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In this study, a multi-source data fusion method was proposed for the development of a Hybrid seismic hazard model (HSHM) in China by using publicly available data of the 5th Seismic Ground Motion Parameter Zoning Map (NSGM) and historical seismic catalogues and integrating with modern ground motion prediction equations (GMPEs). This model incorporates the characteristics of smoothed seismicity and areal sources for regional seismic hazard assessment. The probabilistic seismic hazard for the North China Plain earthquake belt was investigated through sensitivity analysis related to the seismicity model and GMPEs. The analysis results indicate that the Hybrid model can produce a consistent result with the NSGM model in many cases. However, the NSGM model tends to overestimate hazard values in locations where no major events have occurred and underestimate hazard values in locations where major events have occurred. The Hybrid model can mitigate the degree of such biases. Compared to the modern GMPEs, the GMPE with epicentral distance measures significantly underestimate the seismic hazard under near-field and large-magnitude scenarios. In addition, a comparison of the uniform hazard spectra (UHS) obtained by the models, with China’s design spectrum, shows that the current design spectrum is more conservative than the calculated UHS.

Rapid and accurate seismic source characterization significantly influences the performance of earthquake early warning (EEW) systems. However, the complexity of the seismic source modeling and the error accumulation during continuous characterization make it difficult to accurately characterize various source parameters. Furthermore, current artificial intelligence methods focus on a single task, lacking intertask fusion and guidance from specialized knowledge. In this study, we propose a novel deep learning (DL) algorithm (SeisParaNet) to estimate P-wave arrival time, source location, and magnitude simultaneously based on a multitask framework. To exploit seismological knowledge and attenuate the strong intertask dependencies, this study incorporates arrival time differences’ information into the analysis of source localization parameters using the attention mechanism and incorporates source location features into estimating local magnitude (Ml). In addition, SeisParaNet uses a probability-based self-attention mechanism (Prob-Attention) to extract temporal information from waveforms. Experimental results demonstrate that following a limited number of trainings on the STanford EArthquake Dataset (STEAD), SeisParaNet exhibits the capability to capture complex seismic patterns and rapidly characterize seismic sources. Furthermore, the introduction of Prob-Attention reduces computational complexity by 67%, validating the potential of SeisParaNet in EEW applications.

Determining source parameters of submarine earthquakes is fundamental to geotechnical and geodynamic research. However, these remote areas are usually poorly monitored, posing challenges in characterizing seismic behaviors. Here, we propose a wavelet-domain seismic source inversion method based on 3-D Green’s functions for modeling teleseismic body waves to obtain locations and focal mechanisms of submarine earthquakes. We adopt the heat bath algorithm to implement inversions. The wavelet transform allows us to simultaneously investigate source features in different frequencies. Numerical experiments conducted on synthetic data show our method is efficient and suggest that, for ~M6 earthquakes, lower-frequency waveforms (<0.1 Hz) are more sensitive to focal mechanisms, while higher frequencies (0.1~0.2 Hz) work better in constraining source locations. Application to a ~M6 earthquake in the Japan Trench suggests that our approach can provide reliable source parameters (locations and focal mechanisms) for submarine earthquakes.

This study examines the seismic source parameters and high-frequency spectral attenuation factor (κ) of 36 minor to moderate earthquake (EQ) events that have been occurred in the north-west Himalayas and its foothills of north India from 2007 to 2016. A non-reference generalised inversion technique has been used to compute the source spectra, and the point source model is used to estimate the seismic moment (M0) and corner frequency (fc). Further, a scaling relation between M0and fchas been developed for the study area. Additionally, based on the determined fcand M0values, source characteristics such as seismic energy (Es), stress drop (Δσ) and apparent stress drop (σa) are estimated for each of the 36 events. In the present work, σaand ∆σ were found to range from 0.1 MPa to 12.36 MPa and 0.17 MPa to 14.61 MPa, respectively. Low values of σaand ∆σ can be associated with the low effective stress model EQs. Furthermore, a majority of EQs appear to have a partial stress drop mechanism according to the Zúñiga parameter (ε) determined for the region. Also, the parameter κis estimated and is found to range between 0.015 and 0.071 for the horizontal component, and 0.01 and 0.036 for the vertical component. The epicentre distance for various site classes is used to construct a regional model for κ. Finally, stochastic ground motion simulations of historical EQ records utilising estimated source parameters and constructed kappa models are performed in order to validate the findings of the present work. The comparison of the original and simulated EQ records is in very good agreement, demonstrating the efficacy of the obtained parameters in capturing the local ground motion features.

