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Earthquake early warning systems use synthetic data from simulation frameworks like MudPy to train models for predicting the magnitudes of large earthquakes. MudPy, although powerful, has limitations: a lengthy simulation time to generate the required data, lack of user-friendliness, and no platform for discovering and sharing its data. We introduce FakeQuakes DAGMan Workflow (FDW), which utilizes Open Science Grid (OSG) for parallel computations to accelerate and streamline MudPy simulations. FDW significantly reduces runtime and increases throughput compared to a single-machine setup. Using FDW, we also explore partitioned parallel HTCondor DAGMan workflows to enhance OSG efficiency. Additionally, we investigate leveraging cyberinfrastructure, such as Virtual Data Collaboratory (VDC), for enhancing MudPy and OSG. Specifically, we simulate using Cloud bursting policies to enforce FDW job-offloading to VDC during OSG peak demand, addressing shared resource issues and user goals; we also discuss VDC’s value in facilitating a platform for broad access to MudPy products. 

Data-driven approaches to identify geophysical signals have proven beneficial in high dimensional environments where model-driven methods fall short. GNSS offers a source of unsaturated ground motion observations that are the data currency of ground motion forecasting and rapid seismic hazard assessment and alerting. However, these GNSS-sourced signals are superposed onto hardware-, location- and time-dependent noise signatures influenced by the Earth’s atmosphere, low-cost or spaceborne oscillators, and complex radio frequency environments. Eschewing heuristic or physics based models for a data-driven approach in this context is a step forward in autonomous signal discrimination. However, the performance of a data-driven approach depends upon substantial representative samples with accurate classifications, and more complex algorithm architectures for deeper scientific insights compound this need. The existing catalogs of high-rate (≥1Hz) GNSS ground motions are relatively limited. In this work, we model and evaluate the probabilistic noise of GNSS velocity measurements over a hemispheric network. We generate stochastic noise time series to augment transferred low-noise strong motion signals from within 70 kilometers of strong events (≥ MW 5.0) from an existing inertial catalog. We leverage known signal and noise information to assess feature extraction strategies and quantify augmentation benefits. We find a classifier model trained on this expanded pseudo-synthetic catalog improves generalization compared to a model trained solely on a real-GNSS velocity catalog, and offers a framework for future enhanced data driven approaches.

 

Stochastic slip rupture modeling is a computationally efficient, reduced‐physics approximation that has the capability to create large numbers of unique ruptures based only on a few statistical assumptions. Yet one fundamental question pertaining to this approach is whether the slip distributions calculated in this way are “realistic.” Rather, can stochastic modeling reproduce slip distributions that match what is seen inM9+ events recorded in instrumental time? We focus here on testing the ability of the von Karman ACF method for stochastic slip modeling to reproduceM9+ events. We start with the 2011M9.1 Tohoku‐Oki earthquake and tsunami where we test both a stochastic method with a homogeneous background mean model and a method where slip is informed by an additional interseismic coupling constraint. We test two coupling constraints with varying assumptions of either trench‐locking or ‐creeping and assess their influence on the calculated ruptures. We quantify the dissimilarity between the 12,000 modeled ruptures and a slip inversion for the Tohoku earthquake. We also model tsunami inundation for over 300 ruptures and compare the results to an inundation survey along the eastern coastline of Japan. We conclude that stochastic slip modeling produces ruptures that can be considered “Tohoku‐like,” and inclusion of coupling can both positively and negatively influence the ability to create realistic ruptures. We then expand our study to show that for the 1960M9.4–9.6 Chile, 1964M9.2 Alaska, and 2004M9.1–9.3 Sumatra events, stochastic slip modeling has the capability to produce ruptures that compare favorably to those events.

 

Earthquake early warning (EEW) systems aim to forecast the shaking intensity rapidly after an earthquake occurs and send warnings to affected areas before the onset of strong shaking. The system relies on rapid and accurate estimation of earthquake source parameters. However, it is known that source estimation for large ruptures in real‐time is challenging, and it often leads to magnitude underestimation. In a previous study, we showed that machine learning, HR‐GNSS, and realistic rupture synthetics can be used to reliably predict earthquake magnitude. This model, called Machine‐Learning Assessed Rapid Geodetic Earthquake model (M‐LARGE), can rapidly forecast large earthquake magnitudes with an accuracy of 99%. Here, we expand M‐LARGE to predict centroid location and fault size, enabling the construction of the fault rupture extent for forecasting shaking intensity using existing ground motion models. We test our model in the Chilean Subduction Zone with thousands of simulated and five real large earthquakes. The result achieves an average warning time of 40.5 s for shaking intensity MMI4+, surpassing the 34 s obtained by a similar GNSS EEW model. Our approach addresses a critical gap in existing EEW systems for large earthquakes by demonstrating real‐time fault tracking feasibility without saturation issues. This capability leads to timely and accurate ground motion forecasts and can support other methods, enhancing the overall effectiveness of EEW systems. Additionally, the ability to predict source parameters for real Chilean earthquakes implies that synthetic data, governed by our understanding of earthquake scaling, is consistent with the actual rupture processes.

