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Records of Alpine microseismicity are a powerful tool to study landscape-shaping processes and warn against hazardous mass movements. Unfortunately, seismic sensor coverage in Alpine regions is typically insufficient. Here we show that distributed acoustic sensing (DAS) bridges critical observational gaps of seismogenic processes in Alpine terrain. Dynamic strain measurements in a 1 km long fiber optic cable on a glacier surface produce high-quality seismograms related to glacier flow and nearby rock falls. The nearly 500 cable channels precisely locate a series of glacier stick-slip events (within 20–40 m) and reveal seismic phases from which thickness and material properties of the glacier and its bed can be derived. As seismic measurements can be acquired with fiber optic cables that are easy to transport, install and couple to the ground, our study demonstrates the potential of DAS technology for seismic monitoring of glacier dynamics and natural hazards. In this study, Walter and colleagues deploy a 1 km long fiber optics cable on a glacier surface. Via the use of distributed acoustic sensing, the authors are capable of monitoring glacier dynamics and Alpine mass movements.

Archived seismograms recorded in the 20th century present a valuable source of information for monitoring earthquake activity. However, old data, which are only available as scanned paper-based images should be digitised and converted from raster to vector format prior to reuse for geophysical modelling. Seismograms have special characteristics and specific featuresrecorded by a seismometer and encrypted in the images: signal trace lines, minute time gaps, timing and wave amplitudes. This information should be recognised and interpreted automatically when processing archives of seismograms containing large collections of data. The objective was to automatically digitise historical seismograms obtained from the archives of the Royal Observatory of Belgium (ROB). The images were originallyrecorded by the Galitzine seismometer in 1954 in Uccle seismic station, Belgium. A dataset included 145 TIFF images which required automatic approach of data processing. Software for digitising seismograms are limited and many have disadvantages. We applied the DigitSeis for machine-based vectorisation and reported here a full workflowof data processing. This included pattern recognition, classification, digitising, corrections and converting TIFFs to the digital vector format. The generated contours of signals were presented as time series and converted into digital format (mat files) which indicated information on ground motion signals contained in analog seismograms. We performed the quality control of the digitised traces in Python to evaluate the discriminating functionality of seismic signals by DigitSeis. We shown a robust approach of DigitSeis as a powerful toolset for processing analog seismic signals. The graphical visualisation of signal traces and analysis of the performed vectorisation results shown that the algorithms of data processing performed accurately and can be recommended in similar applications of seismic signal processing in future related works in geophysical research.

A devastating doublet of earthquakes with moment magnitude MW 7.9 and MW 7.6 earthquakes contiguously occurred in SE Türkiye near the NW border of Syria. Here we perform a potency‐density tensor inversion to simultaneously estimate rupture evolution and fault geometry for the doublet. We find the initial MW 7.9 earthquake involved discrete episodes of supershear rupture and back‐rupture propagation, and was triggered by initial rupture along a bifurcated splay of the East Anatolian Fault. The second MW 7.6 event was triggered by the earlier MW 7.9 event, and it involved more extensive supershear rupture along a favorably curved fault, and was likely stopped by geometric barriers at the fault ends. Our results highlight the multi‐scale cascading rupture growth across the complex fault network that affects the diverse rupture geometries of the 2023 Türkiye earthquake doublet, contributing to the strong ground shaking and associated devastation. Plain Language Summary On 6 February 2023, devastating dual earthquakes; moment magnitude 7.9 and 7.6 events struck southern Türkiye near the northern border of Syria. The two earthquakes were only separated ∼90 km and ∼9 hr apart. The strong shaking from the two earthquakes caused significant damage to the buildings and people, having caused over 50,000 fatalities in Türkiye and Syria. The source region is where the Anatolian, Arabian and African plates meet, developing the network of faults that hosted the large devastating earthquakes. Seismological analyses using observed seismic waveforms are effective for rapidly estimating how the rupture of the two earthquakes evolves over such distinctively oriented and possibly segmented faults. We use the globally observed seismic records to simultaneously estimate rupture evolution and fault geometry of the earthquake doublet. We find the sequence of both earthquakes involves curved and segmented fault ruptures, including the back‐propagating rupture for the initial earthquake, which is facilitated by the complex active fault network. The 2023 earthquake doublet displays the irregular rupture evolution and diverse triggering behaviors both in a single event and across the earthquake sequence, which provide critical inputs in both our understanding of earthquake‐rupture dynamics and better assessment of future damaging earthquakes. Key Points An earthquake doublet of MW 7.9 and MW 7.6 ruptured multiple segments and curved faults Initial splay fault rupture triggered a large MW 7.9 rupture involving pulses of back‐propagating supershear rupture Multi‐scale rupture growth in a complex fault network may facilitate diverse rupture behaviors and triggering interactions in the doublet