Seismic source location is a classic inverse problem in seismology. In mathematical physics, inverse problems have multiple natural solutions. The objective of this study was to develop a generic theory and method of seeking the true solution from multiple solutions for the location of a coal mine seismic source in an idealized velocity structure model of a coal mine with a small scale and complex geological environment. Starting from the simplest velocity structure model, the complexity of the model gradually increased, until it approached the real velocity structure model, i.e., the multilayered horizontal and inclined velocity structure model, in order to find a generic method for solving the multi-solution inverse problem of coal mine seismic source location. Specifically, the wavefront distribution equation in a two-layer horizontal medium was derived and then expanded to any multi-layer horizontal medium. Based on this equation, a positive definite nonlinear equation system was established from the perspective of any observation system. The equation system contained four unknown variables of the spatiotemporal position of the seismic source. To determine the spatiotemporal parameters of the seismic source, nonlinear equations for four stations were required. To solve the nonlinear equation system, an initial iteration value was determined. In order to reduce the difficulty of determining the initial iteration value, the variable substitution method was used to reduce the number of location parameters. By rotating the original geodetic coordinate system of the station to be parallel and orthogonal to the medium interface, the wavefront method was extended to inclined medium. In conclusion, in this study, the problem of coal mine seismic source location in a multi-layer horizontal or inclined medium was effectively solved. The method proposed in this study provides a reference for solving the true solution from multiple solutions for the location of a coal mine seismic source in small-scale coal mines with complex geological environments.

The use of geodetic observation data for seismic fault parameters inversion is the research hotspot of geodetic inversion, and it is also the focus of studying the mechanism of earthquake occurrence. Seismic fault parameters inversion has nonlinear characteristics, and the gradient-based optimizer (GBO) has the characteristics of fast convergence speed and falling into local optimum hardly. This paper applies GBO algorithm to simulated earthquakes and real LuShan earthquakes in the nonlinear inversion of the Okada model to obtain the source parameters. The simulated earthquake experiment results show that the algorithm is stable, and the seismic source parameters obtained by GBO are slightly closer to the true value than the multi peak particle swarm optimization (MPSO). In the 2013 LuShan earthquake experiment, the root mean square error between the deformation after forwarding of fault parameters obtained by the introduced GBO algorithm and the surface observation deformation was 3.703 mm, slightly better than 3.708 mm calculated by the MPSO. Moreover, the inversion result of GBO algorithm is better than MPSO algorithm in stability. The above results show that the introduced GBO algorithm has a certain practical application value in seismic fault source parameters inversion.

Dynamic failure of rock masses around deep tunnels, such as fault-slip rockburst and seismic-induced collapse, can pose a significant threat to tunnel construction safety. One of the most significant factors that control the accuracy of its risk assessment is the estimation of the ground motion around a tunnel caused by seismicity events. In general, the characteristic parameters of ground motion are estimated in terms of empirical scaling laws. However, these scaling laws make it difficult to accurately estimate the near-field ground motion parameters because the roles of control factors, such as tunnel geometry, damage zone distribution, and seismic source parameters, are not considered. For this, the finite fracturing seismic source model (FFSSM) proposed in this study is used to simulate the near-field ground motion characteristics around deep tunnels. Then, the amplification effects of ground motion caused by the interaction between seismic waves and deep tunnels and corresponding control factors are studied. The control effects of four factors on the near-field ground motion amplification effect are analyzed, including the main seismic source wavelength, tunnel span, tunnel shape, and range of damage zones. An empirical formula for the maximum amplification factor (αm) of the near-field ground motion around deep tunnels is proposed, which consists of four control factors, i.e. the wavelength control factor (Fλ), tunnel span factor (FD), tunnel shape factor (Fs) and excavation damage factor (Fd). This empirical formula provides an easy approach for accurately estimating the ground motion parameters in seismicity-prone regimes and the rock support design of deep tunnels under dynamic loads.

In regions of infrequent moderate-to-large earthquakes, historic earthquake catalogs are often insufficient to provide inputs to seismic-hazard analyses (i.e. fault locations and magnitude–frequency relations) or to inform ground-motion predictions for certain seismic sources. In these regions, analysis of relic coseismic evidence, such as paleoliquefaction, is commonly used to infer information about the seismic hazard. However, while paleoliquefaction studies have been performed widely, all existing analysis techniques require a prioriassumptions about the causative earthquake’s location (i.e. rupture magnitude and ground motions can otherwise not be estimated). This may lead to inaccurate assumptions in some settings, and by corollary, erroneous results. Accordingly, this article proposes an inversion framework to probabilistically constrain seismic-source parameters from paleoliquefaction. Analyzing evidence at regional scale leads to (a) a geospatial likelihood surface that constrains the rupture location and (b) a probability distribution of the rupture magnitude, wherein source-location uncertainty is explicitly considered. Simulated paleoliquefaction studies are performed on earthquakes with known parameters. These examples demonstrate the framework’s potential, even in cases of limited field evidence, as well as important caveats and lessons for forward use. The proposed framework has the potential to provide new insights in enigmatic seismic zones worldwide.

