Journal of Geophysical Research: Solid Earth最新文献

Co-eruptive volcanic tremor during the 2021 Fagradalsfjall eruption in Iceland (19 March–18 September 2021) is characterized using seismic and visual data recorded close to the eruption site and across the Reykjanes Peninsula. An automatic seismic network-based approach reveals several tremor patterns associated with seven phases of the eruption, including (a) continuous tremor located beneath the eruption site and attributed to pressure changes in the shallow vent system(s) in phases I, III, and VII, and (b) two patterns of minute- and hour-long intermittent tremor in May (phase II) and July–August (phases IV–VI), respectively. The first intermittent pattern of minute-long tremor bursts associated with pulsating lava fountains in May is attributed to magma degassing in a shallow reservoir (top 100 m) connected to a top-conduit. The progressive enlargement of both the top-conduit and shallow reservoir with time is estimated quantitatively using a collapsing foam model. Sudden changes of their geometries, as detected from tremor characteristics, are systematically associated with observed crater collapse events. The second intermittent pattern of cyclic hour-long tremor episodes associated with episodic effusive activity in July–August is attributed to magma flowing and cooling in the feeder dike connected to a sill at 5 km depth. The sill is fed by a constant influx of magma from the deeper plumbing system and stores magma during low discharge periods. The observed cyclicity of both the eruptive activity and the tremor is interpreted quantitatively with a sill-dike model accounting for magma cooling and induced cyclic viscosity changes in the dike.

Uniformly spaced large-N seismic networks like the Swath-D (2017–2019), a densified part of the AlpArray initiative, provide unmatched opportunities to study microseismic activity and fault structures. Here, we show how the combined analysis of spatially and temporally clustered seismicity, precise relocations, waveform-based clustering, and moment tensor solutions for 67 earthquakes (1.13.3) help to characterize the heterogeneous study region in the south-eastern Alps. We observe a strong zonation, with clustered microseismicity predominantly in the SE and NW parts of the study area. The identified sequences indicate a dominance of swarms in the NW compared to more mainshock-aftershock sequences in the SE, while both sequence types can occur in both regions. We identify multiple short faults in the NW with lengths of a few hundred meters, and distinguish two close fault structures activated in one sequence based on waveform similarity and focal mechanisms. Compared to predominant thrust faulting in the SE, normal and strike-slip faulting in the NW points to high regional complexity, with local stresses deviating from simple expectations of thrust faulting resulting from the Europe-Adria convergence. We find that zones of increased microseismic activity match zones of high P wave attenuation from a recent Qp model developed in the AlpArray initiative (Jozi Najafabadi et al., 2023, https://doi.org/10.1186/s40623-023-01942-0), supporting our interpretations of spatiotemporal patterns concerning crustal properties and tectonic activity. Our findings agree well with the occurrence of large historical earthquakes while simultaneously shedding light on much smaller seismogenic features.

This study analyses how fault segmentation influences seismicity using 3 high-resolution earthquake catalogs from tectonically different areas. The studied event patterns reveal 8 fault steps with different 3D geometries reminiscent of relay zones, which refer to the area of displacement transfer between stepping and overlapping segments due to fault segmentation, including cylindrical, bifurcating, dip, strike and oblique relay zones as mapped from seismic reflection surveys. After detailed mapping of the spatiotemporal event migration, we analyze how 3D fault geometry, and in particular internal segmentation, controls event migration. First, we show that events can migrate continuously between segments via connected areas, producing along-step, around-step and bidirectional migrations, with steps acting as a barrier. Second, we observe seismicity that hops across bounding segments if a sufficiently strong magnitude event, with a relatively large rupture length compared to the step size, occurs near the step. Thus, fault segmentation, inherited from the early stage of fault development, controls earthquake migration patterns, if coupled with the type of forces driving seismicity. Specifically, tectonic-dominated sequences with relatively high magnitude seismicity and critically stressed segments promote inter-segment hopping. In contrast, fluid-dominated sequences, producing lower-magnitude events, are strongly channeled by connected segments.

