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USGS2014

Southern San Andreas Fault evaluation field activity: approaches to measuring small geomorphic offsets--challenges and recommendations for active fault studies

In southern California, where fast slip rates and sparse vegetation contribute to crisp expression of faults and microtopography, field and high‐resolution topographic data (<1 m/pixel) increasingly are used to investigate the mark left by large earthquakes on the landscape (e.g., Zielke et al., 2010; Zielke et al., 2012; Salisbury, Rockwell, et al., 2012, Madden et al., 2013). These studies measure offset streams or other geomorphic features along a stretch of a fault, analyze the offset values for concentrations or trends along strike, and infer that the common magnitudes reflect successive surface‐rupturing earthquakes along that fault section. Wallace (1968) introduced the use of such offsets, and the challenges in interpreting their “unique complex history” with offsets on the Carrizo section of the San Andreas fault; these were more fully mapped by Sieh (1978) and followed by similar field studies along other faults (e.g., Lindvall et al., 1989; McGill and Sieh, 1991). Results from such compilations spurred the development of classic fault behavior models, notably the characteristic earthquake and slip‐patch models, and thus constitute an important component of the long‐standing contrast between magnitude–frequency models (Schwartz and Coppersmith, 1984; Sieh, 1996; Hecker et al., 2013). The proliferation of offset datasets has led earthquake geologists to examine the methods and approaches for measuring these offsets, uncertainties associated with measurement of such features, and quality ranking schemes (Arrowsmith and Rockwell, 2012; Salisbury, Arrowsmith, et al., 2012; Gold et al., 2013; Madden et al., 2013). In light of this, the Southern San Andreas Fault Evaluation (SoSAFE) project at the Southern California Earthquake Center (SCEC) organized a combined field activity and workshop (the “Fieldshop”) to measure offsets, compare techniques, and explore differences in interpretation. A thorough analysis of the measurements from the field activity will be provided separately; this paper discusses the complications presented by such offset measurements using two channels from the San Andreas fault as illustrative cases. We conclude with best approaches for future data collection efforts based on input from the Fieldshop.

California↗

USGS2014

Why the 1964 Great Alaska Earthquake matters 50 years later

Spring was returning to Alaska on Friday 27 March 1964. A two‐week cold snap had just ended, and people were getting ready for the Easter weekend. At 5:36 p.m., an earthquake initiated 12 km beneath Prince William Sound, near the eastern end of what is now recognized as the Alaska‐Aleutian subduction zone. No one was expecting this earthquake that would radically alter the coastal landscape, influence the direction of science, and indelibly mark the growth of a burgeoning state.

Alaska↗

USGS2014

Refinements to the Graves and Pitarka (2010) Broadband Ground Motion Simulation Method

This brief article describes refinements to the Graves and Pitarka (2010) broadband ground motion simulation methodology (GP2010 hereafter) that have been implemented in version 14.3 of the SCEC Broadband Platform (BBP). The updated version of our method on the current SCEC BBP is referred to as GP14.3. Our simulation technique is a hybrid approach that combines low-‐frequency and high-‐frequency motions computed with different methods into a single broadband response. The separate low-‐ and high-‐frequency components have traditionally been called “deterministic” and “stochastic”, respectively; however, this nomenclature is an oversimplification. In reality, the low-‐frequency approach includes many stochastic elements, and likewise, the high-‐frequency approach includes many deterministic elements (e.g., Pulido and Kubo, 2004; Hartzell et al., 2005; Liu et al., 2006; Frankel, 2009; Graves and Pitarka, 2010; Mai et al., 2010). While the traditional terminology will likely remain in use by the broader modeling community, in this paper we will refer to these using the generic terminology “low-‐frequency” and “high-‐ frequency” approaches. Furthermore, one of the primary goals in refining our methodology is to provide a smoother and more consistent transition between the low-‐ and high-‐ frequency calculations, with the ultimate objective being the development of a single unified modeling approach that can be applied over a broad frequency band. GP2010 was validated by modeling recorded strong motions from four California earthquakes. While the method performed well overall, several issues were identified including the tendency to over-‐predict the level of longer period (2-‐5 sec) motions and the effects of rupture directivity. The refinements incorporated in GP14.3 are aimed at addressing these issues with application to the simulation of earthquakes in Western US (WUS). These refinements include the addition of a deep weak zone (details in following section) to the rupture characterization and allowing perturbations in the correlation of rise time and rupture speed with the specified slip distribution. Additionally, we have extended the parameterization of GP14.3 so that it is also applicable for simulating Eastern North America (ENA) earthquakes. This work has been guided by the comprehensive set of validation studies described in Goulet and Abrahamson (2014) and Dreger et al. (2014). The GP14.3 method shows improved performance relative to GP2010, and we direct the interested reader to Dreger et al. (2014) for a detailed assessment of the current methodology. In this paper, we concentrate on describing the modifications in more detail, and also discussing additional refinements that are currently being developed.