Seismic source locations are fundamental to many fields of Earth and planetary sciences, such as seismology, volcanology and tectonics. However, seismic source detection and location are challenging when events cluster closely in space and time with signals tangling together at observing stations, such as they often do in major aftershock sequences. Though emerging algorithms and artificial intelligence (AI) models have made processing high volumes of seismic data easier, their performance is still limited, especially for complex aftershock sequences. In this study, we propose a novel approach that utilizes three‐dimensional image segmentation—a computer vision technique—to detect and locate seismic sources, and develop this into a complete workflow, Source Untangler Guided by Artificial intelligence image Recognition (SUGAR). In our synthetic and real data tests, SUGAR can handle complex, energetic earthquake sequences in near real time better than skillful analysts and other AI and non‐AI based algorithms. We apply SUGAR to the 2016 Kaikōura, New Zealand sequence and obtain five times more events than the analyst‐based GeoNet catalog. The improved aftershock distribution illuminates a continuous fault system with extensive fracture zones beneath the segmented, discontinuous surface ruptures. Our method has broader applicability to non‐earthquake sources and other time series image data sets. Detecting and locating earthquakes is fundamental to seismology, volcanology, and tectonics. A number of emerging algorithms, including some based upon artificial intelligence (AI), have made processing large volumes of seismic data much easier. However, their performance is still limited, especially in clustered aftershock sequences whose signals overlap at observing seismographs. We propose a new, AI computer vision‐based approach to this problem, and develop it into a complete earthquake detection and location workflow, named SUGAR. Tests on synthetic and real earthquake data sets show that SUGAR characterizes complex earthquake sequences better than other AI and non‐AI algorithms or professional analysts. We apply SUGAR to the complex aftershock sequence of the 2016 Mw7.8 Kaikōura, New Zealand earthquake, detecting five times more events than the analyst‐based GeoNet catalog. Whereas surface breaks of the Kaikōura earthquake are highly discontinuous, our improved aftershock distribution supports a continuous fault system surrounded by extensive fracture zones at depth. Our method has broader potential for other types of seismic sources and image series. We propose a new seismic source detection and location approach based on the source‐scanning algorithm and 3D image segmentationThis approach outperforms human analysts and popular artificial intelligence (AI) and non‐AI based methods in characterizing intense aftershock sequencesThe resulting catalog of the 2016 Kaikōura earthquake sequence suggests a continuous fault system surrounded by extensive fracturing We propose a new seismic source detection and location approach based on the source‐scanning algorithm and 3D image segmentation This approach outperforms human analysts and popular artificial intelligence (AI) and non‐AI based methods in characterizing intense aftershock sequences The resulting catalog of the 2016 Kaikōura earthquake sequence suggests a continuous fault system surrounded by extensive fracturing

The violent eruption of the Hunga (Tonga) submarine volcano on 15 January 2022 caused a 58 km‐heigh ash plume, catastrophic tsunami, and significant global seismic and infrasound waves. However, the physical mechanism underpinning its multiple‐explosive events remains unclear, and its resolvability relies on the seismic waveform source inversion. The studies of two different point‐source models, the seismic moment tensor (MT) and the single force (SF), have been performed separately for this eruption, which, interestingly, can explain the seismic data adequately. Here, we use a joint inversion of MT and SF to unravel a composite source of an explosion‐like MT and a significant upward force for the first major explosive event. Regarding the direction and magnitude, we propose that the upward force is likely a rebound force in response to the pressure drop on the seafloor because the water body above the volcano was abruptly uplifted by the shallow underwater explosion. The physical process of the violent eruption of the Hunga (Tonga) submarine volcano on 15 January 2022 remains unclear. To date, the common source model for volcano eruptions—a single force (SF) and the common source model for earthquakes and explosions—a moment tensor (MT), have been inferred individually for this eruption. Interestingly, both can explain the recorded seismic signals reasonably well. A question arises whether a combination of sources is a better physical model. Therefore, we combine the MT and SF to represent the eruption process in this study. The source analysis for the first major event of this eruption reveals a possible composite process of a shallow underwater explosion and a significant upward force. The upward force is opposite the common downward‐reaction force to the material jetting. It is likely caused by the abrupt displacement of the water above the volcano resulting from the shallow underwater explosion. When the downward water pressure on the seafloor vanishes, the seafloor responds by an upward‐rebound force. We perform probabilistic inversion of seismic waveform data to study the force equivalent system of the 2022 Hunga (Tonga) eruptionThe first major explosive event of the shallow eruption sequences may consist of an explosion‐like moment tensor and a large upward forceA possible mechanism of the accompanying upward force is the rebound force responding to the sudden pressure drop of uplifted water body We perform probabilistic inversion of seismic waveform data to study the force equivalent system of the 2022 Hunga (Tonga) eruption The first major explosive event of the shallow eruption sequences may consist of an explosion‐like moment tensor and a large upward force A possible mechanism of the accompanying upward force is the rebound force responding to the sudden pressure drop of uplifted water body

