Mapping landforms on the Moon is of great interest and importance for future human settlements and resources exploration. One of the first steps is to map the topography and investigate their shape and geometry in great detail and resolution, which would provide the first conditions for assessing their suitability for future on-site analysis. However, data from the Lunar Orbiter Laser Altimeter (LOLA) provide low resolution elevation maps in comparison to the size of detailed geological features. To improve resolution, we developed an inverse method to upscale topographic maps to a higher resolution using photographic data from the Lunar Reconnaissance Orbiter Camera (LROC). The method, which exploits the relation between topographic gradients and degrees of shading of incoming sunlight, shows an improvement from ~60 metres per pixel to 0.9 metres per pixel, bringing it to the same resolution as the optical images from LROC. Our method can detect craters as small as ~3 metres of diameter and, if illumination from several angles are available, is potentially a way to remove shades from complex features such as caves. It is also possible to estimate the error of the model due to uncertainties in the albedo.

Understanding the interaction between tectonic plates from geodetic data is relevant to the assessment of seismic hazard. To shed light on that prevalently slow aseismic interaction, we developed a new static-slip inversion strategy, the ELADIN (ELastostatic ADjoint INversion) method, that uses the adjoint elastostatic equations to compute the gradient of the cost function. To handle plausible slip constraints, ELADIN is a 2-step inversion algorithm. First it finds the slip that best explains the data without any constraint, and then refines the solution by imposing the constraints through a Gradient Projection Method. To obtain a selfsimilar, physically-consistent slip distribution that accounts for sparsity and uncertainty in the data, ELADIN reduces the model space by using a von Karman regularization function that controls the wavenumber content of the solution, and weights the observations according to their covariance using the data precision matrix. Since crustal deformation is the result of different concomitant interactions at the plate interface, ELADIN simultaneously determines the regions of the interface subject to both stressing (i.e., coupling) and relaxing slip regimes. For estimating the resolution, we introduce a mobile checkerboard that allows to determine lower-bound fault resolution zones for an expected slip-patch size and a given stations array. We systematically test ELADIN with synthetic inversions along the whole Mexican subduction zone and use it to invert the 2006 Guerrero Slow Slip Event (SSE), which is one of the most studied SSEs in Mexico. Since only 12 GPS stations recorded the event, careful regularization is thus required to achieve reliable solutions. We compared our preferred slip solution with two previously published models and found that our solution retains their most reliable features. In addition, although all three SSE models predict an upward slip penetration invading the seismogenic zone of the Guerrero seismic gap, our resolution analysis indicates that this penetration might not be a reliable feature of the 2006 SSE.

From interpreting data to scenario modeling of subduction events, numerical modeling has been crucial for studying tsunami generation by earthquakes. Seafloor instruments in the source region feature complex signals containing a superposition of seismic, ocean acoustic, and tsunami waves. Rigorous modeling is required to interpret these data and use them for tsunami early warning. However, previous studies utilize separate earthquake and tsunami models, with one-way coupling between them and approximations that might limit the applicability of the modeling technique. In this study, we compare four earthquake-tsunami modeling techniques, highlighting assumptions that affect the results, and discuss which techniques are appropriate for various applications. Most techniques couple a 3D Earth model with a 2D depth-averaged shallow water tsunami model. Assuming the ocean is incompressible and that tsunami propagation is negligible over the earthquake duration leads to technique (1), which equates earthquake seafloor uplift to initial tsunami sea surface height. For longer duration earthquakes, it is appropriate to follow technique (2), which uses time-dependent earthquake seafloor velocity as a time-dependent forcing in the tsunami mass balance. Neither technique captures ocean acoustic waves, motivating newer techniques that capture the seismic and ocean acoustic response as well as tsunamis. Saito et al. (2019) propose technique (3), which solves the 3D elastic and acoustic equations to model the earthquake rupture, seismic wavefield, and response of a compressible ocean without gravity. Then, sea surface height is used as a forcing term in a tsunami simulation. A superposition of the earthquake and tsunami solutions provides the complete wavefield, with one-way coupling. The complete wavefield is also captured in technique (4), which utilizes a fully-coupled solid Earth and ocean model with gravity (Lotto & Dunham, 2015). This technique, recently incorporated into the 3D code SeisSol, simultaneously solves earthquake rupture, seismic waves, and ocean response (including gravity). Furthermore, we show how technique (3) follows from (4) subject to well-justified approximations.