California↗

USGS2014

Validation of the SCEC broadband platform V14.3 simulation methods using pseudo spectral acceleration data

This paper summarizes the evaluation of ground motion simulation methods implemented on the SCEC Broadband Platform (BBP), version 14.3 (as of March 2014). A seven-member panel, the authorship of this article, was formed to evaluate those methods for the prediction of pseudo-‐spectral accelerations (PSAs) of ground motion. The panel’s mandate was to evaluate the methods using tools developed through the validation exercise (Goulet et al. ,2014), and to define validation metrics for the assessment of the methods’ performance. This paper summarizes the evaluation process and conclusions from the panel. The five broadband, finite-source simulation methods on the BBP include two deterministic approaches herein referred to as CSM (Anderson, 2014) and UCSB (Crempien and Archuleta, 2014); a band-‐limited stochastic white noise method called EXSIM (Atkinson and Assatourians, 2014); and two hybrid approaches, referred to as G&P (Graves and Pitarka, 2014) and SDSU (Olsen and Takedatsu, 2014), which utilize a deterministic Green’s function approach for periods longer than 1 second and stochastic methods for periods shorter than 1 second. Two acceptance tests were defined to validate the broadband finite‐source ground methods (Goulet et al., 2014). Part A compared observed and simulated PSAs for periods from 0.01 to 10 seconds for 12 moderate to large earthquakes located in California, Japan, and the eastern US. Part B compared the median simulated PSAs to published NGA-‐West1 (Abrahamson and Silva, 2008; Boore and Atkinson, 2008; Campbell and Bozorgnia, 2008; and Chiou and Youngs, 2008) ground motion prediction equations (GMPEs) for specific magnitude and distance cases using a pass-‐fail criteria based on a defined acceptable range around the spectral shape of the GMPEs. For the initial Part A and Part B validation exercises during the summer of 2013, the software for the five methods was locked in at version 13.6 (see Maechling et al., 2014). In the spring of 2014, additional moderate events were considered for the Part A validation, and additional magnitude and distance cases were considered for the Part B validation, for the software locked in at version 14.3. Several of the simulation procedures, specifically UCSB and SDSU, changed significantly between versions 13.6 and 14.3. The CSM code was not submitted in time for the v14.3 evaluation and its detailed performance is not addressed in this paper. As described in Goulet et al. (2014) and Maechling et al. (2014), the BBP generates a variety of products, including three-‐component acceleration time series. A series of post-‐processing codes were developed to provide individual component PSAs and average median horizontal-‐component PSA (referred to as RotD50; Boore, 2010) for oscillator periods ranging from 0.01 to 10 seconds, as well as median PSA values computed using the NGA-‐West 1 GMPEs. The BBP was also configured to provide statistical analysis of simulation results relative to recordings (Part A) and GMPEs (Part B) as described further in sections below. As part of our evaluation, we reviewed documentation provided by each of the developers, which included the technical basis behind the methods and the developer’s self-‐assessments regarding the extrapolation capabilities (in terms of magnitude and distance ranges) of their methods. Two workshops were held in which methods and results were presented, and the panel was given the opportunity to question the developers and to have detailed technical discussions. A SCEC report (Dreger et al., 2013) describes the results of this review for BBP version 13.6. This paper summarizes that work and presents results for the more recent BBP 14.3 validation.

