Search

Abstract Earthquakes that rupture several faults occur frequently within the shallow lithosphere but are rarely observed for intermediate‐depth events (70–300 km). On 29 November 2007, the Mw7.4 Martinique earthquake struck the Lesser Antilles Island Arc near the deep end of the Wadati‐Benioff‐Zone. The sparse regional seismic network of 2007 previously hampered a detailed examination of this unusually complex event. Here, we combine seismic data from different studies with regional moment tensor inversion results and 3D full‐waveform modeling. We show that the earthquake is a doublet consisting of dip‐slip and strike‐slip motion along two oblique structures, both activated under extensional stress along the strike of the slab. Comparison with tectonic reconstructions suggests that the earthquake ruptured along a re‐activated ridge‐transform segment of the subducted Proto‐Caribbean spreading ridge. The unprecedented resolution of the source process highlights the influence of pre‐existing structures on localizing slab deformation also at intermediate‐depth.

Abstract Recordings of earthquakes by a temporary deployment of 84 short period seismometers in northern Chile were used to derive regional 3D seismic velocity models for the Taltal segment. We used the Regressive ESTimator (REST) package for event detection and automatic onset estimation of P‐ and S‐wave arrival times to create an earthquake catalog with 23,985 hypocenters. We followed standard acceptability criteria (i.e., azimuthal gap and residual cutoff) to create a high‐quality data set and inverted for 3D Vp, Vs and Vp/Vs models using local earthquake tomography. Plots of hypocenters from the catalog, comprising 16,349 earthquakes, reveal active structures in the upper crust, dip changes along the slab and fracturing within the oceanic crust. Moreover, the wavespeed models illuminate anomalies in both the Nazca and South American plates that correlate with the observed seismicity distribution, including variations from low (1.75) to high (>1.80) Vp/Vs near the Atacama fault system on the coastline and the Domeyko Fault System in the forearc. The seismic velocity models also provide evidence for fluid circulation caused by the subducting Taltal ridge on the coast and partial melting feeding a volcanic complex close to the Andes. Finally, the observed low Vp/Vs ratios (∼1.75) are associated with copper mining operations in the area, suggesting that this kind of imaging can be used to characterize the distribution of potential ore deposits in the area.

Abstract Detecting phase arrivals and pinpointing the arrival times of seismic phases in seismograms is crucial for many seismological analysis workflows. For land station data, machine learning methods have already found widespread adoption. However, deep learning approaches are not yet commonly applied to ocean bottom data due to a lack of appropriate training data and models. Here, we compiled an extensive and labeled ocean bottom seismometer (OBS) data set from 15 deployments in different tectonic settings, comprising ∼90,000 P and ∼63,000 S manual picks from 13,190 events and 355 stations. We propose PickBlue, an adaptation of the two popular deep learning networks EQTransformer and PhaseNet. PickBlue joint processes three seismometer recordings in conjunction with a hydrophone component and is trained with the waveforms in the new database. The performance is enhanced by employing transfer learning, where initial weights are derived from models trained with land earthquake data. PickBlue significantly outperforms neural networks trained with land stations and models trained without hydrophone data. The model achieves a mean absolute deviation of 0.05 s for P‐waves and 0.12 s for S‐waves, and we apply the picker on the Hikurangi Ocean Bottom Tremor and Slow Slip OBS deployment offshore New Zealand. We integrate our data set and trained models into SeisBench to enable an easy and direct application in future deployments.

Abstract On June 21st, a Mw6.2 earthquake struck the Afghan‐Pakistan‐border‐region, situated within the India‐Asia collision. Thousand thirty‐nine deaths were reported, making the earthquake the deadliest of 2022. We investigate the event's rupture processes by combining seismological and geodetic observations, aiming to understand what made it that fatal. Our Interferometric Synthetic Aperture Radar‐constrained slip‐model and regional moment‐tensor inversion, confirmed through field observations, reveal a sinistral rupture with maximum slip of 1.8 m at 5 km depth on a N20°E striking, sub‐vertical fault. We suggest that not only external factors (event‐time, building stock) but fault‐specific factors made the event excessively destructive. Surface rupture was favored by the rock foliation, coinciding with the fault strike. The distribution of Peak‐Ground‐Velocity was governed by the sub‐vertical fault. Maximum slip was large compared to other events globally and might have resulted in peak‐frequencies coinciding with resonance‐frequencies of the local buildings and demonstrates the devastating impact of moderate‐size earthquakes.

