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Abstract Tectonic and seismogenic variations in subduction forearcs can be linked through various processes associated with subduction. Along the Cascadia forearc, significant variations between different geologic expressions of subduction appear to correlate, such as episodic tremor-and-slip (ETS) recurrence interval, intraslab seismicity, slab dip, uplift and exhumation rates, and topography, which allows for the systematic study of the plausible controlling mechanisms behind these variations. Even though the southern Cascadia forearc has the broadest topographic expression and shortest ETS recurrence intervals along the margin, it has been relatively underinstrumented with modern seismic equipment. Therefore, better seismic images are needed before robust comparisons with other portions of the forearc can be made. In March 2020, we deployed the Southern Cascadia Earthquake and Tectonics Array throughout the southern Cascadia forearc. This array consisted of 60 continuously recording three-component nodal seismometers with an average station spacing of ∼15 km, and stations recorded ∼38 days of data on average. We will analyze this newly collected nodal dataset to better image the structural characteristics and constrain the seismogenic behavior of the southern Cascadia forearc. The main goals of this project are to (1) constrain the precise location of the plate interface through seismic imaging and the analysis of seismicity, (2) characterize the lower crustal architecture of the overriding forearc crust to understand the role that this plays in enabling the high nonvolcanic tremor density and short episodic slow-slip recurrence intervals in the region, and (3) attempt to decouple the contributions of subduction versus San Andreas–related deformation to uplift along this particularly elevated portion of the Cascadia forearc. The results of this project will shed light on the controlling mechanisms behind heterogeneous ETS behavior and variable forearc surficial responses to subduction in Cascadia, with implications for other analogous subduction margins. 

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.

 

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.

 

Deep long-period earthquakes (DLPs) are an enigmatic type of volcanic seismicity that sometimes precedes eruptions but mostly occurs at quiescent volcanoes. These earthquakes are depleted in high-frequency content and typically occur near the base of the crust. We observed a near-periodic, long-lived sequence of more than one million DLPs in the past 19 years beneath the dormant postshield Mauna Kea volcano in Hawaiʻi. We argue that this DLP sequence was caused by repeated pressurization of volatiles exsolved through crystallization of cooling magma stalled beneath the crust. This “second boiling” of magma is a well-known process but has not previously been linked to DLP activity. Our observations suggest that, rather than portending eruptions, global DLP activity may more commonly be indicative of stagnant, cooling magma. 

Abstract We deployed a network of 68 three-component geophones on the slow-moving Two Towers earthflow in northern California. We compute horizontal-to-vertical spectral ratios (HVSRs) from the ambient seismic field. The HVSRs have two prominent peaks, one near 1.23 Hz and another between 4 and 8 Hz at most stations. The 1.23 Hz resonance is a property of the background noise field and may be due to a velocity contrast at a few hundred meters depth. We interpret the higher frequency peaks as being related to slide deposits and invert the spectral ratios for shallow velocity structure using in situ thickness measurements as a priori constraints on the inversion. The thickness of the shallowest, low-velocity layer is systematically larger than landslide thicknesses inferred from inclinometer data acquired since 2013. Given constraints from field observations and boreholes, the inversion may reflect the thickness of deposits of an older slide that is larger in spatial extent and depth than the currently active slide. Because the HVSR peaks measured at Two Towers are caused by shallow slide deposits and represent frequencies that will experience amplification during earthquakes, the depth of the actively sliding mass may be less relevant for assessing potential slide volume and associated hazard than the thicknesses determined by our inversions. More generally, our results underscore the utility of combining both geotechnical measurements and subsurface imaging for landslide characterization and hazard assessment. 

Low‐frequency earthquakes are a seismic manifestation of slow fault slip. Their emergent onsets, low amplitudes, and unique frequency characteristics make these events difficult to detect in continuous seismic data. Here, we train a convolutional neural network to detect low‐frequency earthquakes near Parkfield, CA using the catalog of Shelly (2017),https://doi.org/10.1002/2017jb014047as training data. We explore how varying model size and targets influence the performance of the resulting network. Our preferred network has a peak accuracy of 85% and can reliably pick low‐frequency earthquake (LFE) S‐wave arrival times on single station records. We demonstrate the abilities of the network using data from permanent and temporary stations near Parkfield, and show that it detects new LFEs that are not part of the Shelly (2017),https://doi.org/10.1002/2017jb014047catalog. Overall, machine‐learning approaches show great promise for identifying additional low‐frequency earthquake sources. The technique is fast, generalizable, and does not require sources to repeat.

 

From California to British Columbia, the Pacific Northwest coast bears an omnipresent earthquake and tsunami hazard from the Cascadia subduction zone. Multiple lines of evidence suggests that magnitude eight and greater megathrust earthquakes have occurred ‐ the most recent being 321 years ago (i.e., 1700 A.D.). Outstanding questions for the next great megathrust event include where it will initiate, what conditions are favorable for rupture to span the convergent margin, and how much slip may be expected. We develop the first 3‐D fully dynamic rupture simulations for the Cascadia subduction zone that are driven by fault stress, strength and friction to address these questions. The initial dynamic stress drop distribution in our simulations is constrained by geodetic coupling models, with segment locations taken from geologic analyses. We document the sensitivity of nucleation location and stress drop to the final seismic moment and coseismic subsidence amplitudes. We find that the final earthquake size strongly depends on the amount of slip deficit in the central Cascadia region, which is inferred to be creeping interseismically, for a given initiation location in southern or northern Cascadia. Several simulations are also presented here that can closely approximate recorded coastal subsidence from the 1700 A.D. event without invoking localized high‐stress asperities along the down‐dip locked region of the megathrust. These results can be used to inform earthquake and tsunami hazards for not only Cascadia, but other subduction zones that have limited seismic observations but a wealth of geodetic inference.