Seismological Research Letters↗

USGS2015

“All Models Are Wrong, but Some Are Useful”

Building a new model, especially one used for policy purposes, takes considerable time, effort, and resources. In justifying such expenditures, one inevitably spends a lot of time denigrating previous models. For example, in pitching the third Uniform California Earthquake Rupture Forecast (UCERF3) ( http://www.WGCEP.org/UCERF3 ), criticisms of the previous model included fault‐segmentation assumptions and the lack of multifault ruptures. In the context of including spatiotemporal clustering for operational earthquake forecasting (e.g., Jordan et al. , 2011 ), another criticism has been that previous candidate models not only ignore elastic rebound but also produce results that are antithetical to that theory. For instance, the short‐term earthquake probabilities model ( Gerstenberger et al. , 2005 ), which provided California aftershock hazard maps at the U.S. Geological Survey web site between 2005 and 2010, implies that the time of highest likelihood for any rupture will be the moment after it occurs, even for a big one on the San Andreas fault. Furthermore, Monte Carlo simulations imply that excluding elastic rebound in such models also produces unrealistic triggering statistics ( Field, 2012 ).

Seismological Research Letters↗

USGS2014

Metrics for comparing dynamic earthquake rupture simulations

Earthquakes are complex events that involve a myriad of interactions among multiple geologic features and processes. One of the tools that is available to assist with their study is computer simulation, particularly dynamic rupture simulation. A dynamic rupture simulation is a numerical model of the physical processes that occur during an earthquake. Starting with the fault geometry, friction constitutive law, initial stress conditions, and assumptions about the condition and response of the near‐fault rocks, a dynamic earthquake rupture simulation calculates the evolution of fault slip and stress over time as part of the elastodynamic numerical solution (Ⓔ see the simulation description in the electronic supplement to this article). The complexity of the computations in a dynamic rupture simulation make it challenging to verify that the computer code is operating as intended, because there are no exact analytic solutions against which these codes’ results can be directly compared. One approach for checking if dynamic rupture computer codes are working satisfactorily is to compare each code’s results with the results of other dynamic rupture codes running the same earthquake simulation benchmark. To perform such a comparison consistently, it is necessary to have quantitative metrics. In this paper, we present a new method for quantitatively comparing the results of dynamic earthquake rupture computer simulation codes.

Seismological Research Letters↗

USGS2014

Operational earthquake forecasting can enhance earthquake preparedness

We cannot yet predict large earthquakes in the short term with much reliability and skill, but the strong clustering exhibited in seismic sequences tells us that earthquake probabilities are not constant in time; they generally rise and fall over periods of days to years in correlation with nearby seismic activity. Operational earthquake forecasting (OEF) is the dissemination of authoritative information about these time‐dependent probabilities to help communities prepare for potentially destructive earthquakes. The goal of OEF is to inform the decisions that people and organizations must continually make to mitigate seismic risk and prepare for potentially destructive earthquakes on time scales from days to decades. To fulfill this role, OEF must provide a complete description of the seismic hazard—ground‐motion exceedance probabilities as well as short‐term rupture probabilities—in concert with the long‐term forecasts of probabilistic seismic‐hazard analysis (PSHA).

Seismological Research Letters↗

USGS2015

The 21 May 2014 M w 5.9 Bay of Bengal earthquake: macroseismic data suggest a high‐stress‐drop event

A modest but noteworthy M w 5.9 earthquake occurred in the Bay of Bengal beneath the central Bengal fan at 21:51 Indian Standard Time (16:21 UTC) on 21 May 2014. Centered over 300 km from the eastern coastline of India (Fig. 1), it caused modest damage by virtue of its location and magnitude. However, shaking was very widely felt in parts of eastern India where earthquakes are uncommon. Media outlets reported as many as four fatalities. Although most deaths were blamed on heart attacks, the death of one woman was attributed by different sources to either a roof collapse or a stampede (see Table S1, available in the electronic supplement to this article). Across the state of Odisha, as many as 250 people were injured (see Table S1), most after jumping from balconies or terraces. Light damage was reported from a number of towns on coastal deltaic sediments, including collapsed walls and damage to pukka and thatched dwellings. Shaking was felt well inland into east‐central India and was perceptible in multistoried buildings as far as Chennai, Delhi, and Jaipur at distances of ≈1600  km (Table 1).