Seismic imaging of subducted plates offers a way to improve plate tectonic reconstructions. Here, Braszus et al. use new ocean-bottom seismometer data from the Lesser Antilles to locate subducted spreading centres and faults thus providing a new understanding of the evolution of the Caribbean plate.

Abstract The crust and upper mantle structure of the Greater and Lesser Antilles Arc provides insights into key subduction zone processes in a unique region of slow convergence of old slow‐spreading oceanic lithosphere. We use ambient noise tomography gathered from island broadband seismic stations and the temporary ocean bottom seismometer network installed as part of the Volatile Recycling in the Lesser Antilles experiment to map crustal and upper mantle shear‐wave velocity of the eastern Greater Antilles and the Lesser Antilles Arc. Taking the depth to the 2.0 km/s contour as a proxy, we find sediment thickness up to 15 km in the south in the Grenada and Tobago basins and thinner sediments near the arc and to the north. We observe thicker crust, based on the depth to the 4.0 km/s velocity contour, beneath the arc platforms with the greatest crustal thickness of around 30 km, likely related to crustal addition from arc volcanism through time. There are distinct low velocity zones (4.2–4.4 km/s) in the mantle wedge (30–50 km depth), beneath the Mona Passage, Guadeloupe‐Martinique, and the Grenadines. The Mona passage mantle anomaly may be related to ongoing extension there, while the Guadeloupe‐Martinique and Grenadine anomalies are likely related to fluid flux, upwelling, and/or partial melt related to nearby slab features. The location of the Guadeloupe‐Martinique anomaly is slightly to the south of the obliquely subducted fracture zones. This feature could be explained by either three‐dimensional mantle flow, a gap in the slab, variable slab hydration, and/or melt dynamics including ponding and interactions with the upper plate.

Abstract Subduction zone dynamics are important for a better understanding of natural hazards, plate tectonics, and the evolution of the planet. Despite this, the factors dictating the location and style of volcanism are not well‐known. Here we present Rayleigh Wave imaging of the Lesser Antilles subduction zone using the ocean bottom and land seismic data collected as a part of the VoiLA experiment. This region is an important global endmember that represents a slow (

Volcanic activity at the Santiaguito dome complex (Guatemala) is characterized by lava extrusion interspersed with small, regular, gas-and-ash explosions that are believed to result from shallow magma fragmentation; yet, their triggering mechanisms remain debated. Given that the understanding of source processes at volcanoes is essential to risk assessments of future eruptions, this study seeks to shed light on those processes. We use data from a permanent seismic and infrasound network at Santiaguito volcano, Guatemala, established in 2018 and additional temporary stations, including a seismic array deployed during a 13-day field investigation in January 2019 to analyze and resolve the source characteristics of fragmentation leading to gas-and-ash explosions. Seismic data gathered within a distance of 4.5 km from the vent show a weak seismic signal 2–6 s prior to the explosions and associated main seismic signal. To resolve the source location and origin of the seismic signals, we first used ambient noise analysis to assess seismic velocities in the subsurface and then used two-dimensional spectral element modeling (SPECFEM2D) to simulate seismic waveforms. The analyzed data revealed a two-layer structure beneath the array, with a shallow, low-velocity layer (vs = 650 m/s) above deeper, high-velocity rocks (vs = 2,650 m/s). Using this velocity structure, possible source mechanisms and depths were constrained using array and particle motion analyses. The comparison of simulated and observed seismic data indicated that the precursory signal is associated with particle motion in the RZ-plane, pointing toward the opening of tensile cracks at a depth of ∼600 m below the summit; in contrast, the main signal is accompanied by a vertical single force, originating at a shallow depth of about ∼200 m. This suggests that the volcanic explosions at Santiaguito are following a bottom-up process in which tensile fractures develop at depth and enable rapid gas rise which leads to the subsequent explosion. The result indicates that explosions at Santiaguito do not occur from a single source location, but from a series of processes possibly associated with magma rupture, gas channeling and accumulation, and fragmentation. Our study provides a good foundation for further investigations at Santiaguito and shows the value of comparing seismic observations with synthetic data calculated for complex media to investigate in detail the processes leading up to gas-ash-rich explosions found at various other volcanoes worldwide.

