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Seismic tomography of shield volcanoes can be used to better understand its structure, formation, and evolution. Previous tomographic studies on the Island of Hawai'i used body waves from earthquakes and active sources and had limited resolution in the shallow crust. In this study, we obtained the empirical Green Functions (EGFs) and empirical Green Tensors (EGTs) from cross‐correlating and stacking of multiyear seismic ambient noise recorded on the island. The EGFs/EGTs contained fundamental mode and first higher mode Rayleigh waves. The different modes were separated with a new algorithm and their group velocities were measured. Using the group arrival times, we inverted for two‐dimensional group velocity maps, which provide, for the first time, a full coverage of the Island of Hawai'i. From the group velocity maps, we inverted for a three‐dimensional shear wave velocity model, which shows strong lateral variations and yields new insights into the structure and growth of the volcanoes on the island: Kı̄lauea's East Rift Zone has prominent high velocities at all depths, whereas the current rift zones of Mauna Loa are characterized by intermediate to high velocities only at depths greater than 1 km below ground surface, which may be attributed to their relatively short history and less developed state. The flanks of the volcanoes, some cut by fault zones, displayed low velocities at over a range of depths, generally interpreted as consisting of extrusive rocks, which could be further shattered by faulting.

 

Earthquakes result from fast slip that occurs along a fault surface. Interestingly, numerous dense geodetic observations over the last two decades indicate that such dynamic slip may start by a gradual unlocking of the fault surface and related progressive slip acceleration. This first slow stage is of great interest, because it could define an early indicator of a devastating earthquake. However, not all slow slip turns into fast slip, and sometimes it may simply stop. In this study, we use a numerical model based on the discrete element method to simulate crustal strike‐slip faults of 50 km length that generate a wide variety of slip‐modes, from stable‐slip, to slow earthquakes, to fast earthquakes, all of which show similar characteristics to natural cases. The main goal of this work is to understand the conditions that allow slow events to turn into earthquakes, in contrast to those that cause slow earthquakes to stop. Our results suggest that fault surface geometry and related dilation/contraction patterns along strike play a key role. Slow earthquakes that initiate in large dilated regions bounded by neutral or low contracted domains, might turn into earthquakes. Slow events occurring in regions dominated by closely spaced, alternating, small magnitude dilational and contractional zones tend not to accelerate and may simply stop as isolated slow earthquakes.

 

Abstract On 3 May 2018, Kīlauea Volcano, one of the most active volcanoes in the world, entered a new eruptive phase because of a dike intrusion in the East Rift zone. One day later, an Mw 6.9 earthquake, which was likely trigged by the dike intrusion, occurred in the submarine south flank of Kīlauea Volcano. In mid-July, an ocean-bottom seismometer (OBS) array consisting of 12 stations was deployed on the submarine south flank of Kīlauea Volcano to monitor the aftershocks and lava–water interaction near the ocean entry. Eleven OBSs were recovered in mid-September. Preliminary evaluation of the data reveals a large number of seismic and acoustic events, which provide a valuable dataset for understanding flank deformation and stability as well as lava–water interaction. Here, we introduce this dataset and document notable instrument malfunctions along with some initial seismic and acoustic observations. 

We propose a binary classification model rooted in state‐of‐the‐art deep learning techniques to predict whether or not complete‐interface rupture is imminent along a numerical megathrust fault. The models are trained on labeled 2D space‐time input features taken from the synthetic fault system. We contrast the performance of two neural networks trained on three types of data, to determine the relative predictive power of each. The neural networks are able to discriminate imminent complete rupture precursors from everything else, thus providing a relative size and time forecast. Vertical displacements along the fault demonstrate relatively good predictive power. The results confirm previous qualitative observations that precursory deformation scales with upcoming event size, consistent with the preslip model for earthquake nucleation. The methods we propose are adaptable and can be modified to use 3D data in the future.