Seismological Research Letters↗

USGS2015

The Red Atrapa Sismos (Quake Catcher Network in Mexico): assessing performance during large and damaging earthquakes.

The Quake‐Catcher Network (QCN) is an expanding seismic array made possible by thousands of participants who volunteered time and resources from their computers to record seismic data using low‐cost accelerometers (http://qcn.stanford.edu/; last accessed December 2014). Sensors based on Micro‐Electromechanical Systems (MEMS) technology have rapidly improved over the last few years due to the demand of the private sector (e.g., automobiles, cell phones, and laptops). For strong‐motion applications, low‐cost MEMS accelerometers have promising features due to an increasing resolution and near‐linear phase and amplitude response ( Cochran, Lawrence, Christensen, and Jakka, 2009 ; Clayton et al. , 2011 ; Evans et al. , 2014 ). Each volunteer computer monitors ground motion and communicates using the Berkeley Open Infrastructure for Network Computing (BOINC, Anderson, 2004 ). Using a standard short‐term average, long‐term average (STLA) algorithm ( Earle and Shearer, 1994 ; Cochran, Lawrence, Christensen, Chung, 2009 ; Cochran, Lawrence, Christensen, and Jakka, 2009 ), volunteer computer and sensor systems detect abrupt changes in the acceleration recordings. Each time a possible trigger signal is declared, a small package of information containing sensor and ground‐motion information is streamed to one of the QCN servers ( Chung et al. , 2011 ). Trigger signals, correlated in space and time, are then processed by the QCN server to look for potential earthquakes.

Seismological Research Letters↗

USGS2015

Upgrade of the New China Digital Seismograph Network

No abstract available.

Seismological Research Letters↗

USGS2015

Geodetic constraints on the 2014 M 6.0 South Napa earthquake

On 24 August 2014, the M 6.0 South Napa earthquake shook much of the San Francisco Bay area, leading to significant damage in the Napa Valley. The earthquake occurred in the vicinity of the West Napa fault (122.313° W, 38.22° N, 11.3 km), a mapped structure located between the Rodger’s Creek and Green Valley faults, with nearly pure right‐lateral strike‐slip motion (strike 157°, dip 77°, rake –169°; http://comcat.cr.usgs.gov/earthquakes/eventpage/nc72282711#summary, last accessed December 2014) (Fig. 1). The West Napa fault previously experienced an M 5 strike‐slip event in 2000 but otherwise exhibited no previous definitive evidence of historic earthquake rupture ( Rodgers et al. , 2008 ; Wesling and Hanson, 2008 ). Evans et al. (2012) found slip rates of &sim;9.5  mm/yr along the West Napa fault, with most slip rate models for the Bay area placing higher slip rates and greater earthquake potential on the Rodger’s Creek and Green Valley faults, respectively (e.g., Savage et al. , 1999 ; d’Alessio et al. , 2005 ; Funning et al. , 2007 ).

Seismological Research Letters↗

USGS2015

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 ).