The Sumatran subduction zone exhibits strong seismic and tsunamogenic potential with the prominent examples of the 2004, 2005 and 2007 earthquakes. Here, we invert travel-time data of local earthquakes for vp and vp∕vs velocity models of the central Sumatran forearc. Data were acquired by an amphibious seismometer network consisting of 52 land stations and 10 ocean-bottom seismometers located on a segment of the Sumatran subduction zone that had not ruptured in a great earthquake since 1797 but witnessed recent ruptures to the north in 2005 (Nias earthquake, Mw = 8.7) and to the south in 2007 (Bengkulu earthquake, Mw = 8.5). The 2-D and 3-D vp velocity anomalies reveal the downgoing slab and the sedimentary basins. Although the seismicity pattern in the study area appears to be strongly influenced by the obliquely subducting Investigator Fracture Zone to at least 200 km depth, the 3-D velocity model shows prevailing trench-parallel structures at depths of the plate interface. The tomographic model suggests a thinned crust below the basin east of the forearc islands (Nias, Pulau Batu, Siberut) at ∼ 180 km distance to the trench. vp velocities beneath the magmatic arc and the Sumatran fault zone (SFZ) are around 5 km s−1 at 10 km depth and the vp∕vs ratios in the uppermost 10 km are low, indicating the presence of felsic lithologies typical for continental crust. We find moderately elevated vp∕vs values of 1.85 at ∼ 150 km distance to the trench in the region of the Mentawai Fault. vp∕vs ratios suggest an absence of large-scale alteration of the mantle wedge and might explain why the seismogenic plate interface (observed as a locked zone from geodetic data) extends below the continental forearc Moho in Sumatra. Reduced vp velocities beneath the forearc basin covering the region between the Mentawai Islands and the Sumatra mainland possibly reflect a reduced thickness of the overriding crust.

Background In nonvalvular atrial fibrillation (AF), oral anticoagulants prevent ischemic strokes and transient ischemic attacks (TIAs), but nonpersistence with vitamin K antagonist (VKA) oral anticoagulant therapy (20–50% at 1 year) is problematic. The precise risk of stroke/TIA after VKA cessation and its time course during extended follow‐up is unknown. Methods and Results The study cohort of incident AF in patients receiving initial VKA between 2001 and 2013 was identified from the UK Clinical Practice Research Datalink (linked hospitalizations and causes of death). Using a nested case‐control analysis, patients with incident stroke/TIA were matched to patients without stroke/TIA (controls). Relative risk with time since VKA cessation compared with current VKA use was approximated from conditional logistic regression. We studied 16 696 patients with incident AF and initial VKA treatment. There were 489 stroke/TIA cases matched to 2137 controls (mean CHA2DS2‐VASc score 4.3). Compared with current VKA use, the excess incidence rate of stroke/TIA following VKA cessation in the first year after AF diagnosis was 2.29 (95% CI, 0.98–3.90) per 100 person‐years of VKA cessation or 1 additional stroke/TIA per 43 patients per year discontinuing VKA, compared with 1.43 (95% CI, 0.97–1.88) per 100 person‐years corresponding to 1 additional stroke/TIA per 70 patients per year, when VKA was discontinued more than 1 year after AF diagnosis. Conclusions VKA cessation is associated with a continuous excess thromboembolic stroke/TIA risk. Increasing oral anticoagulant persistence, especially in the year after AF diagnosis, should be a therapeutic target to reduce stroke/TIA in AF.

Objectives The purpose of this study was to estimate the annual incidence of Lyme disease (LD) in the UK.Design This was a retrospective descriptive cohort study.Setting Study data were extracted from the Clinical Practice Research Datalink (CPRD), a primary care database covering about 8% of the population in the UK in 658 primary care practices.Participants Cohort of 8.4 million individuals registered with general practitioners with 52.4 million person-years of observation between 1 January 2001 and 31 December 2012.Primary and secondary outcome measures LD was identified from recorded medical codes, notes indicating LD, laboratory tests and use of specific antibiotics. Annual incidence rates and the estimated total number of LD cases were calculated separately for each UK region.Results The number of cases of LD increased rapidly over the years 2001 to 2012, leading to an estimated incidence rate of 12.1 (95% CI 11.1 to 13.2) per 100 000 individuals per year and a UK total of 7738 LD cases in 2012. LD was detected in every UK region with highest incidence rates and largest number of cases in Scotland followed by South West and South England. If the number of cases has continued to rise since the end of the study period, then the number in the UK in 2019 could be over 8000.Conclusions The incidence of LD is about threefold higher than previously estimated, and people are at risk throughout the UK. These results should lead to increased awareness of the need for preventive measures.Trial registration number This study was approved by the Independent Scientific Advisory Committee for CPRD research (Protocol number 13_210R).

