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Supplemental material, sj-pdf-1-eqs-10.1177_87552930211003916 for Ground motions in urban Los Angeles from the 2019 Ridgecrest earthquake sequence by Filippos Filippitzis, Monica D Kohler, Thomas H Heaton, Robert W Graves, Robert W Clayton, Richard G Guy, Julian J Bunn and K Mani Chandy in Earthquake Spectra

In her comment (Hardebeck, 2015) on our stress heterogeneity article (Smith and Heaton, 2011), Hardebeck suggests a different focal‐mechanism error distribution than what we used in our 2011 article and suggests that this new error distribution will reduce our estimates of stress heterogeneity. In response to this, we have rerun our calculations three ways: (1) with the original mechanism error distribution from Smith and Heaton (2011), (2) with a mechanism error distribution similar to the one presented by Hardebeck (2015), and (3) with a mechanism error distribution derived from repeating earthquake statistics. We find the two new mechanism error models, relative to the original mechanism error distribution, reduce the heterogeneity ratio (HR) estimates by approximately 35%–40% (using Hardebeck’s suggested distribution) and by approximately 8%–10% (using the repeating earthquake based error distribution).

The proliferation of smartphones and other powerful sensor-equipped consumer devices enables a new class of Web application: community sense and response (CSR) systems, distinguished from standard Web applications by their use of community-owned commercial sensor hardware. Just as social networks connect and share human-generated content, CSR systems gather, share, and act on sensory data from users' Internet-enabled devices. Here, we discuss the Caltech Community Seismic Network (CSN) as a prototypical CSR system harnessing accelerometers in smartphones and consumer electronics, including the systems and algorithmic challenges of designing, building, and evaluating a scalable network for real-time awareness of dangerous earthquakes.

To provide rapid estimates of fault rupture extent during large earthquakes, we have developed the Finite Fault Rupture Detector algorithm, ‘FinDer’. FinDer uses image recognition techniques to detect automatically surface-projected fault ruptures in real-time (assuming a line source) by estimating their current centroid position, length L, and strike θ. The approach is based on a rapid high-frequency near/far-source classification of ground motion amplitudes in a dense seismic network (station spacing

We investigate the chaotic behaviour of slip pulses that propagate in a spring block slider model with velocity weakening friction by numerically solving a computationally intensive set of n coupled non-linear equations, where n is the number of blocks. We observe that the system evolves into a spatially heterogeneous pre-stress after the occurrence of a sufficient number of events. We observe that, although the spatiotemporal evolution of the amplitude of a slip pulse in a single event is surprisingly complex, the geometric description of the pulses is simple and self-similar with respect to the size of the pulse. This observation allows us to write an energy balance equation that describes the evolution of the pulse as it propagates through the known pre-stress. The equation predicts the evolution of individual ruptures and reduces the computational time dramatically. The long-time solution of the equation reveals its multiscale nature and its potential to match many of the long-time statistics of the original system, but with a much shorter computational time.

Evidence suggests that slip in earthquakes and the resultant stress changes are spatially heterogeneous. If crustal stress from past earthquakes is spatially heterogeneous, then earthquake focal mechanisms should also be spatially variable. We describe the statistical attributes of simulated earthquake catalogs, including hypocenters and focal mechanisms, for a spatially 3D, time-varying model of the crustal stress tensor with stochastic spatial variations. It is assumed that temporal variations in stress are spatially smooth and are primarily caused by plate tectonics. Spatial variations in stress are assumed to be the result of past earthquakes and are independent of time for periods between major earthquakes. It is further assumed that heterogeneous stress can be modeled as a stochastic process that is specified by an autocorrelation function. Synthetic catalogs of earthquake hypocenters and their associated focal mechanisms are produced by identifying the locations and times at which the second deviatoric stress invariant exceeds a specified limit. The model produces a seismicity catalog that is spatially biased. The only points in the grid that exceed the failure stress are those where the heterogeneous stress is approximately aligned with the stress rate. This bias results in a focal-mechanism catalog that appears less heterogeneous than the underlying stress orientations. Comparison of synthetic focal-mechanism catalogs with catalogs of real earthquakes suggests that stress in the crust is heterogeneous. Stochastic parameters are estimated which generate distance dependent spatial variations in focal mechanisms similar to those reported by Hardebeck (2006) for southern California.