A minequake of magnitude ML4.1 occurred on 18 May 2020 early in the morning at the LKAB underground iron ore mine in Kiruna, Sweden. This is the largest mining‐induced earthquake in Scandinavia. It generated acoustic signals observed at three infrasound arrays at 9.3 (KRIS, Sweden), 155 (IS37, Norway), and 286 km (ARCI, Norway) distance. We perform full‐waveform focal mechanism inversion based on regional seismic data and local infrasound data. These independently highlight that this event was dominated by a shallow‐depth collapse in agreement with in‐mine seismic station data. However, regional infrasound data cannot inform the inversion process without an accurate model of atmospheric winds and temperatures. Yet, our numerical simulations demonstrate a potential of using local and regional infrasound data to constrain an event's focal mechanism and depth. The largest mining‐induced earthquake in Scandinavia (ML4.1) occurred on 18 May 2020 early in the morning at the LKAB underground iron ore mine in Kiruna, Sweden. The seismic waves coupled to the atmosphere and propagated large distances as sound waves which were observed at three infrasound arrays at 9.3 (KRIS, Sweden), 155 (IS37, Norway), and 286 km (ARCI, Norway) distance. Our seismic and acoustic modeling results highlight a strong collapse event within the northern section of the mine. The modeling of acoustic and seismic waves across the Earth‐atmosphere suggests that sound wave data can help when determining the location and properties of a seismic source. Seismic and acoustic records indicate a strong collapse at shallow depth of 1  ±  0.5 km for the 2020 Kiruna minequakeFocal mechanisms and depths of shallow seismic sources can be retrieved from local infrasound recordsInversions using regional infrasound data is possible when accurate weather models are available Seismic and acoustic records indicate a strong collapse at shallow depth of 1  ±  0.5 km for the 2020 Kiruna minequake Focal mechanisms and depths of shallow seismic sources can be retrieved from local infrasound records Inversions using regional infrasound data is possible when accurate weather models are available

Between 2018 and 2021, AIMS and its partners conducted a large-scale experiment to investigate the effect of exposure to a 3D seismic survey on pearl oysters. In August 2018, ≈11 000 adult wild silverlip pearl oysters (Pinctada maxima ) were collected and attached to bottom culture lines to acclimatise for 1 month at a holding lease site, 40 km off Broome, WA. These oysters were split across rows configured parallel to each other at seven horizontal distances up to 6000 m from a ‘zero line’. In September 2018, we sailed a commercial seismic vessel along one 20 km-long inactive (vessel control) sail line along the zero line, followed by six operational (exposure) sail lines, at increasing distances from the zero line, with each line separated in time by either 12 or 24 h. This design resulted in groups of pearl oysters with 35 different exposure histories (seven distances, five exposure distances) that were then transferred to undergo either lethal biophysical sampling or pearl seeding for a ‘grow-out’ study of pearl production. Biophysical sampling included immunology tests, primarily using flow cytometry and gene expression changes in nacre- and stress-associated genes. Audits of oyster growth and condition were conducted at various time points between the exposure period and harvesting of the seeded oysters. The number and quality of pearls produced by each exposure group was assessed 2 years after exposure (December 2020). The results of these analyses are being finalised for presentation.