Understanding the physical mechanisms of intraplate earthquakes requires a better understanding of the earthquake cycle and aseismic slip. A potential short-term repetition of intraplate earthquakes has been documented from interferometric synthetic aperture radar (InSAR) observations of two M_w5.9 events in northern Ibaraki Prefecture, Japan, which occurred on 19 March 2011 and 28 December 2016. However, our understanding based on surface geodetic measurements remains limited, particularly in terms of spatial resolution at depth. Here, we used the near-field seismic waveform data to complement InSAR data, thereby improving the spatial resolution of the coseismic slip distributions. First, we confirmed that the aftershocks of both events occurred along a common fault area. We then conducted a joint inversion that successfully reconciled near-identical surface displacements with the different seismic waveforms. Our results indicate that while the 2011 event mainly ruptured just updip of the hypocenter at 6 km depth, the 2016 rupture initiated deeper, at 10 km, and propagated to the shallow, northern side, causing large slip near the 2011 rupture area. The slip distributions of two events were complementary in the deeper portion (3 < z < 6 km), whereas those on the shallower side (z < 3 km) largely overlapped, resulting in nearly identical surface displacements. Small earthquakes rarely occur shallower than 3 km, suggesting that this shallow segment releases accumulated strain aseismically. The two events repeatedly triggered seismic slip on this typically aseismic shallow segment, possibly due to dynamic stress changes.

Over the last decade, the expansion and densification of broadband seismic networks has led to an increased interest in tomographic approaches based on gradiometry, which relies on the spatial derivatives of the phase and amplitude of surface wavefronts. Nevertheless, the use of amplitude information remains challenging. In this study, we present a novel approach to Helmholtz tomography based on the use of generalized wave equation smoothing splines. The application of these generalized smoothing splines results in the generation of smooth amplitude and phase fields that satisfy the Helmholtz and transport equations, and hence the wave equation. This enables the direct derivation of phase velocity maps using the Helmholtz equation. We apply this new Helmholtz tomography approach to Rayleigh waves recorded by permanent and temporary networks in Western Europe from 2008 to 2022 to obtain phase velocity maps for periods between 25 and 120 s. These maps reveal the detailed structure of the crust and upper mantle in this region. Compared to eikonal tomography, the phase velocity anomalies obtained by Helmholtz tomography are stronger in amplitude and more sharply defined.

We develop a new coupled hydro-mechanical-chemical (HMC) model to investigate the stress-controlled evolution of dissolution cavities along a hectometer-scale heterogeneous fracture. The fracture is conceptualized to consist of numerous patches associated with spatially-variable, stress and dissolution-dependent local stiffnesses and apertures. We consider the complete coupling relationships among mechanical deformation, fluid flow, and chemical dissolution within the fracture. More specifically, our model captures non-linear fracture deformational responses and their consequences on localized flow pattern and dissolutional aperture growth, as well as the feedback of dissolution to mechanical weakening and stress redistribution. We elucidate how geomechanical processes affect the aperture and flow patterns and the formation of small to large dissolution cavities. Our simulation results show that stress retards the permeability increase with the extent of retardation positively related to a dimensionless penetration length l_p′. Stress induces the splitting of the dissolution front, promoting localized flow and branched dissolution. At low l_p′ (wormhole dissolution regime), stress also promotes the sustained growth of dissolution branches. Hence, there is no apparent increase in global flow heterogeneity. At high l_p′, stress transitions the system from uniform dissolution into wormhole formation. Wormholes initiate from remote stiffer regions and converge toward the inlet. Our results have important implications for understanding various dissolution phenomena in subsurface fractured rocks, ranging from karstification to reservoir acidization.

Offshore distributed acoustic sensing (DAS) has emerged as a powerful technology for seismic monitoring, expanding the capacities of cable networks and coastal seismic networks to monitor offshore seismicity. However, offshore DAS data often combine signals unfamiliar to seismologists, including new types of instrumental noise and ocean signals that overprint those from tectonic sources, which may hinder seismological research. We develop a self-supervised deep learning algorithm, a masked auto-encoder (MAE), to denoise DAS data for seismological purposes. The model is trained on DAS recordings of local earthquakes with randomly masked channels acquired on fiber-optic cables in the Cook Inlet offshore Alaska. To demonstrate the benefits of denoising for seismological research, we conduct the most fundamental steps to build any earthquake catalog: seismic phase picking, signal-to-noise ratio (SNR) estimation, and event association. We leverage the generalizability of ensemble deep learning models with cross-correlation to predict phase picks with sufficient precision for post-processing (e.g., earthquake location). The SNR of the denoised S waves of testing DAS data increased by 2.5 dB on average. The MAE denoised, on average, DAS data allows 2.7 times more S picks than the original noisy data for smaller regional earthquakes. The results demonstrate that our self-supervised MAE can elevate the accuracy and efficiency of seismic monitoring with higher earthquake detectability.