Corridors of size-selected crescent-shaped dunes, known as barchans, are commonly found in water, air, and other planetary environments. The growth of barchans results from the interplay between a fluid flow and a granular bed, but their size regulation involves intricate exchanges between different barchans within a field. One size-regulating mechanism is the binary interaction between nearby dunes, when two dunes exchange mass via the near flow field or by direct contact (collision). In a recent Letter (Assis & Franklin, GRL, 2020), we identified five different patterns arising from binary interactions of subaqueous barchans, and proposed classification maps. In this paper, we further inquire into binary exchanges by investigating the motion of individual grains while barchans interact with each other. The experiments were conducted in a water channel where the evolution of pairs of barchans in both aligned and off-centered configurations was recorded by conventional and high-speed cameras. Based on image processing, we obtained the morphology of dunes and motion of grains for all interaction patterns. We present the trajectories of individual grains, from which we show the origin and destination of moving grains, and their typical lengths and velocities. We also show that grains from the impacting dune spread with a diffusion-like component over the target barchan, and we propose a diffusion length. Our results provide new insights into the size-regulating mechanisms of barchans and barchanoid forms found on Earth and other planets.

Ionospheric perturbations induced by tsunamis and earthquakes can be used for tsunami early warning and remote sensing of earthquakes, provided the perturbations are characterized properly to distinguish them from the ones caused by other sources. The ionospheric perturbations are increasingly being obtained from Global Positioning System (GPS) based Total Electron Content (TEC) measurements sampled at uniform time intervals. However, the sampling is not uniform in space. The nonuniform spatial sampling along the GPS satellite tracks introduces aliasing if it is not accounted while computing the ionospheric perturbations. All the methods hitherto used to detect the co-seismic and tsunamigenic ionospheric perturbations did not account the nonuniform spatial sampling while computing these perturbations. In addition, the residual approach used to obtain the perturbations by detrending the TEC time series using high-order polynomial fit introduces artifacts. These aliasing and artifacts corrupt amplitude, Signal-to-Noise Ratio (SNR), phase, and frequency of ionospheric perturbations which are vital to distinguish the perturbations induced by tsunamis and earthquakes from the rest. We show that Spatio-Periodic Leveling Algorithm (SPLA) successfully removes such aliasing and artifacts. The efficiency of SPLA in removing the aliases and artifacts is validated under two simulated scenarios, and using GPS observations carried out during two natural disasters – the 2004 Indian Ocean tsunami and the 2015 Nepal-Gorkha earthquake. We, further, studied the severity of aliasing and artifacts on co-seismic and tsunamigenic perturbations by analyzing its characteristics employing SNR, spatiotemporal, and wavelet analyses. The results reveal that removal of aliasing and artifacts using SPLA i) increases the SNR up to ~149% compared to the residual method and ~39% compared to the differential method, ii) distinctly resolves signals from sharp static variations, and iii) detects 50% more co-seismic ionospheric perturbations and 25% more tsunami-induced ionospheric perturbations in the two events studied. Cross-correlation of the perturbation time series obtained using the residual method and SPLA reveals that aliasing and artifacts shift the time of occurrence by -7.64 minutes to +4.21 minutes. Further, the results show that the SPLA efficiently detects the ionospheric perturbations at low elevation angles, thereby removes the need of applying elevation cut-off and increases the area of ionospheric exploration of a GPS receiver.