Genuine non‐double‐couple (non‐DC) components of a seismic source, defined here as the non‐DC components that are not due to summation of pure double‐couple (DC) components, provide important insight into special physical processes in non‐earthquake sources such as explosion, volcano eruption and collapse etc. Yet they remain challenging to be resolved. To address the issue and explore the physical mechanism of those special events, we develop a waveform‐polarity‐based moment tensor (WPMT) inversion method and employ it to study physical process in the 2014–2015 Bárðarbunga volcano event sequence. The WPMT method incorporates P‐wave polarity data and seismic waveforms in the source inversion, designs a source simplicity test to check possible complex rupture in the seismic source, and employs a simulated annealing algorithm to search the best source solution. The simplicity test checks consistency of the source processes in the initiation stage of the event and the major energy release process of the event, thus ensuring that the inferred non‐DC components are genuine to the seismic source. Real event and synthetic tests indicate that the WPMT method can identify and resolve genuine non‐DC components in a seismic source. The WPMT inversions of the Bárðarbunga sequence yield many genuine non‐DC source components and reveal that the eruptions are accompanied by seismic activities in depths of 1–5 km with magma migrations out of chambers, collapses of conduits, failures of normal faults, and a magma recharge at a depth of 9 km accompanied by a failure on a nearby normal fault. Special seismic events such as explosion, collapse and volcanic eruption possess source components that are different from fault solutions used to represent a typical tectonic earthquake of simple shear. The identification and quantification of the source components of those special seismic sources thus play an important role in deciphering the physical process during those events. Yet, it remains challenging to resolve the source components of special seismic events due to the ambiguity of distinguishing them from an apparent one due to complex earthquake rupture. We develop a waveform‐polarity‐based moment‐tensor inversion to resolve the source components of a special seismic source and employ the method to study the physical process in the 2014–2015 Bárðarbunga volcano event sequence in Iceland. Real event and synthetic tests confirm method's capability of identifying and constraining the source components of special non‐earthquake events. The study of the Bárðarbunga sequence reveals that the eruptions are accompanied by seismic activities in depths of 1–5 km with magma migrations out of chambers, collapses of conduits, failures of normal faults, and a magma recharge at a depth of 9 km accompanied by a failure on a nearby normal fault. We propose a waveform‐polarity‐based moment tensor inversion method to extract non‐double‐couple components of moderate seismic eventsReal event and synthetic tests show the method is effective in distinguishing and resolving genuine non‐double‐couple componentsMethod application reveals magma activities, conduit collapses and induced earthquakes during the 2014–2015 Bárðarbunga eruptions We propose a waveform‐polarity‐based moment tensor inversion method to extract non‐double‐couple components of moderate seismic events Real event and synthetic tests show the method is effective in distinguishing and resolving genuine non‐double‐couple components Method application reveals magma activities, conduit collapses and induced earthquakes during the 2014–2015 Bárðarbunga eruptions

Up to now, the possibility to obtain images of seismic source from ionospheric Global Navigation Satellite Systems (GNSS) measurements (seismo‐ionospheric imagery) has only been demonstrated for giant earthquakes with moment magnitude Mw ≥ 9.0. In this work, we discuss difficulties and restrictions of this method, and we apply for the first time the seismo‐ionospheric imagery for smaller earthquakes. The latter is done on the example of the Mw7.4 Sanriku‐oki earthquake of 9 March 2011. Analysis of 1‐Hz data of total electron content (TEC) shows that the first coseismic ionospheric disturbances (CID) occur ~470–480 s after the earthquake as TEC enhancement on the east‐northeast from the epicenter. The location of these first CID arrivals corresponds to the location of the coseismic uplift that is known as the source of tsunamis. Our results confirm that despite several difficulties and limitations, high‐rate ionospheric GNSS data can be used for determining the seismic source parameters for both giant and smaller/moderate earthquakes. In addition to these seismo‐ionospheric applications, we raise several fundamental questions on CID nature and evolution, namely, one of the most challenging queries—can moderate earthquake generate shock‐acoustic waves? Ionosphere is a layer of charged particles of the Earth's atmosphere located at altitudes ~60–800 km. However, despite being high above the Earth's surface, the ionosphere is sensible to numerous near‐ground geophysical events (earthquakes, tsunamis, volcano eruptions, etc). Acoustic and gravity waves emitted by these events propagate upward and generate atmospheric/ionospheric perturbations. Ionospheric disturbances generated by earthquakes are known as coseismic ionospheric disturbances (CID). Recently, it has been suggested that analysis of CID and their first arrivals can provide information on the position and on the structure of seismic fault ruptured in earthquake. This method is known as ionospheric imagery of seismic source. However, so far, this method has been only applied to giant earthquakes (Mw ≥ 9.0). In this work, for the first time, we apply the ionospheric imagery for smaller earthquakes on the example of the M7.4 Sanriku‐oki earthquake that occurred on 9 March 2011 in Japan. Our results show that this method is applicable to smaller earthquakes, and despite some difficulties, it can indicate the position of coseismic uplift ~8 min after the earthquake. The uplift generates CID but also tsunamis. Therefore, our method can be used as independent or complementary one for near–real‐time tsunami warning systems. By applying the method of seismo‐ionospheric imagery, we show the location of the seismic source for the Mw7.4 2011 Sanriku‐oki earthquakeWe discuss possibilities, difficulties, and challenges of the method of ionospheric imagery of seismic sourceSimultaneous use of the ray‐tracing technique and GNSS observations can be useful to resolve some difficulties of the ionospheric imagery