There has been recent interest in using acoustic techniques to detect damage in instrumented civil structures. An automated damage detection method that analyzes recorded data has application to building types that are susceptible to a signature type of failure, where locations of potential structural damage are known a priori. In particular, this method has application to the detection of brittle fractures in welded beam-column connections in steel moment-resisting frames (MRFs). Such a method would be valuable if it could be used to detect types of damage that are otherwise difficult and costly to identify. The method makes use of a prerecorded catalog of Green's function templates and a matched filter method to detect the occurrence and location of structural damage in an instrumented building. This technique is different from existing acoustic methods because it is designed to recognize and use seismic waves radiated by the original brittle failure event where the event is not known to have occurred with certainty and the resulting damage may not be visible. The method is outlined as follows. First, identify probable locations of failure in an undamaged building. In pre-Northridge steel MRFs, which are susceptible to brittle failure of welded beam-column connections, those connections would be the locations of probable failure for this type of building. Second, obtain a Green's function template for each identified location of probable failure by applying a short-duration high-frequency pulse (e.g. using a force transducer hammer) at that location. One underlying assumption of this method is that the Green's function template specific to a potential location of failure can be used to approximate the dynamic response of the structure to structural damage at that location. Lastly, after a seismic event, systematically screen the recorded high-frequency seismograms for the presence of waveform similarities to each of the catalogued Green's function templates in order to detect structural damage. This is achieved by performing a running cross-correlation between each Green's function template and a moving window of the continuous data recorded during the earthquake. Damage that occurs at one of the catalogued potential locations is expected to result in a high cross-correlation value when using the correct Green's function template. This method, also known as the matched filter method, has seen recent success in other fields, but has yet to be explored in the context of acoustic damage detection in civil structures. Preliminary experimental results from tap tests performed on a small-scale laboratory frame are presented. Cross-correlation calculations highlight similarities among events generated at the same source location and expose differences among events generated at different source locations. Finally, a blind tap test is performed to test whether cross-correlation techniques and catalogued Green's function templates can be used to identify the occurrence of and pinpoint the location of an assumed-unknown event.

Earthquake EarlyWarning (EEW) predicts future ground shaking based on presently available data. Long ruptures present the best opportunities for EEW since many heavily shaken areas are distant from the earthquake epicentre and may receive long warning times. Predicting the shaking from large earthquakes, however, requires some estimate of the likelihood of the future evolution of an ongoing rupture. An EEW system that anticipates future rupture using the present magnitude (or rupture length) together with the Gutenberg-Richter frequencysize statistics will likely never predict a large earthquake, because of the rare occurrence of ‘extreme events’. However, it seems reasonable to assume that large slip amplitudes increase the probability for evolving into a large earthquake. To investigate the relationship between the slip and the eventual size of an ongoing rupture, we simulate suites of 1-D rupture series from stochastic models of spatially heterogeneous slip. We find that while large slip amplitudes increase the probability for the continuation of a rupture and the possible evolution into a ‘Big One’, the recognition that rupture is occurring on a spatially smooth fault has an even stronger effect.We conclude that anEEWsystem for large earthquakes needs some mechanism for the rapid recognition of the causative fault (e.g., from real-time GPS measurements) and consideration of its ‘smoothness’. An EEW system for large earthquakes on smooth faults, such as the San Andreas Fault, could be implemented in two ways: the system could issue a warning, whenever slip on the fault exceeds a few metres, because the probability for a large earthquake is high and strong shaking is expected to occur in large areas around the fault. A more sophisticated EEW system could use the present slip on the fault to estimate the future slip evolution and final rupture dimensions, and (using this information) could provide probabilistic predictions of seismic ground motions along the evolving rupture. The decision on whether an EEW system should be realized in the first or in the second way (or in a combination of both) is user-specific.