Seismological Research Letters↗

USGS2015

Ground motion observations of the 2014 South Napa earthquake

Ground motions of the South Napa earthquake (24 August 2014; M 6.0) were recorded at 19 stations within 20 km and 292 stations within 100 km of the rupture surface trace, generating peak ground motions in excess of 50% g and 50  cm/s in and near Napa Valley. This large dataset allows us to compare the ground motion from the earthquake to existing ground‐motion prediction equations (GMPEs) in considerable detail. Using the ground‐motion data compiled and reported by ShakeMap ( Wald et al. , 2000 ), we examine the peak ground acceleration (PGA) and peak ground velocity (PGV), as well as the pseudospectral acceleration (PSA) at periods of 0.3, 1.0, and 3.0 s. At the higher frequencies, especially PGA, data recorded at close distances (within &sim;20  km) are very consistent with the GMPEs, implying a stress drop for this event similar to the median for California, that is, 5 MPa ( Baltay and Hanks, 2014 ). At all frequencies, the attenuation with distance is stronger than the GMPEs would predict, which suggests the attenuation in the Napa and San Francisco Bay delta region is stronger than the average attenuation in California. The spatial plot of the ground‐motion residuals is positive to the north, in both Napa and Sonoma Valleys, consistent with increases in amplitude expected from both the directivity and basin effects. More interestingly, perhaps, there is strong ground motion to the south in the along‐strike direction, particularly for PSA at 1.0 s. These strongly positive residuals align with an older, Quaternary fault structure associated with the Franklin or Southampton fault, potentially indicating a fault‐zone‐guided wave.

California↗

USGS2015

The 2014 Mw6.1 South Napa Earthquake: A unilateral rupture with shallow asperity and rapid afterslip

The M w 6.1 South Napa earthquake occurred near Napa, California on August 24, 2014 (UTC), and was the largest inland earthquake in Northern California since the 1989 M w 6.9 Loma Prieta earthquake. The first report of the earthquake from the Northern California Earthquake Data Center (NCEDC) indicates a hypocentral depth of 11.0km with longitude and latitude of (122.3105°W, 38.217°N). Surface rupture was documented by field observations and Lidar imaging (Brooks et al. 2014; Hudnut et al. 2014; Brocher et al., 2015), with about 12 km of continuous rupture starting near the epicenter and extending to the northwest. The southern part of the rupture is relatively straight, but the strike changes by about 15° at the northern end over a 6-km segment. The peak dextral offset was observed near the Buhman residence with right-.‐lateral motion of 46 cm, near the location where the strike of fault begins to rotate clock-.‐wise (Hudnut et al., 2014). The earthquake was well recorded by the strong motion network operated by the NCEDC, the California Geological Survey and the U.S. Geological Survey (USGS). There are about 12 sites within an epicentral distance of 15km, with relatively good azimuthal coverage (Fig.1). The largest peak-ground-velocity (PGV) of nearly 100 cm/s was observed on station 1765, which is the closest station to the rupture and lies about 3 km east of the northern segment (Fig. 1). The ground deformation associated with the earthquake was also well recorded by the high-resolution COSMO-SkyMed satellite and Sentinel-1A satellite, providing independent static observations.

California↗

USGS2015

The Mw6.0 24 August 2014 South Napa earthquake

The M w 6.0 South Napa earthquake, which occurred at 10:20 UTC 24 August 2014 was the largest earthquake to strike the greater San Francisco Bay area since the M w 6.9 1989 Loma Prieta earthquake. The rupture from this right‐lateral earthquake propagated mostly unilaterally to the north and up‐dip, directing the strongest shaking toward the city of Napa, where peak ground accelerations (PGAs) between 45% g and 61% g were recorded and modified Mercalli intensities (MMIs) of VII–VIII were reported. Tectonic surface rupture with dextral slip of up to 46 cm was observed on a 12.5 km long segment, some of which was along a previously mapped strand of the West Napa fault system, although the rupture extended to the north of the mapped Quaternary strand. Modeling of seismic and geodetic data suggests an average coseismic slip of 50 cm, with a maximum slip of about 1 m at depths of 10–11 km. We observed up to 35 cm of afterslip along the surface trace in the week following the mainshock, primarily along the southern half of the surface rupture that experienced relatively little coseismic offset. Relocation of the sparse aftershock sequence suggests en echelon southwest‐ and northeast‐dipping fault planes, reflective of the complex fault geometry in this region. The Napa basin and historic and late Holocene alluvial flood deposits in downtown Napa amplified the ground motions there. Few ground failures were mapped, reflecting the dry season (as well as a persistent drought that had lowered the groundwater table) and the short duration of strong shaking in the epicentral area.