The variability in obtaining estimates of tsunami inundation and runup on a near‐real‐time tsunami hazard assessment setting is evaluated. To this end, 19 different source models of the Maule Earthquake were considered as if they represented the best available knowledge an early tsunami warning system could consider. Results show that large variability can be observed in both coseismic deformation and tsunami variables such as inundated area and maximum runup. This suggests that using single source model solutions might not be appropriate unless categorical thresholds are used. Nevertheless, the tsunami forecast obtained from aggregating all source models is in good agreement with observed quantities, suggesting that the development of seismic source inversion techniques in a Bayesian framework or generating stochastic finite fault models from a reference inversion solution could be a viable way of dealing with epistemic uncertainties in the framework of nearly‐real‐time tsunami hazard mapping. Owing to recent advancements in rapid seismic source characterization and tsunami simulation, nearly‐real‐time tsunami hazard forecasts in the framework of tsunami early warning systems are starting to be within reach. However, in this study we bring a note of caution regarding its future operational implementation since the level of uncertainty associated to a single rupture inversion is high and thus calls for the use of multiple realizations of seismic inversions to forecast inundation maps and assess their uncertainty bounds. We provide an empirical assessment of the variability of finite fault inversions and its consequences for rapid tsunami hazard mappingThe variability in modeled tsunami maximum amplitudes, runups, and inundated 16 areas is of the same order as their median values evidencing a high inherent uncertaintyCombining the available knowledge to build a distribution of the tsunami source allows to obtain sound predictions of tsunami variables

The development of reliable systems for monitoring injected CO2is essential in carbon capture and storage projects. We applied time‐lapse surface wave analysis to measure temporal variations of the shallow subsurface among 11 periods (0.2–21.6 days) of continuous seismic data acquired from 2014 to 2016 at the Aquistore CO2storage site in Canada. We focused on monitoring environmental influences on shallow seismic velocity, which are unrelated to CO2injection into deep reservoirs. A continuous, controlled seismic source system called Accurately Controlled Routinely Operated Signal System was used to enhance the temporal resolution and source repeatability. Observed phase velocities were clearly higher in winter than in warmer seasons. The seasonal variation could be reproduced by an increase in the shallow Swave velocity during winter associated with the greater extent of freezing of partially saturated rock. We also observed gradual increases or decreases in phase velocities as the seasons changed, which could be related to gradual freezing or melting of ice. Our monitoring system thus could be effective for monitoring temporal variations of the shallow subsurface associated with the degree of freezing. Furthermore, the high temporal stability of our monitoring approach in warm seasons may make it possible to immediately identify CO2leakage in the shallow subsurface. We monitored the shallow subsurface at the Aquistore CO2storage site using the ACROSS source systemWe estimated near‐surface Swave velocity variations possibly related to changes in the degree of freezing of partially saturated rockHigh temporal stability of our monitoring approach may make it possible to immediately identify CO2leakage in the shallow subsurface

The detection of deformation in Global Navigation Satellite System (GNSS) time series associated with (a)seismic events down to a low magnitude is still a challenging issue. The presence of a considerable amount of noise in the data makes it difficult to reveal patterns of small ground deformation. Traditional analyses and methodologies are able to effectively retrieve the deformation associated with medium to large magnitude events. However, the automatic detection and characterization of such events is still a complex task, because traditionally employed methods often separate the time series analysis from the source characterization. Here we propose a first end‐to‐end framework to characterize seismic sources using geodetic data by means of deep learning, which can be an efficient alternative to the traditional workflow, possibly overcoming its performance. We exploit three different geodetic data representations in order to leverage the intrinsic spatio‐temporal structure of the GNSS noise and the target signal associated with (slow) earthquake deformation. We employ time series, images, and image time series to account for the temporal, spatial, and spatio‐temporal domain, respectively. Thereafter, we design and develop a specific deep learning model for each dataset. We analyze the performance of the tested models both on synthetic and real data from North Japan, showing that image time series of geodetic deformation can be an effective data representation to embed the spatio‐temporal evolution, with the associated deep learning method outperforming the other two. Therefore, jointly accounting for the spatial and temporal evolution may be the key to effectively detect and characterize fast or slow earthquakes. The continuous monitoring of ground displacement with Global Navigation Satellite System allowed, at the beginning of the 2000s, the discovery of slow earthquakes—a transient slow slippage of tectonic faults that releases stress without generating seismic waves. Nevertheless, the detection of small events is still a challenge, because they are hidden in the noise. Most of the methods which are traditionally employed are able to extract the deformation down to a certain signal‐to‐noise level. However, one can ask if deep learning can be a more efficient and powerful alternative. To this end, we address the problem by using deep learning, as it stands as a powerful way to automatize and possibly overcome traditional methods. We use and compare three data representations, that is time series, images, and image time series of deformation, which account for the temporal, spatial, and spatio‐temporal variability, respectively. We train our methods on synthetic data, since real datasets are still not enough to be effectively employed with deep learning, and we test on synthetic and real data as well, claiming that image time series and its associated deep learning model may be more effective toward the study of the slow deformation. We develop deep learning approaches on synthetics mimicking the spatio‐temporal structure of static deformation and realistic Global Navigation Satellite System (GNSS) noiseWe design three deep learning models and we train and test them against three GNSS data representationsTransformers and image time series of deformation can effectively characterize small deformation patterns associated with the seismic source We develop deep learning approaches on synthetics mimicking the spatio‐temporal structure of static deformation and realistic Global Navigation Satellite System (GNSS) noise We design three deep learning models and we train and test them against three GNSS data representations Transformers and image time series of deformation can effectively characterize small deformation patterns associated with the seismic source