The 28 September 2018 Mw 7.5 Palu earthquake occurred at a triple junction zone where the Philippine Sea, Australian, and Sunda plates are convergent. Here, we utilized Advanced Land Observing Satellite-2 (ALOS-2) interferometry synthetic aperture radar (InSAR) data together with broadband regional seismograms to investigate the source geometry and rupture kinematics of this earthquake. Results showed that the 2018 Palu earthquake ruptured a fault plane with a relatively steep dip angle of ~85°. The preferred rupture model demonstrated that the earthquake was a supershear event from early on, with an average rupture speed of 4.1 km/s, which is different from the common supershear events that typically show an initial subshear rupture. The rupture expanded rapidly (~4.1 km/s) from the hypocenter and propagated bilaterally towards the north and south along the strike direction during the first 8 s, and then to the south. Four visible asperities were ruptured during the slip pulse propagation, which resulted in four significant deformation lobes in the coseismic interferogram. The maximum slip of 6.5 m was observed to the south of the city of Palu, and the total seismic moment released within 40 s was 2.64 × 1020 N·m, which was equivalent to Mw 7.55. Our results shed some light on the transtensional tectonism in Sulawesi, given that the 2018 Palu earthquake was dominated by left-lateral strike slip (slip maxima is 6.2 m) and that some significant normal faulting components (slip maxima is ~3 m) were resolved as well.

High-quality data concerning the Tohoku Earthquake ( M 9.0) on March 11, 2011, were obtained from the deep borehole observation network (maximum depth of 1030 m; epicentral distance of approximately 600 km) of the Tono Research Institute of Earthquake Science. In addition to data acquired via seismometers, stress meters, and strain meters, barometric seismograms were recorded by several barometers that are usually used for weather observations. We examined the characteristics of barometric and stress seismograms and compared them to the data obtained using broadband seismometers, finding a shared feature: large amplitudes and long-period waveforms began with the arrival of surface waves. We also investigated the relationship between vertical movements observed with GPS and barometric variations and discovered that the barometric variations were related to the differential of vertical movements, while the vertical movements corresponded to the integral of barometric variations. All these results demonstrate that vertical movements at observation points can be computed from the barometric variations observed at those points.

Seismic wave arrival time measurements form the basis for numerous downstream applications. State‐of‐the‐art approaches for phase picking use deep neural networks to annotate seismograms at each station independently, yet human experts annotate seismic data by examining the whole network jointly. Here, we introduce a general‐purpose network‐wide phase picking algorithm based on a recently developed machine learning paradigm called Neural Operator. Our model, called Phase Neural Operator, leverages the spatio‐temporal contextual information to pick phases simultaneously for any seismic network geometry. This results in superior performance over leading baseline algorithms by detecting many more earthquakes, picking more phase arrivals, while also greatly improving measurement accuracy. Following similar trends being seen across the domains of artificial intelligence, our approach provides but a glimpse of the potential gains from fully‐utilizing the massive seismic data sets being collected worldwide. Plain Language Summary Earthquake monitoring often involves measuring arrival times of P‐ and S‐waves of earthquakes from continuous seismic data. With the advancement of artificial intelligence, state‐of‐the‐art phase picking methods use deep neural networks to examine seismic data from each station independently; this is in stark contrast to the way that human experts annotate seismic data, in which waveforms from the whole network containing multiple stations are examined simultaneously. With the performance gains of single‐station algorithms approaching saturation, it is clear that meaningful future advances will require algorithms that can naturally examine data for entire networks at once. Here we introduce a multi‐station phase picking algorithm based on a recently developed machine learning paradigm called Neural Operator. Our algorithm, called Phase Neural Operator, leverages the spatial‐temporal information of earthquake signals from an input seismic network with arbitrary geometry. This results in superior performance over leading baseline algorithms by detecting many more earthquakes, picking many more seismic wave arrivals, yet also greatly improving measurement accuracy. Key Points We introduce a multi‐station phase picking algorithm, Phase Neural Operator (PhaseNO), that is based on a new machine learning paradigm called Neural Operator PhaseNO can use data from any number of stations arranged in any arbitrary geometry to pick phases across the input stations simultaneously By leveraging the spatial and temporal contextual information, PhaseNO achieves superior performance over leading baseline algorithms