Here we model crustal structure across a profile in the south-central United States extending from the Gulf of Mexico to northern Arkansas by jointly modeling P receiver functions and autocorrelograms. Interfaces visible in the P receiver functions vary across the profile. Beneath the Gulf Coastal Plain we find an intra-sedimentary contact, the basin bottom, and the Moho. Beneath the Ouachita Mountains two impedance contrasts are visible, as well as the Moho. In the Arkoma Basin, the basin bottom and Moho are visible. At the northern end of the profile only the Moho is visible. We observe basin depths of 0–13 km, and a Moho depth ranging from ∼26 to 47 km, with the Moho deepening to the north and shallowing to the south. We also observe faster crustal P velocities south of the Ouachita Mountains and slower values to the north, potentially reflecting changes in bulk crustal composition.

Linear magnetic anomalies (LMA), resulting from Earth's magnetic field reversals recorded by seafloor spreading serve as crucial evidence for oceanic crust formation and plate tectonics. Traditionally, LMA analysis relies on visual inspection and manual interpretation, which can be subject to biases due to the complexities of the tectonic history, uneven data coverage, and strong local anomalies associated with seamounts and fracture zones. In this study, we present a Machine learning (ML)-based framework to identify LMA, determine their orientations and distinguish spatial patterns across oceans. The framework consists of three stages and is semi-automated, scalable and unbiased. First, a generation network produces artificial yet realistic magnetic anomalies based on user-specified conditions of linearity and orientation, addressing the scarcity of the labeled training dataset for supervised ML approaches. Second, a characterization network is trained on these generated magnetic anomalies to identify LMA and their orientations. Third, the detected LMA features are clustered into groups based on predicted orientations, revealing underlying spatial patterns, which are directly related to propagating ridges and tectonic activity. The application of this framework to magnetic data from seven areas in the Atlantic and Pacific oceans aligns well with established magnetic lineations and geological features, such as the Mid-Atlantic Ridge, Reykjanes Ridge, Galapagos Spreading Center, Shatsky Rise, Juan de Fuca Ridge and even Easter Microplate and Galapagos hotspot. The proposed framework establishes a solid foundation for future data-driven marine magnetic analyses and facilitates objective and quantitative geological interpretation, thus offering the potential to enhance our understanding of oceanic crust formation.

Three long sediment cores recovered from the SE Pacific were subjected to a comprehensive magnetostratigraphic investigation. According to the partly preliminary age models obtained, the cores reach 48, 140, and 482 ka back in time, respectively. At two sites (PC02 and PC03 at ∼46°S) sedimentation rates are highly variable, ranging from about 2 to 100 cmka⁻¹, whereas sedimentation rates are significantly lower at the southernmost site (PC04, ∼51°S), ranging from about 1 to 20 cmka⁻¹. All three cores provide evidence for the Laschamps geomagnetic excursion at 41 ka. However, the excursion was recorded during times of low sedimentation rates and thus, only little detail on geomagnetic field variability could be obtained. Nevertheless, the excursion is documented in inclination and declination, thus as a full reversal, and is associated with a deep low in relative paleointensity. At site PC04, the Iceland Basin excursion (∼195 ka) was also recorded as a short almost reversed phase, followed by an apparently long phase of non-dipolar directions, all associated with a deep low in paleointensity. This is interpreted as the result of variable sedimentation rates not resolved by the preliminary age model of the core. A directional anomaly coinciding with a deep low in paleointensity at around 220 ka is interpreted as the Pringle Falls excursion. Besides these findings, no evidence for further excursions could be found in the studied cores.