Abstract: Many recent structural imaging studies involve deployment of dense linear seismic arrays (e.g., HiClimb, CIFALPS) across a target region. These dense arrays can also record many local and regional seismic events that could be used to illuminate fault structures and tectonic processes. Recently Li et al. (2018) developed a local similarity method to detect weak seismic events recorded by Large-N arrays. It computes cross-correlation (CC) for each station with its nearest neighbors and then directly stacks the CC traces into an array-averaged one. Such direct stacking works well for small-aperture arrays (up to ten kilometers) but is not suitable for wide-aperture ones (tens and hundreds of kilometers) because of the time delays at different stations. In this study, we update the local similarity method with time-shift stacking using predicted travel times from a local 1-D velocity model. We use a grid-search strategy to detect local events with the highest stacked CC value given a set of hypothetical source locations, similar to the source-scanning algorithm (Kao and Shan, 2004) or the match-and-locate algorithm (Zhang and Wen, 2015). This results in more robust detections than simple stacking and meanwhile provides the estimate of event locations. We apply this updated method to one-year recordings of the China-Italy-France Alps (CIFALPS) dense linear array (~10 km spacing and ~400 km long) deployed along the Western Alps in 2012-2013. We find many new events that are not listed in the existing regional earthquake catalogs. Our results suggest that this method can provide high-resolution seismic event detection and location for both regular linear arrays and Large-N arrays.
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Abstract: The inclusion of crowd sourcing based earthquake detection and characterization has proven to be very successful as a first alert in disaster scenarios and to increase coverage for earthquake detection where there are sparse sensor networks. A variety of crowd sourcing applications such as the USGS Twitter Earthquake Detector (TED), University of California Berkeley’s MyShake, RaspberryShake, and RedVox Inc. Infracorder, are in use to name a few. Commercially available smartphones offer a lower deployment cost coupled with high data sampling rates, with drawbacks such as timing, durability, and sensitivity issues. We investigate the functionality of incorporating non-traditional information/data sources such as the use of smartphone and social media applications as a potential method to corroborate geophysical detections from traditional sensor network data. We compare data collected with smartphones collocated with traditional sensors in various environments. The use of non-traditional collection methods could potentially assist in identifying unknown sources and may enhance the scientific community’s ground truth data collections.
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Abstract: The objective of the Fault to Seismic Hazard Assessment (Fault2SHA) Working Group is to build a community of active fault-related researchers to exchange data, tools and ideas on how to best model faults in seismic hazard assessment in specific tectonic contexts. After a few meetings (Paris 2014, Chieti 2015) and thematic sessions at international conferences in 2016 (https://sites.google.com/site/linkingfaultpsha/home) the WG was officially established inside the European Seismological Commission (ESC) in 2016. Being a not-funded entity the WG acts on voluntary basis. The community involved is made of data providers, data modellers and data users willing to share data and methodological approaches. The WG milestones achieved since 2016 are: a paper on aftershock probabilistic seismic hazard based on fault data gathered by many European teams in the wake of the Amatrice, 2016 M6.0 earthquake (Peruzza et al., 2016); the organisation of an international workshop in Barcelonette in 2017, France, that gathered 50 participants from around the world; the publication of 10 papers in a special issue of the NHESS journal; the organisation of a training course in Paris in 2017, where geologists learned how to use some Fault2SHA tools. The WG has initiated other collaborative initiatives such as the establishment of natural laboratories in Italy and Spain. Preliminary results will be presented at SSA. In these laboratories we want to address specific issues and questions such as: Methods to define sections/ruptures; Physics-based approaches; Needs for the collection of data (volcanic area?) to update scaling laws; How to constrain slip on faults using geodesy? How to propagate uncertainty in fault-PSHA? The Fault2SHA session to be held during the 2018 SSA conference in Miami is an additional opportunity to widen the discussion beyond the European context and to open to new potential members the opportunity to join us at the next ESC meeting that will be held in 2018 in Malta.