Previous studies have interpreted Last Interglacial (LIG; ~129-116 ka) sea-level estimates in multiple different ways to calibrate projections of future Antarctic ice-sheet (AIS) mass loss and associated sea-level rise. This study systematically explores the extent to which LIG constraints could inform future Antarctic contributions to sea-level rise. We develop a Gaussian process emulator of an ice-sheet model to produce continuous probabilistic projections of Antarctic sea-level contributions over the LIG and a future high-emissions scenario. We use a Bayesian approach conditioning emulator projections on a set of LIG constraints to find associated likelihoods of model parameterizations. LIG estimates inform both the probability of past and future ice-sheet instabilities and projections of future sea-level rise through 2150. Although best-available LIG estimates do not meaningfully constrain Antarctic mass loss projections or physical processes until 2060, they become increasingly informative over the next 130 years. Uncertainties of up to 50 cm remain in future projections even if LIG Antarctic mass loss is precisely known (+/-5 cm), indicating there is a limit to how informative the LIG could be for ice-sheet model future projections. The efficacy of LIG constraints on Antarctic mass loss also depends on assumptions about the Greenland ice sheet and LIG sea-level chronology. However, improved field measurements and understanding of LIG sea levels still have potential to improve future sea-level projections, highlighting the importance of continued observational efforts.

The Kelvin-Helmholtz instability (KHI) at Earth’s magnetopause and associated turbulence are suggested to play a role in the transport of mass and momentum from the solar wind into Earth’s magnetosphere. We investigate electromagnetic turbulence observed in KH vortices encountered at the dusk flank magnetopause by the Magnetospheric Multiscale (MMS) spacecraft under northward interplanetary magnetic field (IMF) conditions in order to reveal its generation process, mode properties, and role. A comparison with another MMS event at the dayside magnetopause with reconnection but no KHI signatures under a similar IMF condition indicates that while high-latitude magnetopause reconnection excites a modest level of turbulence in the dayside low-latitude boundary layer, the KHI further amplifies the turbulence, leading to magnetic energy spectra with a power-law index –5/3 at magnetohydrodynamic scales even in its early nonlinear phase. The mode of the electromagnetic turbulence is analyzed with a single-spacecraft method based on Ampère’s law, developed by Bellan (2016), for estimating wave vectors as a function of spacecraft-frame frequency. The results suggest that the turbulence does not consist of propagating normal-mode waves, but is due to interlaced magnetic flux tubes advected by plasma flows in the vortices. The turbulence at sub-ion scales in the early nonlinear phase of the KHI may not be the cause of the plasma transport across the magnetopause, but rather a consequence of three-dimensional vortex induced reconnection, the process that can cause an efficient transport by producing tangled reconnected field lines.

The southern Granite-Rhyolite province contains a comprehensive record of lithospheric evolution in North America. During the last decade, increased seismicity along with improved seismic monitoring installations in Oklahoma provided a rich catalog of local earthquakes. The source-receiver geometry of this dataset is well posed to illuminate the middle and lower crust through long offset recordings of the Pg phase. We present a 3-D P-wave velocity model for central and north Oklahoma developed through a non-standard processing scheme applied to local earthquake waveforms recorded from 2010-2017, focusing on the deeper crust. We employed common-mid-point sorting, stacking, and inversion of Pg-phases which resulted in a set of localized velocity-depth functions up to depths of 40 km. Using this methodology, we significantly increased the S/N ratio for far offset (~250 km) local earthquake waveforms which led to the increase in depth of investigation for our final 3-D velocity model. We find high velocity (> 7 km/s) lower crust throughout the investigated area which suggests a mafic lower crust. The high velocities support previously established models which state that the lower crust of the Granite-Rhyolite province was derived from melting of older crust. We further relate shallow and middle crustal velocity anomalies to other data sets such as gravimetric and magnetic anomalies, and the spatial distribution of earthquakes. We interpret the Nemaha Fault system as a deep-rooted discontinuity which separates two crustal domains. On the contrary, we do not find clear evidence for the existence of the Midcontinent rift (MCR) in northern Oklahoma.