Meteoroid impacts shape planetary surfaces by forming new craters and alter atmospheric composition. During atmospheric entry and impact on the ground, meteoroids excite transient acoustic and seismic waves. However, new crater formation and the associated impact-induced mechanical waves have yet to be observed jointly beyond Earth. Here we report observations of seismic and acoustic waves from the NASA InSight lander’s seismometer that we link to four meteoroid impact events on Mars observed in spacecraft imagery. We analysed arrival times and polarization of seismic and acoustic waves to estimate impact locations, which were subsequently confirmed by orbital imaging of the associated craters. Crater dimensions and estimates of meteoroid trajectories are consistent with waveform modelling of the recorded seismograms. With identified seismic sources, the seismic waves can be used to constrain the structure of the Martian interior, corroborating previous crustal structure models, and constrain scaling relationships between the distance and amplitude of impact-generated seismic waves on Mars, supporting a link between the seismic moment of impacts and the vertical impactor momentum. Our findings demonstrate the capability of planetary seismology to identify impact-generated seismic sources and constrain both impact processes and planetary interiors. Impact-induced acoustic and seismic wave events on Mars recorded by the InSight lander’s seismometer have been traced to fresh craters observed in spacecraft imagery.

Differential rotation of Earth’s inner core relative to the mantle is thought to occur under the effects of the geodynamo on core dynamics and gravitational core–mantle coupling. This rotation has been inferred from temporal changes between repeated seismic waves that should traverse the same path through the inner core. Here we analyse repeated seismic waves from the early 1990s and show that all of the paths that previously showed significant temporal changes have exhibited little change over the past decade. This globally consistent pattern suggests that differential inner-core rotation has recently paused. We compared this recent pattern to the Alaskan seismic records of South Sandwich Islands doublets going back to 1964 and it seems to be associated with a gradual turning-back of the inner core relative to the mantle as a part of an approximately seven-decade oscillation, with another turning point in the early 1970s. This multidecadal periodicity coincides with changes in several other geophysical observations, especially the length of day and magnetic field. These observations provide evidence for dynamic interactions between the Earth’s layers, from the deepest interior to the surface, potentially due to gravitational coupling and the exchange of angular momentum from the core and mantle to the surface.Multidecadal oscillation of the Earth’s inner core, coinciding with length of day and magnetic field variations, is experiencing a pause and reversing, according to analysis of repeating seismic waves traversing the inner core since the 1960s.