 

Coastal subsidence, dating of plant remains and tree rings, and evidence for tsunami inundation point to coseismic activity on a sizable portion of the Cascadia subduction zone around three centuries ago. A tsunami of remote origin in 1700 C.E., probably from Cascadia, caused flooding and damage in Japan. In previous modeling, this transpacific evidence was found most simply explained by one Cascadia rupture about 1,000 km long. Here I model tens of thousands of ruptures and simulate their subsidence and tsunami signals and show that it is possible that the earthquake was part of a sequence of several events. Partial rupture of ∼400 km offshore southern Oregon and northern California in one large M ≥ 8.7 earthquake can explain the tsunami in Japan without conflicting with the subsidence. As many as four more earthquakes with M ≤ 8.7 can complete the subsidence signal without their tsunamis being large enough to be recorded in Japan. The purpose of this study is not to find a single, most likely, scenario or disprove the single‐rupture hypothesis favored by alternative evidence such as turbidites. Rather, it demonstrates that a multiple rupture sequence may explain part of the available data, and therefore cannot be discounted. Given the gaps in the presently available estimates of subsidence it is also possible that segments of the megathrust, for example from Copalis to the Strait of Juan de Fuca, did not rupture in 1700. The findings have significant implications for Cascadia geodynamics and how earthquake and tsunami hazards in the region are quantified.

 

ABSTRACT We present an approach for generating stochastic scenario rupture models and semistochastic broadband seismic waveforms that include validated P waves, an important feature for application to early warning systems testing. There are few observations of large magnitude earthquakes available for development and refinement of early warning procedures; thus, simulated data are a valuable supplement. We demonstrate the advantage of using the Karhunen–Loève expansion method for generating stochastic scenario rupture models, as it allows the user to build in desired spatial qualities, such as a slip inversion, as a mean background slip model. For waveform computation, we employ a deterministic approach at low frequencies (<1  Hz) and a semistochastic approach at high frequencies (>1  Hz). Our approach follows Graves and Pitarka (2010) and extends to model P waves. We present the first validation of semistochastic broadband P waves, comparing our waveforms against observations of the 2014 Mw 8.1 Iquique, Chile, earthquake in the time domain and across frequencies of interest. We then consider the P waves in greater detail, using a set of synthetic waveforms generated for scenario ruptures in the Cascadia subduction zone. We confirm that the time-dependent synthetic P-wave amplitude growth is consistent with previous analyses and demonstrate how the data could be used to simulate earthquake early warning procedures. 

At subduction zones, the down‐dip limit of slip represents how deep an earthquake can rupture. For hazards it is important ‐ it controls the intensity of shaking and the pattern of coseismic uplift and subsidence. In the Cascadia Subduction Zone, because no large magnitude events have been observed in instrumental times, the limit is inferred from geological estimates of coastal subsidence during previous earthquakes; it is typically assumed to coincide approximately with the coastline. This is at odds with geodetic coupling models as it leaves residual slip deficits unaccommodated on a large swath of the megathrust. Here we will show that ruptures can penetrate deeper into the megathrust and still produce coastal subsidence provided slip decreases with depth. We will discuss the impacts of this on expected shaking intensities.

 

Within the fore‐arc of the Cascadia Subduction Zone, there are significant along‐strike differences in the orientation of splay faults, sediment consolidation, and fault roughness. Here, we use dynamic rupture simulations of megathrust earthquakes on different realizations of a fault system that incorporate fore‐arc properties representative of offshore Oregon and Washington to estimate how splay faults may behave in future megathrust earthquakes in Cascadia. While splay faults were activated in all of our simulations, splay orientation is a primary control on slip amplitude. Seaward vergent faults accommodate significant amounts of slip resulting in large seafloor uplift and significantly larger tsunami amplitudes. For example, our median tsunami heights including splay faults are about a factor of two larger than those that did not include splay fault deformation. We suggest that there is an urgent need to revisit existing approaches to tsunami hazard assessment in Cascadia to include the influence of splay faults.

 

Abstract The La Crucecita earthquake ruptured on the megathrust, generating strong shaking and a modest but long-lived tsunami. This is a significant earthquake that illuminates important aspects of the behavior of the megathrust as well as the potential related hazards. The rupture is contained within 15–30 km depth, ground motions are elevated, and the energy to moment ratio is high. We argue that it represents a deep megathrust earthquake, the 30 km depth is the down-dip edge of slip. The inversion is well constrained, ruling out any shallow slip. It is the narrow seismogenic width and the configuration of the coastline that allow for deformation to occur offshore. The minor tsunamigenesis can be accounted for by the deep slip patch. There is a significant uplift at the coast above it, which leads to negative maximum tsunami amplitudes. Finally, tide-gauge recordings show that edge-wave modes were excited and produce larger amplitudes and durations in the Gulf of Tehuantepec.