The Lower St. Lawrence Seaway (LSLS), in eastern Canada, is an important habitat for several species of endangered baleen whale. As we seek to reduce the hazards that these endangered species face from human activity, there is increasing demand for detailed knowledge of their habitat use. Only a sparse network of hydrophones exists in the LSLS to remotely observe whales. However, there is also a network of onshore seismometers, designed to monitor earthquakes, that have sufficiently high sample rates to record fin and blue whale calls. We present a simple method for detecting band-limited, regularly repeating calls, such as the 20 Hz calls of fin and blue whales, and apply the method to build a catalog of fin and blue whale detections at 14 onshore seismometers across the LSLS, over approximately a four-year period. The resulting catalog contains >600000 fin whale calls and >60000 blue whale calls. Individual calls are rarely detected at more than one seismometer. Fin whale calls recorded onshore appear to travel mainly through solid earth, rather than only entering the earth at the shoreline, and they often have a complex ~2 s sequence of P-like and S-like phases. Onshore seismometers provide a valuable, previously unused source of data for monitoring baleen whales. However, in the LSLS, the current seismometer network cannot provide high-precision whale tracking alone, so a denser deployment of onshore and/or offshore seismometers is required.

Advances in machine learning (ML) techniques and computational capacity have yielded state-of-the-art methodologies for processing, sorting, and analyzing large seismic data sets. In this work, we consider an application of ML for automatically identifying dominant types of impulsive seismicity contained in observations from a 34-station broadband seismic array deployed on the Ross Ice Shelf (RIS), Antarctica from 2014 to 2017. The RIS seismic data contain signals and noise generated by many glaciological processes that are useful for monitoring the integrity and dynamics of ice shelves. Deep clustering was employed to efficiently investigate these signals. Deep clustering automatically groups signals into hypothetical classes without the need for manual labeling, allowing for comparison of their signal characteristics and spatial and temporal distribution with potential source mechanisms. The method uses spectrograms as input and encodes their salient features into a lower-dimensional latent representation using an autoencoder, a type of deep neural network. For comparison, two clustering methods are applied to the latent data: a Gaussian mixture model (GMM) and deep embedded clustering (DEC). Eight classes of dominant seismic signals were identified and compared with environmental data such as temperature, wind speed, tides, and sea ice concentration. The greatest seismicity levels occurred at the RIS front during the 2016 El Niño summer, and near grounding zones near the front throughout the deployment. We demonstrate the spatial and temporal association of certain classes of seismicity with seasonal changes at the RIS front, and with tidally driven seismicity at Roosevelt Island.

Disaster risk reduction relies on quantitative estimates of the future impacts and consequences of known hazard threats in order to evaluate proposed mitigation and adaptation measures. Natural Resources Canada is collaborating with the Global Earthquake Model Foundation on the first ever national seismic risk assessment in Canada to inform disaster risk reduction planning by individuals, businesses and organizations working across all jurisdictional levels. The 2020 National Seismic Risk Model incorporates the 6th Generation National Seismic Hazard Map, a novel physical exposure model for the entire country, localized exposure models based on a machine learning approach to building categorization, and HAZUS-based earthquake building performance functions. Before results can be transmitted to end users, the model must be validated in a Canadian context using observations from real world disaster events or pre-existing catastrophic risk models. This study focuses on benchmarking the 2020 Canadian National Seismic Risk Model using shaking intensities and physical impacts recorded from the 2001 Mw 6.8 Nisqually and 2012 Mw 7.8 Haida Gwaii events, and the results of a 2013 catastrophic risk assessment performed by AIR Worldwide to evaluate the potential impact of major earthquakes in eastern Quebec and Cascadia. We compute anticipated building damage, economic loss, and fatalities for these benchmark scenario earthquakes using the OpenQuake engine and the national exposure dataset. Preliminary results indicate that the model results are largely consistent with observed or predicted impacts of these earthquakes in Canada, after adjusting for economic and population growth. Subsequently, we will evaluate the impact of running the Cascadia scenario using a regional building-level exposure database versus the national level inventory. Ultimately, this work will assess the ability of the National Seismic Risk Assessment to reproduce expected results, to ensure the applicability of this model in anticipating future outcomes at the national and local level.