Detecting phase arrivals and pinpointing the arrival times of seismic phases in seismograms is crucial for many seismological analysis workflows. For land station data, machine learning methods have already found widespread adoption. However, deep learning approaches are not yet commonly applied to ocean bottom data due to a lack of appropriate training data and models. Here, we compiled an extensive and labeled ocean bottom seismometer (OBS) data set from 15 deployments in different tectonic settings, comprising ∼90,000 P and ∼63,000 S manual picks from 13,190 events and 355 stations. We propose PickBlue, an adaptation of the two popular deep learning networks EQTransformer and PhaseNet. PickBlue joint processes three seismometer recordings in conjunction with a hydrophone component and is trained with the waveforms in the new database. The performance is enhanced by employing transfer learning, where initial weights are derived from models trained with land earthquake data. PickBlue significantly outperforms neural networks trained with land stations and models trained without hydrophone data. The model achieves a mean absolute deviation of 0.05 s for P‐waves and 0.12 s for S‐waves, and we apply the picker on the Hikurangi Ocean Bottom Tremor and Slow Slip OBS deployment offshore New Zealand. We integrate our data set and trained models into SeisBench to enable an easy and direct application in future deployments. Plain Language Summary: Ocean bottom seismometers (OBS) are seismic stations on the seafloor. Just like their counterparts on land, they record many earthquakes on three component sensors but are additionally equipped with underwater hydrophones. To determine the location of an earthquake, seismologists must precisely measure the arrival times of seismic waves. For onshore data, machine learning (ML) has been highly successful in determining earthquake arrival times. However, the noise and the signal are different in the ocean environment. For example, the recordings can contain whale songs and water layer reverberations and are disturbed by ocean bottom currents. We have assembled an extensive database of ocean bottom recordings and trained artificial neural networks to use the underwater hydrophone information and cope with the ocean noise environment. We demonstrate that the resulting ML picker picks are similar to those of human experts and outperform phase pickers based on land data only. We compare earthquake catalogs based on different pickers created from an OBS deployment offshore New Zealand and demonstrate that PICKBLUE outperforms previous pickers. We make the database and ML picker available with a standard interface so that it is easy for other scientists to apply them in their studies. Key Points: We assembled a database of ocean Bottom Seismometer (OBS) waveforms and manual P and S picks, on which we train PickBlue, a deep learning pickerOur picker significantly outperforms pickers trained with land‐based data with confidence values reflecting the likelihood of outlier picksThe picker and database are available in the SeisBench platform, allowing easy and direct application to OBS traces and hydrophone records [ABSTRACT FROM AUTHOR]

Rapid transitions in eruptive activity during lava dome eruptions pose significant challenges for monitoring and hazard assessment. A comprehensive understanding of the dynamic evolution of active lava dome systems requires extensive sets of multi-parametric datasets to fully constrain and understand shifts in eruptive behavior, but few such datasets have been compiled. The Santiaguito lava dome complex, Guatemala, is a remarkable example of an open-vent volcanic system where continuous eruptive activity has typically been characterized by cycles of effusion and frequent, small to moderate, gas-and-ash explosions. During 2015–2016 the volcano experienced a rapid intensification of activity including large vulcanian explosions, frequently accompanied by pyroclastic density currents. Here we present a chronology of the eruptive activity at Santiaguito from November 2014–May 2017, compiled from field observations (visual and thermal) and activity reports. We also present seismic and acoustic infrasound data collected during the same period, the longest and largest dataset collected at Santiaguito to date. Three major phases of eruptive activity took place during the study period. The first phase was consistent with the long-term eruptive behavior reported at Santiaguito by previous studies: lava effusion simultaneous with small (