and distributed floors and to varying degrees. The dynamic analysis loading function is a Gaussian pulse applied to the lowest level fixed boundary condition, producing a broadband response on all floors. After narrowband filtering the synthetic seismograms and measuring the maximum amplitude, the frequency‐dependent travel times and differential travel times are computed. The travel‐time and amplitude measurements are converted to shear‐wave velocity at each floor via the Helmholtz wave equation whose solutions can be used to track perturbations to wavefronts through densely sampled wavefields. These results provide validation of the method’s application to recorded data from real buildings to detect and locate structural damage using earthquake, explosion, or ambient seismic noise data in near‐real time.

Downtown Los Angeles 52-Story High-Rise and Free-Field Response to an Oil Refinery Explosion

The ExxonMobil Corp. oil refinery in Torrance, California, experienced an explosion on 18 February 2015, causing ground shaking equivalent to a magnitude 2.0 earthquake. The impulse response for the source was computed from Southern California Seismic Network data for a single force system with a value of 2 × 105 kN vertically downward. The refinery explosion produced an air pressure wave that was recorded 22.8 km away in a 52-story high-rise building in downtown Los Angeles by a dense accelerometer array that is a component of the Community Seismic Network. The array recorded anomalous waveforms on each floor displaying coherent arrivals that are consistent with the building's elastic response to a pressure wave caused by the refinery explosion. Using a finite-element model of the building, the force on the building on a floor-by-floor scale was found to range up to 1.42 kN, corresponding to a pressure perturbation of 7.7 Pa.

Community Seismic Network: A Dense Array to Sense Earthquake Strong Motion

Research Article| August 05, 2015 Community Seismic Network: A Dense Array to Sense Earthquake Strong Motion Robert W. Clayton; Robert W. Clayton aSeismological Laboratory, Caltech, MC 252‐21, Pasadena, California 91125 U.S.A.clay@gps.caltech.edu Search for other works by this author on: GSW Google Scholar Thomas Heaton; Thomas Heaton bCivil and Mechanical Engineering, Caltech, MC 104‐44, Pasadena, California 91125 U.S.A. Search for other works by this author on: GSW Google Scholar Monica Kohler; Monica Kohler bCivil and Mechanical Engineering, Caltech, MC 104‐44, Pasadena, California 91125 U.S.A. Search for other works by this author on: GSW Google Scholar Mani Chandy; Mani Chandy cComputer Science, Caltech, MC 305‐16, Pasadena, California 91125 U.S.A. Search for other works by this author on: GSW Google Scholar Richard Guy; Richard Guy aSeismological Laboratory, Caltech, MC 252‐21, Pasadena, California 91125 U.S.A.clay@gps.caltech.edu Search for other works by this author on: GSW Google Scholar Julian Bunn Julian Bunn dCenter for Data‐Driven Discovery, Caltech, MC 158‐79, Pasadena, California 91125 U.S.A. Search for other works by this author on: GSW Google Scholar Author and Article Information Robert W. Clayton aSeismological Laboratory, Caltech, MC 252‐21, Pasadena, California 91125 U.S.A.clay@gps.caltech.edu Thomas Heaton bCivil and Mechanical Engineering, Caltech, MC 104‐44, Pasadena, California 91125 U.S.A. Monica Kohler bCivil and Mechanical Engineering, Caltech, MC 104‐44, Pasadena, California 91125 U.S.A. Mani Chandy cComputer Science, Caltech, MC 305‐16, Pasadena, California 91125 U.S.A. Richard Guy aSeismological Laboratory, Caltech, MC 252‐21, Pasadena, California 91125 U.S.A.clay@gps.caltech.edu Julian Bunn dCenter for Data‐Driven Discovery, Caltech, MC 158‐79, Pasadena, California 91125 U.S.A. Publisher: Seismological Society of America First Online: 14 Jul 2017 Online ISSN: 1938-2057 Print ISSN: 0895-0695 © 2015 by the Seismological Society of America Seismological Research Letters (2015) 86 (5): 1354–1363. https://doi.org/10.1785/0220150094 Article history First Online: 14 Jul 2017 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Search Site Citation Robert W. Clayton, Thomas Heaton, Monica Kohler, Mani Chandy, Richard Guy, Julian Bunn; Community Seismic Network: A Dense Array to Sense Earthquake Strong Motion. Seismological Research Letters 2015;; 86 (5): 1354–1363. doi: https://doi.org/10.1785/0220150094 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietySeismological Research Letters Search Advanced Search The Community Seismic Network (CSN) is currently a 500‐element strong‐motion network located in the Los Angeles area of California (see Fig. 1). The sensors in the network are low‐cost microelectromechanical (MEM) accelerometers that are capable of recording on scale up to accelerations of ±2g. The primary product of the network is a set of measurements of ground shaking in the seconds following a major earthquake. An example of this is shown in Figure 2. The shaking information will be contributed to U.S. Geological Survey products such as ShakeMap (Wald et al., 1999) and... You do not have access to this content, please speak to your institutional administrator if you feel you should have access.