Wind erosion is integral to the evolution of arid landscapes on Earth and Mars, but the nature of long-term wind erosion of bedrock is poorly understood. Here we describe the Salina del Fraile (SdF) depression in the Puna Plateau of the Central Andes, NW Argentina, as a landform excavated by wind over several million years. New structural cross-sections and a compilation of chronostratigraphic ages rule out the hypothesis that the depression was created by transtensional tectonics. Dated remnant lacustrine and alluvial deposits in the floor of the depression constrain the rate and timing of erosion. Late Oligocene–Miocene compressional folding uplifted friable strata that were preferentially eroded, resulting in the high-relief (900 m) depression. Up to 1.95 km and an average of 1.05 km of strata were eroded during the last 8.2 to 17 Ma, at rates of 0.06 to 0.23 mm/yr. These rates are similar to long-term average wind erosion rates reported in other regions. Coarse-grained eolian megaripples, yardangs, and elongated ridges indicate ongoing eolian abrasion and deflation, aided by salt weathering, of the floor of the depression. Megaripple migration across stony lag surfaces exposes fresh bedrock to continued erosion. The SdF also contains kilometerscale mesas and ridges that we interpret as erosional remnants. These landforms are similar to megayardangs and erosional topography identified on the lower flanks of Mount Sharp, Gale crater, Mars. In such hyperarid landscapes characterized by lithologic heterogeneities, high relief landforms can be generated and sustained by wind erosion, without significant fluvial or glacial incision.

Time-lapse gravimetry (repeat microgravity measurement) is a powerful tool for monitoring temporal mass distribution variations, including seasonal and long-term groundwater storage changes (GWSC). This geophysical method for measuring changes in gravity (Δg) is potentially applicable to any groundwater system. Here, I present Gravi4GW, a Python tool for the site-adapted calculation of β, the conversion factor between Δg and GWSC (also known as “topographic admittance”). Alpine catchments, in particular, are ideal target sites as they are highly sensitive to climate variations and can experience significant GWSC, while often lacking groundwater monitoring infrastructure. Therefore, to illustrate the usage of Gravi4GW, I investigate a detailed example of an alpine catchment and examine spatial variations and the effects of depth assumptions. This novel and accessible tool is designed to be useful in both the planning and data processing stages of time-lapse gravimetric field studies.

Adjoint waveform tomography, which is an emerging seismic imaging method for the crust- and global-scale problems, has gained popularity in the past and present decade. This study, for first time, applies adjoint waveform tomography to the large volume of seismic data recorded by the densely spaced, permanent monitoring network that covers the entirety of Japan. We develop a heterogeneous shear-wave velocity model of central Japan that agrees with the geology and lithology. The results reduce the time-frequency phase misfit by 16.4% in the 0.02–0.05 Hz frequency band and 6.7% in the 0.033–0.1 Hz band, respectively. We infer that some velocity anomalies resolved in this work would reflect the subsurface structures such as the volcanic fluids, dehydration of the subducted crust, and sedimentary basin. In addition, dense distributions of deep earthquakes are visible beneath the high-velocity blocks estimated in this study. The results of this study suggest the possibility of imaging large scale heterogeneous subsurface structures using waveform tomography with a densely distributed network of permanent seismometers.

The electrical transport behavior of lizardite was investigated by in-situ impedance measurements up to 22.6 GPa in a diamond anvil cell with comparation to its dehydrated counterpart. The conductivity of lizardite is found to increase one order of magnitude with increasing pressures from 0.2 to 1.9 GPa, due to pressure-activated ionic and electronic transportation. The proton hopping and hopping-created vacancy accounts for the conduction mechanisms. Compression initially promotes proton hopping at lower pressures and then impedes it at elevates pressure to make conduction purely electronic. Compared to the dehydrated specimen, the hydroxyl in lizardite enhances conductivity 4-7 times. The electronic resistivity at higher pressures gradually increases at a constant rate, except in the pressure range where pressure minimized the misfit structural disordering. The pressure-activated proton hopping in the lizardite and other phyllosilicates may ascribe the high conductive layer in the craton lithosphere and geoelectric anomalies related to earthquakes.