The potential for devastating earthquakes in the Himalayan orogeny has long been recognized. The 2015 MW7.8 Gorkha, Nepal earthquake has heightened the likelihood that major earthquakes will occur along this orogenic belt in the future. Reliable seismic hazard assessment is a critical element in development of policy for seismic hazard mitigation and risk reduction. In this study, we conduct probabilistic seismic hazard assessment using three different seismogenic source models (smoothed gridded, linear, and areal sources) based on the complicated tectonics of the study area. Two sets of ground motion prediction equations are combined in a standard logic tree by taking into account the epistemic uncertainties in hazard estimation. Long‐term slip rates and paleoseismic records are also incorporated in the linear source model. Peak ground acceleration and spectral acceleration at 0.2 s and 1.0 s for 2% and 10% probabilities of exceedance in 50 years are estimated. The resulting maps show significant spatial variation in seismic hazard levels. The region of the Lesser Himalaya is found to have high seismic hazard potential. Along the Main Himalayan Thrust from east to west beneath the Main Central Thrust, large earthquakes have occurred regularly in history; hazard values in this region are found to be higher than those shown on existing hazard maps. In essence, the combination of long span earthquake catalogs and multiple seismogenic source models gives improved seismic hazard constraints in Nepal.

Seismic imaging during piling may reduce construction costs in sensitive areas, and combined with technology to look ahead of the pile tip, the risk of stopping the piling before reaching the bedrock may be reduced. Seismic images obtained during piling can give vital information about the subsurface structure in the vicinity of the pile. In this paper, we discuss how to produce seismic images as a part of the piling procedure without using any external seismic sources. Using surface and borehole synthetic data examples, we show that it is possible to use pressure (P) waves emitted from the pile tip for imaging purposes. A pilot field study was carried out to test the feasibility of this method. The field study revealed that high‐frequency pressure waves are emitted from the pile tip. However, they are masked by strong surface and shear waves, especially at the nearer offsets. The data processing approach allowed us to achieve reasonable signal‐to‐noise ratio for pressure waves at the further offsets, while the signal was not as well recovered at the nearer offsets. This study demonstrates that it is possible to acquire fair‐quality signals without using any seismic source other than the impact on the pile itself. More field data are needed and some acquisition procedures need to be optimized before the method can be applied for practical imaging.

We present the updated seismic source characterization (SSC) for the 2014 update of the National Seismic Hazard Model (NSHM) for the conterminous United States. Construction of the seismic source models employs the methodology that was developed for the 1996 NSHM but includes new and updated data, data types, source models, and source parameters that reflect the current state of knowledge of earthquake occurrence and state of practice for seismic hazard analyses. We review the SSC parameterization and describe the methods used to estimate earthquake rates, magnitudes, locations, and geometries for all seismic source models, with an emphasis on new source model components. We highlight the effects that two new model components—incorporation of slip rates from combined geodetic-geologic inversions and the incorporation of adaptively smoothed seismicity models—have on probabilistic ground motions, because these sources span multiple regions of the conterminous United States and provide important additional epistemic uncertainty for the 2014 NSHM.

Influence of different models and assumptions with respect to seismic source modelling and magnitude distribution on seismic hazard results is examined, taking Jordan as a case study. Four alternative models, which are based on different combinations of seismic source models and magnitude-recurrence relationships, are considered. Seismic hazard curves obtained at four different sites in Jordan according to these four models are compared. In order to display the magnitude of spatial variation of peak ground acceleration (PGA) values obtained from these models, difference maps for return periods of 475 and 2475 years are constructed. Logic tree method is applied to aggregate the results calculated based on different models and assumptions. Then, best estimate seismic hazard maps for PGA and spectral acceleration at 0.2 and 1.0 s corresponding to return periods of 475 and 2475 years are plotted.