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Abstract: Double Benioff Zones (DBZs) have been previously observed in the Hikurangi trench, New Zealand. The correlation between seismicity and velocity heterogeneities can help understanding of their occurrence. Therefore, seismic tomography is commonly applied to identify the position and extent of the down-going slabs at depth in addition to crustal and upper mantle velocity heterogeneities. Previous studies developed three-dimensional seismic velocity models based on local and regional data. The recent availability of multiscale seismic tomography and waveform cross-correlation can boost the structural and seismicity resolution especially in areas with sparse data. Here, we investigate the characteristics of the DBZs along the strike of Hikurangi. We obtain local and regional waveform data from Geonet and teleseismic arrival times reported by the International Seismological Center for events within magnitude range of 2-5 between January 2012 and July 2017. We apply the teletomoDD algorithm based on absolute and differential times in different scales. This algorithm uses nested regional-global model parameterization that builds a coarser global model encompassing a finely gridded regional one. A differential-time relocation method based on waveform cross-correlation data improves the relative locations with ~500 m uncertainties in both horizontal and vertical. Our preliminary relocation results show a clear DBZ in the north and south of the trench, but the DBZ disappears in the central North Island. We also observe a decrease in DBZ layer separation and bending of the slab from the north to the south of the trench. Seismicity in the upper and lower planes if correlated with velocity anomalies provides insights about the causes of the within-slab-deformation (e.g. slab dehydration). Tracking such correlation along the trench may explain the presence/absence of the DBZs in some cross-sections and thereafter the mechanism responsible for intermediate-depth earthquakes.
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Abstract: Today machine learning is being applied to solve various problems in seismology using large available datasets. In many cases these data have already been analyzed by humans and serve as training or testing datasets. However, for some important problems in seismology we are severely data-limited. In particular, the enormous numbers of free parameters in deep learning algorithms require amounts of data that are often far larger than what is available today. This data limitation is especially severe for very large events, which are rare but very important cases. Traditional data-augmentation methods include interpolation and adding random noise to existing data. Here we suggest a way to generate realistic synthetic seismic waveforms using generative adversarial networks (GANs). We train a GAN to capture the major features of a large data set of real seismic waveforms, and to synthesize new waveforms. We show that these synthetic waveforms are realistic enough to fool professionals and contain realistic physical features, e.g. major body wave packets, coda wave decay and frequency dispersion. Compared to simulated synthetic waveforms, they contain some realistic high-frequency features that are not easily modeled by more traditional waveform simulation methods. Beyond augmenting the test data, the GANs can have potentially wide applications in seismology, such as seismic event/phase discrimination, clipped waveform completion, and waveform up-sampling. Updated results will be presented in the meeting.
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Abstract: Four co-located 3-component (3-C) Eentec R-1 rotational velocity sensors and Episensor FBA ES-T translational accelerometers were deployed at the Nevada National Security Site to record three Source Physics Experiment (SPE) chemical explosions with yields of 90kg (SPE1), 997kg (SPE2), and 905kg (SPE3) equivalent TNT. The 4 co-located sensors were deployed 1km from ground zero within a granite outcrop. Three earthquakes were also recorded by this seismic array, a Ml 3.3 at 28km, a Ml 2.6 at 58km, and Ml 3.5 at 123km distance from SPE. Igel et al. (2005) demonstrated using long period teleseismic surface waves that the vertical rotational velocity (Ωz) is in phase and scales in amplitude with the transverse (SH) translational acceleration (üT) by the horizontal phase velocity c (üT / Ωz = -2c). We expect this also holds true for higher frequency body-waves at local distances and the radial and transverse rotational velocities should scale with the vertical and radial accelerations (P-SV) by the phase velocity, e.g., üZ / ΩR ~ c (Li and Baan, 2017). Using all 3-C of the rotational and translational motions, we measured the horizontal phase velocity of 450 m/s and 1125 m/s for 2 separate directions. In contrast, the horizontal velocity measured for the Ml 3.3 earthquake is 6 km/s in the 0.1 to 10 Hz band. While the earthquakes showed high coherency between 3-C rotational and translational motions, the explosions exhibited more coherency with P-SV wave but less coherency for SH-wave radiation. This may be due to explosion SH-waves originating from scattering rather than the source. This difference could be exploited as a discriminant between explosions and earthquakes. We explore such a discriminant using array-derived rotational motions from historical nuclear explosions recorded at regional distance. The ratio of peak-acceleration to peak-rotational-rate shows promise as a discriminant statistic. Prepared by LLNL under Contract DE-AC52-07NA27344.
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