Transient creep occurs during geodynamic processes that impose stress changes on rocks at high temperatures. The transient is manifested as evolution in the viscosity of the rocks until steady-state flow is achieved. Although several phenomenological models of transient creep in rocks have been proposed, the dominant microphysical processes that control such behavior remain poorly constrained. To identify the intragranular processes that contribute to transient creep of olivine, we performed stress-reduction tests on single crystals of olivine at temperatures of 1250–1300°C. In these experiments, samples undergo time-dependent reverse strain after the stress reduction. The magnitude of reverse strain is ~10-3 and increases with increasing magnitude of the stress reduction. High-angular resolution electron backscatter diffraction analyses of deformed material reveal lattice curvature and heterogeneous stresses associated with the dominant slip system. The mechanical and microstructural data are consistent with transient creep of the single crystals arising from accumulation and release of backstresses among dislocations. These results allow the dislocation-glide component of creep at high temperatures to be isolated, and we use these data to calibrate a flow law for olivine to describe the glide component of creep over a wide temperature range. We argue that this flow law can be used to estimate both transient creep and steady-state viscosities of olivine, with the transient evolution controlled by the evolution of the backstress. This model is able to predict variability in the style of transient (normal versus inverse) and the load-relaxation response observed in previous work.

An earthquake-induced stress drop on a megathrust instigates different responses on the upper plate and slab. We mimic homogenous and heterogeneous megathrust interfaces at the laboratory scale to monitor the strain relaxation on two elastically bi-material plates by establishing analog velocity weakening and neutral materials. A sequential elastic rebound follows the coseismic shear-stress drop in our elastoplastic-frictional models: a fast rebound of the upper plate and the delayed and smaller rebound on the elastic belt (model slab). A combination of the rebound of the slab and the rapid relaxation (i.e., elastic restoration) of the upper plate after an elastic overshooting may accelerate the relocking of the megathrust. This acceleration triggers/antedates the failure of a nearby asperity and enhances the early slip reversal in the rupture area. Hence, the trench-normal landward displacement in the upper plate may reach a significant amount of the entire interseismic slip reversal and speeds up the stress build-up on the upper plate backthrust that emerges self-consistently at the downdip end of the seismogenic zones. Moreover, the backthrust switches its kinematic mode from a normal to reverse mechanism during the coseismic and postseismic stages, reflecting the sense of shear on the interface.

Clay material characterization is of importance for many geo-engineering and environmental applications, and geo-electrical methods are often used to detect them in the subsurface. Spectral induced polarization (SIP) is a geo-electric method that non-intrusively measures the frequency-dependent complex electrical conductivity of a material, in the mHz to the kHz range. We present a new SIP dataset of four different types of clay (a red montmorillonite sample, a green montmorillonite sample, a kaolinite sample, and an illite sample) at five different salinities (initially de-ionized water, ~10-3, ~10-2, ~10-1, and 1 mol/L of NaCl). We propose a new laboratory protocol that allows the repeatable characterization of clay samples. The complex conductivity spectra are interpreted with the widely used phenomenological double-Pelton model. We observe an increase of the real part of the conductivity with salinity for all types of clay, while the imaginary part presents a non monotonous behavior. The decrease of polarization over conduction with salinity is interpreted as evidence that conduction increases with salinity faster than polarization. We test the empirical petrophysical relationship between σ”surf and σ’surf and validate this approach based on our experimental data and two other datasets from the literature. With this dataset we can better understand the frequency-dependent electrical response of different types of clay. This unique dataset of complex conductivity spectra for different types of clay samples is a step forward toward better characterization of clay formations in situ.