The sources of ambient seismic noise are extensively studied both to better understand their influence on ambient noise tomography and related techniques, and to infer constraints on their excitation mechanisms. Here we develop a gradient‐based inversion method to infer the space‐dependent and time‐varying source power spectral density of the Earth's hum from cross correlations of continuous seismic data. The precomputation of wavefields using spectral elements allows us to account for both finite‐frequency sensitivity and for three‐dimensional Earth structure. Although similar methods have been proposed previously, they have not yet been applied to data to the best of our knowledge. We apply this method to image the seasonally varying sources of Earth's hum during North and South Hemisphere winter. The resulting models suggest that hum sources are localized, persistent features that occur at Pacific coasts or shelves and in the North Atlantic during North Hemisphere winter, as well as South Pacific coasts and several distinct locations in the Southern Ocean in South Hemisphere winter. The contribution of pelagic sources from the central North Pacific cannot be constrained. Besides improving the accuracy of noise source locations through the incorporation of finite‐frequency effects and 3‐D Earth structure, this method may be used in future cross‐correlation waveform inversion studies to provide initial source models and source model updates. The Earth's hum is a low‐frequency, background seismic signal excited by the interplay of long periodic ocean waves with the Earth's solid crust. We present the first real‐data application of a new localization technique for the sources of hum, which can be used for similar ambient sources in future studies. This technique can account for realistic wave propagation effects of seismic waves in the Earth. This is achieved by storing simulated responses of the Earth to impulsive sources, so‐called Green's functions, in order to use them during localization. Our results confirm previous findings that the sources of the Earth's hum vary seasonally, occurring at Pacific coasts or shelves and in the North Atlantic during North Hemisphere winter, as well as South Pacific coasts and several distinct locations in the Southern Ocean in South Hemisphere winter. Our results also appear to indicate that hum sources are rather localized in space. We apply gradient‐based ambient noise source inversion to long periodic “hum” cross correlationsThe inversion procedure fully accounts for heterogeneous Earth structure and finite‐frequency sensitivityHum sources appear persistent and narrowly localized at coasts or shelves and high bathymetry

Earthquake source properties such as seismic moment and stress drop are routinely estimated from far‐field body wave amplitude spectra. Some quantitative but model‐dependent relations have been established between seismic spectra and source parameters. However, large variability is seen in the parameter estimates, and it is uncertain how the variability is partitioned among real variability in the source parameters, observational error, and modeling error due to complexity of earthquake behaviors. Earthquake models with dynamic weakening have been found to exhibit two different modes of rupture: expanding‐crack and self‐healing pulse modes. Four representative models are generated to model the transition from crack‐like to pulse‐like. Pulse‐like rupture leads to development of a second corner frequency, and the intermediate spectral slope is approximately 2 in most cases. The focal‐sphere‐averaged lower Pand Swave corner frequencies are systematically higher for pulse‐like models than crack models of comparable rupture velocity. The slip‐weighted stress drop ΔσEexceeds the moment‐based stress drop ΔσMfor pulse‐like ruptures, with the ratio ranging from about 1.3 to 1.65, while they are equal for the crack‐like case. The variations in rupture mode introduce variability of the order of a factor of 2 in standard (i.e., crack model‐based) spectral estimates of stress drop. The transition from arresting‐ to growing‐pulse rupture is accompanied by a large (factor of ∼1.6) increase in the radiation ratio. Thus, variations in rupture mode may account for the portion of the scatter in observational spectral estimates of source parameters. Corner frequency changes in crack‐like and pulse‐like modelsStress drop estimate varies with source characteristicsDouble spectral decay slopes

The accurate determination of stress drop, seismic efficiency, and how source parameters scale with earthquake size is an important issue for seismic hazard assessment of induced seismicity. We propose an improved nonparametric, data-driven strategy suitable for monitoring induced seismicity, which combines the generalized inversion technique together with genetic algorithms. In the first step of the analysis the generalized inversion technique allows for an effective correction of waveforms for attenuation and site contributions. Then, the retrieved source spectra are inverted by a nonlinear sensitivity-driven inversion scheme that allows accurate estimation of source parameters. We therefore investigate the earthquake source characteristics of 633 induced earthquakes (Mw2–3.8) recorded at The Geysers geothermal field (California) by a dense seismic network (i.e., 32 stations, more than 17.000 velocity records). We find a nonself-similar behavior, empirical source spectra that require an ??source model with ?> 2 to be well fit and small radiation efficiency ?SW. All these findings suggest different dynamic rupture processes for smaller and larger earthquakes and that the proportion of high-frequency energy radiation and the amount of energy required to overcome the friction or for the creation of new fractures surface changes with earthquake size. Furthermore, we observe also two distinct families of events with peculiar source parameters that in one case suggests the reactivation of deep structures linked to the regional tectonics, while in the other supports the idea of an important role of steeply dipping faults in the fluid pressure diffusion. A novel data-driven strategy for monitoring induced seismicity that combines generalized inversion and genetic algorithm is presentedStudying the seismicity at The Geysers geothermal field, we found different dynamic rupture processes for smaller and larger earthquakesTwo groups of events suggest the reactivation of deep structures and an important role of steeply dipping fault in fluid pressure diffusion