Most aftershocks occur in areas experiencing large co‐seismic stress changes, yet some occur long after the mainshock in remote lightly stressed regions. The triggering mechanism of these remote delayed aftershocks is not well understood. Here, we study aftershocks occurring in the Dead Sea (DS) area following the 2023 Mw7.8 and Mw7.6 Kahramanmaraş earthquakes. Most aftershocks cluster along previously quiescent structures off‐ the main DS fault strand. Visual inspection disclosed three aftershocks instantaneously triggered by the Mw7.6 in the northern DS basin, and match‐filtering revealed a delayed aftershock. Waveform similarity and temporal clustering suggest the northern DS aftershocks re‐rupture a stick‐slip patch loaded by surrounding creep. Velocity‐gradient seismograms show the Mw7.6 exerted larger transient stresses than the Mw7.8, which may explain triggering by the Mw7.6, but not by the Mw7.8. This account of instantaneously triggered repeaters underscores the role of interactions between aseismic and seismic slip in remote triggering. Plain Language Summary Most aftershocks occur in areas experiencing large co‐seismic permanent stress changes, yet some occur long after the mainshock in remote regions experiencing small stress changes. The physics controlling the triggering of remote delayed aftershocks is not well understood. Here, we report on remotely triggered aftershocks in the Palestine Territories and Israel following the 2023 Kahramanmaraş earthquake pair. The main fault within the study area is the Dead Sea (DS) Transform (DST), yet most aftershocks occur on secondary structures located off‐ the main DST fault strand. This indicates off‐fault structures are presently more pre‐stressed than the main DST fault, which has important implications for seismic hazard analysis. We document a sequence of four aftershocks re‐rupturing the same fault patch in the Northern DS basin. Three of these aftershocks were triggered during the Mw7.6 surface‐wave passage, and one aftershock is delayed. We do not observe triggering in the study area due to the larger and closer Mw7.8. The non‐triggering by the Mw7.8 and later triggering by the Mw7.6 is explained in terms of the mainshock source properties. The aftershock decay rates and moments are consistent with a model in which a stick‐slip patch is being loaded by creep in the surrounding area. Key Points We document a dramatic earthquake rate increase in the Palestine Territories and Israel following the 2023 Kahramanmaraş earthquakes We provide the first account of instantaneous remotely triggered repeating aftershocks triggered by the Mw7.6 mainshock Non‐triggering by the Mw7.8 and later triggering by the Mw7.6 underscores the role of aseismic and seismic fault slip interactions

In this study, we build a multi‐station phase‐picking model named EdgePhase by integrating an Edge Convolutional module with a state‐of‐the‐art single‐station phase‐picking model, EQTransformer. The Edge Convolutional module, a variant of Graph Neural Network, exchanges information relevant to seismic phases between neighboring stations. In EdgePhase, seismograms are first encoded into the latent representations, then converted into enhanced representations by the Edge Convolutional module, and finally decoded into the P‐ and S‐phase probabilities. Compared to the standard EQTransformer, EdgePhase increases the precision (fraction of phase identifications that are real) and recall (fraction of phase arrivals that are identified) rate by 5% on our training and test data sets of Southern California earthquakes. To evaluate its performance in regions of different tectonic settings, we applied EdgePhase to detect the early aftershocks following the 2020 M7.0 Samos, Greece earthquake. Compared to a local earthquake catalog, EdgePhase produced 190% additional detections with an event distribution more conformative to a planar fault interface, suggesting higher fidelity in event locations. This case study indicates that EdgePhase provides a strong regional generalization capability in real‐world applications. Plain Language Summary Identifying seismic phases from continuous waveforms is an important task for earthquake monitoring and early warning systems. Traditional phase recognition methods include visual inspection and detections based on mathematical functions (e.g., STA/LTA, kurtosis, AIC). Recently, machine learning technology has been applied to this task because of its fast operation speed and complete automation. A variety of neural‐network‐based models take the waveforms of a single station as input and predict the P‐phases and S‐phases. In this study, we improve the model performance by taking into account the mutually consistent features in multiple stations. We incorporate a Graph Neural Network module to exchange information relevant to seismic phases between neighboring stations. Compared to the standard single station model, our multi‐station model performs better on seismic data in Southern California in terms of the precision and recall rate. We also tested our model on the 2020 M7.0 Greece, Samos Earthquake and found that it detected significantly more aftershocks compared to local catalogs in the first month after the mainshock. Key Points We developed EdgePhase, a multi‐station phase‐picking model, by fine‐tuning EQTransformer with Graphic Neural Networks Compared to the standard EQTransformer, EdgePhase increases the F1 score by 5% on the Southern California Seismic data set Performance evaluation of EdgePhase shows its strong generalization ability in real‐world applications