Future mission carrying seismometer payloads to icy ocean worlds will measure global and local seismicity to determine where the ice shell is seismically active. We use two locations, a seismically active site on Gulkana Glacier, Alaska, and a more seismically quiet site on the northwestern Greenland Ice Sheet as geophysical analogs. We compare the performance of a single-station seismometer against a small-aperture seismic array to detect both high (> 1 Hz) and low (< 0.1 Hz) frequency events at each site. We created catalogs of high frequency (HF) and low frequency (LF) seismicity at each location using the automated Short-Term Average/ Long-Term Average technique. We find that with a 2-meter small-aperture seismic array, our detection rate increased (9 % for Alaska, 46% for Greenland) over the single-station approach. At Gulkana, we recorded an order of magnitude greater HF events than the Greenland site. We ascribe the HF events sources to a combination of icequakes, rockfalls, and ice-water interactions, while very high frequency events are determined to result from bamboo poles that were used to secure gear. We further find that local environmental noise reduces the ability to detect low-frequency global tectonic events. Based upon this study, we recommend that future missions consider the value of the expanded capability of a small array compared to a single station, design detection algorithms that can accommodate variable environmental noise, and assess the potential landings sites for sources of local environmental noise that may limit detection of global events.

Modelling the spatial distribution of infrasound attenuation (or transmission loss, TL) is key to understanding and interpreting microbarometer data and observations. Such predictions enable the reliable assessment of infrasound source characteristics such as ground pressure levels associated with earthquakes, man-made or volcanic explosion properties, and ocean-generated microbarom wavefields. However, the computational cost inherent in full-waveform modelling tools, such as Parabolic Equation (PE) codes, often prevents the exploration of a large parameter space, i.e., variations in wind models, source frequency, and source location, when deriving reliable estimates of source or atmospheric properties – in particular for real-time and near-real-time applications. Therefore, many studies rely on analytical regression-based heuristic TL equations that neglect complex vertical wind variations and the range-dependent variation in the atmospheric properties. This introduces significant uncertainties in the predicted TL. In the current contribution, we propose a deep learning approach trained on a large set of simulated wavefields generated using PE simulations and realistic atmospheric winds to predict infrasound ground-level amplitudes up to 1000 km from a ground-based source. Realistic range dependent atmospheric winds are constructed by combining ERA5, NRLMSISE-00, and HWM-14 atmospheric models, and small-scale gravity-wave perturbations computed using the Gardner model. Given a set of wind profiles as input, our new modelling framework provides a fast (0.05 s runtime) and reliable (~5 dB error on average, compared to PE simulations) estimate of the infrasound TL.

The establishment of the High Sensitivity Seismograph Network (Hi-net) in Japan has led to the discovery of deep low-frequency tremors. Since such tremors are considered to be related to large earthquakes adjacent to tremors on the same subducting plate interface, it is important in seismology to investigate tremors before establishing modern seismograph networks that record seismic data digitally. We propose a deep learning method to detect evidence of tremors from seismogram images recorded on paper more than 50 years ago. In our previous study, we constructed a convolutional neural network (CNN) based on the Residual Network (ResNet) structure and verified its performance through learning with synthetic images generated based on past seismograms. In this study, we trained the CNN with seismogram images converted from real seismic data recorded by Hi-net. The CNN trained by fine-tuning achieved an accuracy of 98.64% for determining whether an input image contains tremors. The Gradient-weighted Class Activation Mapping (Grad-CAM) heatmaps to visualize model predictions indicate that the CNN successfully detects tremors without affections of a variety of noises, such as teleseisms. The trained CNN was applied to the past seismograms recorded at the Kumano observatory, Japan, operated by Earthquake Research Institute, The University of Tokyo. The CNN shows the potential to detect tremors from past seismogram images for broader applications, such as publishing a new tremor catalog.