California↗

USGS2015

Resilience by Design: Bringing Science to Policy Makers

No one questions that Los Angeles has an earthquake problem. The “Big Bend” of the San Andreas fault in southern California complicates the plate boundary between the North American and Pacific plates, creating a convergent component to the primarily transform boundary. The Southern California Earthquake Center Community Fault Model has over 150 fault segments, each capable of generating a damaging earthquake, in an area with more than 23 million residents (Fig. 1). A Federal Emergency Management Agency (FEMA) analysis of the expected losses from all future earthquakes in the National Seismic Hazard Maps (Petersen et al., 2014) predicts an annual average of more than $3 billion per year in the eight counties of southern California, with half of those losses in Los Angeles County alone (Federal Emergency Management Agency [FEMA], 2008). According to Swiss Re, one of the world’s largest reinsurance companies, Los Angeles faces one of the greatest risks of catastrophic losses from earthquakes of any city in the world, eclipsed only by Tokyo, Jakarta, and Manila (Swiss Re, 2013).

California↗

USGS2015

Hundreds of earthquakes per day: The 2014 Guthrie, Oklahoma, Earthquake Sequence

A remarkable increase in seismic activity in Oklahoma since 2009 has been shown to correlate closely with enhanced hydrocarbon extraction and associated wastewater disposal; 99% of this recent Oklahoma earthquake activity has occurred within 15 km of a call II injection well (Ellsworth, 2013). In response to this increase in seismic activity, the U.S. Geological Survey (USGS) partnered with the Oklahoma Geological Survey (OGS) to exchange waveform data from permanent and temporary seismic stations to improve the cataloging of earthquake source parameters for a broad region of north-central Oklahoma. For a particularly persistent earthquake sequence near Guthrie, Oklahoma, a subspace detection method is applied to data from nearby seismic stations. This approach documents the occurrence of hundreds of readily detectable, highly similar, earthquakes per day, with rates occasionally exceeding 1000 earthquakes per day. Time-varying changes in b-value appear episodic, suggesting a correlation with periods of reversible fault weakening and associated failure.

Oklahoman↗

USGS2015

Self-noise models of five commercial strong-motion accelerometers

Strong‐motion accelerometers provide onscale seismic recordings during moderate‐to‐large ground motions (e.g., up to tens of m/s 2 peak). Such instruments have played a fundamental role in improving our understanding of earthquake source physics (Bock etal. , 2011), earthquake engineering (Youd et al. , 2004), and regional seismology (Zollo et al. , 2010). Although strong‐motion accelerometers tend to have higher noise levels than high‐quality broadband velocity seismometers, their higher clip‐levels provide linear recordings at near‐field sites even for the largest of events where a collocated broadband sensor would no longer be able to provide onscale recordings (Clinton and Heaton, 2002). Recently, the seismological community has begun to make use of strong‐motion accelerometer data even in the absence of large ground motions (e.g., Tibuleac et al. , 2011). The noise floor of the instruments often limits the usefulness of strong‐motion accelerometer data in such studies, because it obscures first arrivals or can make the traces dominated by noise. When a strong‐motion accelerometer is deployed in a quiet setting, the noise floors of the digitizer and the accelerometer tend to dominate the other noise sources (Cauzzi and Clinton, 2013). This situation is unlike that using broadband sensors, in which site conditions are typically the largest contributing source of noise in seismic data, especially at long periods (Wilson et al. , 2002). With the widespread deployment of strong‐motion accelerometers recorded on high resolution digitizers, it is now possible to get continuous high‐rate acceleration data in which the digitizer noise is not the dominant noise source (Cauzzi and Clinton, 2013). To better characterize the noise of a number of commonly deployed accelerometers in a standardized way, we conducted noise measurements on five different models of strong‐motion accelerometers. Our study was limited to traditional accelerometers (Fig. 1) and is in no way exhaustive.

Seismological Research Letters↗