On‐Site Early Warning and Rapid Damage Forecasting Using Single Stations: Outcomes from the REAKT Project

Earthquake early warning systems (EEWS) in their various forms are recognized as potentially valuable tools for mitigating against the risk associated with earthquakes (see Allen, 2013; Wenzel and Zschau, 2014). For example, operational systems currently exist in Japan (Horiuchi et al. , 2005; Kamigaichi et al. , 2009), Taiwan (Wu and Zhao, 2006; Wu, Chen, et al. , 2013), and Mexico (Espinosa‐Aranda et al. , 2009), while there are others nearing operational status, under development, or are being strongly considered in regions such as Italy (Satriano et al. , 2011), Turkey (Alcik et al. , 2009; Wenzel et al. , 2014), California (Bose et al. , 2009), Romania (Bose et al. , 2007), Israel (Allen et al. , 2012), and Spain (Carranza et al. , 2013). However, EEWS are not only valuable for the mainshock, but also for the period after a disastrous earthquake when there is the possibility of reducing losses due to aftershocks if early warning/rapid response systems can be rapidly deployed in the field. In such cases, the deployed instruments could serve a number of functions, such as 1. implementing a threshold‐based on‐site early warning system (OSEWS) for infrastructure; 2. monitoring the structural response, and its changes, of buildings and infrastructure in real time during the aftershock sequence; 3. allowing the assessment of the expected damage to nearby structures soon after an aftershock’s occurrence; 4. allowing the overall expected damage to a target structure during the aftershock sequence to be estimated; and 5. validating damage forecasts determined by probabilistic approaches (or others) and updating the fragility curves based on recorded ground motion. A recent important development in this direction is the Community Seismic Network approach and the Quake‐Catcher Network in California, U.S.A., based on the installation by community volunteers of low‐cost accelerometers in houses and buildings (Clayton et al. , 2011; Kohler et al. , 2013). Within the …

On the Reliability of Quake-Catcher Network Earthquake Detections

Over the past two decades, there have been several initiatives to create volunteer‐based seismic networks. The Personal Seismic Network, proposed around 1990, used a short‐period seismograph to record earthquake waveforms using existing phone lines (Cranswick and Banfill, 1990; Cranswick et al. , 1993). NetQuakes (Luetgert et al. , 2010) deploys triaxial Micro‐Electromechanical Systems (MEMS) sensors in private homes, businesses, and public buildings where there is an Internet connection. Other seismic networks using a dense array of low‐cost MEMS sensors are the Community Seismic Network (Clayton et al. , 2012; Kohler et al. , 2013) and the Home Seismometer Network (Horiuchi et al. , 2009). One main advantage of combining low‐cost MEMS sensors and existing Internet connection in public and private buildings over the traditional networks is the reduction in installation and maintenance costs (Koide et al. , 2006). In doing so, it is possible to create a dense seismic network for a fraction of the cost of traditional seismic networks (D’Alessandro and D’Anna, 2013; D’Alessandro, 2014; D’Alessandro et al. , 2014). A rapidly deployable and highly mobile seismic network can collect enormous volumes of data at high spatial density during an aftershock sequence following major earthquakes (Naito et al. , 2013). Although the low‐cost seismic networks described above were primarily designed to detect and characterize earthquakes, the networks have also been used for other purposes such as to monitor building health in Kohler et al. (2013). These types of low‐cost networks may also have other potential applications such as detecting landslides (Azzam et al. , 2011) and locating explosions (Taylor et al. , 2011). The Quake‐Catcher Network (QCN) is another variant of a cyber‐social seismic network, which has been operating since 2008. Cochran, Lawrence, Christensen, and Chung (2009) and Cochran, Lawrence, Christensen, and Jakka (2009) describe the implementation that uses a client software phase‐picking …

Performance of Several Low-Cost Accelerometers

Several groups are implementing low-cost host-operated systems of strong-motion accelerographs to support the somewhat divergent needs of seismologists and earthquake engineers. The Advanced National Seismic System Technical Implementation Committee (ANSS TIC, 2002), managed by the U.S. Geological Survey (USGS) in cooperation with other network operators, is exploring the efficacy of such systems if used in ANSS networks. To this end, ANSS convened a working group to explore available Class C strong-motion accelerometers (defined later), and to consider operational and quality control issues, and the means of annotating, storing, and using such data in ANSS networks. The working group members are largely coincident with our author list, and this report informs instrument-performance matters in the working group’s report to ANSS. Present examples of operational networks of such devices are the Community Seismic Network (CSN; csn.caltech.edu), operated by the California Institute of Technology, and Quake-Catcher Network (QCN; Cochran et al., 2009; qcn.stanford.edu; November 2013), jointly operated by Stanford University and the USGS. Several similar efforts are in development at other institutions. The overarching goals of such efforts are to add spatial density to existing Class-A and Class-B (see next paragraph) networks at low cost, and to include many additional people so they become invested in the issues of earthquakes, their measurement, and the damage they cause.

Community Seismic Network

The article describes the design of the Community Seismic Network, which is a dense open seismic network based on low cost sensors. The inputs are from sensors hosted by volunteers from the community by direct connection to their personal computers, or through sensors built into mobile devices. The server is cloud-based for robustness and to dynamically handle the load of impulsive earthquake events. The main product of the network is a map of peak acceleration, delivered within seconds of the ground shaking. The lateral variations in the level of shaking will be valuable to first responders, and the waveform information from a dense network will allow detailed mapping of the rupture process. Sensors in buildings may be useful for monitoring the state-of-health of the structure after major shaking.