Between 81º30’ E and 83ºE the Himalayan range’s “perfect” arcuate shape is interrupted by an embayment. We hypothesize that thrust geometry and duplexing along the megathrust at mid-lower crustal depths plays a leading role in growth of the embayment as well the southern margin of the Tibetan plateau. To test this hypothesis, we conducted thermokinematic modeling of published thermochronologic data from the topographic and structural embayment in the western Nepal Himalaya to investigate the three-dimensional geometry and kinematics of the megathrust at mid-lower crustal depths. Models that can best reproduce observed cooling ages suggest that the megathrust in the western Nepal Himalaya is best described as two ramps connected by a long flat that extends further north than in segments to the east and west. These models suggest that the high-slope zone along the embayment lies above the foreland limb of an antiformal crustal accretion zone on the megathrust with lateral and oblique ramps at mid-lower crustal depths. The lateral and oblique ramps may have initiated by ca. 10 Ma. This process may have controlled along-strike variation in Himalayan-plateau growth and therefore development of the topographic embayment. Finally, we analyze geological and morphologic features and propose an evolution model in which landscape and drainage systems across the central-western Himalaya evolve in response to crustal accretion at depth and the three-dimensional geometry of the megathrust. Our work highlights the importance of crustal accretion at different depths in orogenic-wedge growth and that the mid-lower crustal accretion determines the location of plateau edge.

Geophysical granular flows exert basal forces that generate seismic signals, which can be used to better monitor and model these severe natural hazards. A number of empirical relations and existing models link these signals’ high-frequency components to a variety of flow properties, many of which are inaccessible by other analyses. However, the range of validity of the empirical relations remains unclear and the models lack validation, owing to the difficulty of adequately controlling and instrumenting field-scale flows. Here, we present laboratory experiments investigating the normal forces exerted on a basal plate by dense and partially dense flows of spherical glass particles. We measured the power spectra of these forces and inferred predictions for these power spectra from the models for debris flows’ seismic signals proposed by Kean et al. (2015), Lai et al. (2018), and Farin, Tsai, et al. (2019), using Hertz theory to extend Farin, Tsai, et al. (2019)’s models to higher frequencies. Comparison of our bservations to these predictions, and to predictions derived from Bachelet (2018) and Bachelet et al. (2021)’s model for granular flows’ seismic signals, shows those of Farin, Tsai, et al. (2019)’s ‘thin-flow’ model to be the most accurate, so we examine explanations for this accuracy and discuss its implications for geophysical flows’ seismic signals. We also consider the normalisation, by the mean force exerted by each flow, of the force’s mean squared fluctuations, showing that this ratio varies by four orders of magnitude over our experiments, but is determined by the bulk inertial number of the flow.

We report the first Terrestrial Electron Beam detected by the Atmosphere‐Space Interactions Monitor. It happened on 16 September 2018. The Atmosphere‐Space Interactions Monitor Modular X and Gamma ray Sensor recorded a 2 ms long event, with a softer spectrum than typically recorded for Terrestrial Gamma ray Flashes (TGFs). The lightning discharge associated to this event was found in the World Wide Lightning Location Network data, close to the northern footpoint of the magnetic field line that intercepts the International Space Station location. Imaging from a GOES‐R geostationary satellite shows that the source TGF was produced close to an overshooting top of a thunderstorm. Monte‐Carlo simulations were performed to reproduce the observed light curve and energy spectrum. The event can be explained by the secondary electrons and positrons produced by the TGF (i.e., the Terrestrial Electron Beam), even if about 3.5% to 10% of the detected counts may be due to direct TGF photons. A source TGF with a Gaussian angular distribution with standard deviation between 20.6° and 29.8° was found to reproduce the measurement. Assuming an isotropic angular distribution within a cone, compatible half angles are between 30.6° and 41.9°, in agreement with previous studies. The number of required photons for the source TGF could be estimated for various assumption of the source (altitude of production and angular distribution) and is estimated between 1017.2 and 1018.9 photons, that is, compatible with the current consensus.