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Mars’s seismic activity and noise have been monitored since January 2019 by the seismometer of the InSight (Interior Exploration using Seismic Investigations, Geodesy and Heat Transport) lander. At night, Mars is extremely quiet; seismic noise is about 500 times lower than Earth’s microseismic noise at periods between 4 s and 30 s. The recorded seismic noise increases during the day due to ground deformations induced by convective atmospheric vortices and ground-transferred wind-generated lander noise. Here we constrain properties of the crust beneath InSight, using signals from atmospheric vortices and from the hammering of InSight’s Heat Flow and Physical Properties (HP3) instrument, as well as the three largest Marsquakes detected as of September 2019. From receiver function analysis, we infer that the uppermost 8–11 km of the crust is highly altered and/or fractured. We measure the crustal diffusivity and intrinsic attenuation using multiscattering analysis and find that seismic attenuation is about three times larger than on the Moon, which suggests that the crust contains small amounts of volatiles.The crust beneath the InSight lander on Mars is altered or fractured to 8–11 km depth and may bear volatiles, according to an analysis of seismic noise and wave scattering recorded by InSight’s seismometer.

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

We introduce Devito, a new domain-specific language for implementing high-performance finite-difference partial differential equation solvers. The motivating application is exploration seismology for which methods such as full-waveform inversion and reverse-time migration are used to invert terabytes of seismic data to create images of the Earth's subsurface. Even using modern supercomputers, it can take weeks to process a single seismic survey and create a useful subsurface image. The computational cost is dominated by the numerical solution of wave equations and their corresponding adjoints. Therefore, a great deal of effort is invested in aggressively optimizing the performance of these wave-equation propagators for different computer architectures. Additionally, the actual set of partial differential equations being solved and their numerical discretization is under constant innovation as increasingly realistic representations of the physics are developed, further ratcheting up the cost of practical solvers. By embedding a domain-specific language within Python and making heavy use of SymPy, a symbolic mathematics library, we make it possible to develop finite-difference simulators quickly using a syntax that strongly resembles the mathematics. The Devito compiler reads this code and applies a wide range of analysis to generate highly optimized and parallel code. This approach can reduce the development time of a verified and optimized solver from months to days.

Continental breakup represents the successful process of rifting and thinning of the continental lithosphere, leading to plate rupture and initiation of oceanic crust formation. Magmatism during breakup seems to follow a path of either excessive, transient magmatism (magma-rich margins) or of igneous starvation (magma-poor margins). The latter type is characterized by extreme continental lithospheric extension and mantle exhumation prior to igneous oceanic crust formation. Discovery of magma-poor margins has raised fundamental questions about the onset of ocean-floor type magmatism, and has guided interpretation of seismic data across many rifted margins, including the highly extended northern South China Sea margin. Here we report International Ocean Discovery Program drilling data from the northern South China Sea margin, testing the magma-poor margin model outside the North Atlantic. Contrary to expectations, results show initiation of Mid-Ocean Ridge basalt type magmatism during breakup, with a narrow and rapid transition into igneous oceanic crust. Coring and seismic data suggest that fast lithospheric extension without mantle exhumation generated a margin structure between the two endmembers. Asthenospheric upwelling yielding Mid-Ocean Ridge basalt-type magmatism from normal-temperature mantle during final breakup is interpreted to reflect rapid rifting within thin pre-rift lithosphere.

This paper reviews the application of the algorithm particle swarm optimization (PSO) to perform stochastic inverse modeling of geophysical data. The main features of PSO are summarized, and the most important contributions in several geophysical fields are analyzed. The aim is to indicate the fundamental steps of the evolution of PSO methodologies that have been adopted to model the Earth’s subsurface and then to undertake a critical evaluation of their benefits and limitations. Original works have been selected from the existing geophysical literature to illustrate successful PSO applied to the interpretation of electromagnetic (magnetotelluric and time-domain) data, gravimetric and magnetic data, self-potential, direct current and seismic data. These case studies are critically described and compared. In addition, joint optimization of multiple geophysical data sets by means of multi-objective PSO is presented to highlight the advantage of using a single solver that deploys Pareto optimality to handle different data sets without conflicting solutions. Finally, we propose best practices for the implementation of a customized algorithm from scratch to perform stochastic inverse modeling of any kind of geophysical data sets for the benefit of PSO practitioners or inexperienced researchers.

In recent years, supervised-deep-learning methods have shown some advantages over conventional methods in seismic data denoising, such as higher signal-to-noise ratio after denoising, complete separation of signals and noise in shared frequency bands and intelligent denoising without artificial parameter tuning. However, the lack of real noise data matched with raw seismic data has greatly limited its further application. In this paper, we take the surface seismic shot gather as an example to explore the corresponding solutions and propose a novel supervised-deep-learning method with weak dependence on real noise data based on the data augmentation of a generative adversarial network. We utilize the generative adversarial network to augment the pre-arrival noise data acquired from the shot gather itself, thereby obtaining a large amount of synthetic noise data whose probability distribution is extremely similar to that of the real noise in shot gather; the augmented synthetic noise data and sufficient synthetic signal data obtained by forward modeling together form the augmented training dataset. Meanwhile, the dilated convolution and gradual denoising strategy are adopted to construct the basic architecture of denoising convolution neural network. Finally, the above augmented dataset is used to train the network, so as to establish a nonlinear and complex mapping relationship between raw seismic data and desired signals. Both synthetic and real experiments demonstrate that our method can realize the intelligent denoising of different common-shot-point records in shot gather with the help of limited pre-arrival noise data.Article HighlightsWe introduce the data augmentation strategy into the field of deep-learning-based seismic denoising, thereby alleviating the dependence of supervised-deep-learning methods on real noise dataWe propose a novel denoising network architecture with strong recovery ability for weak desired signals by using the gradual denoising strategy and dilated convolutionThe augmented synthetic noise data can meet the requirement of supervised-deep-learning methods on the quantity and authenticity of training data, so this data augmentation strategy by using the Generative Adversarial Net (GAN) is a solution to the lack of real noise data

The water cycle at subduction zones remains poorly understood, although subduction is the only mechanism for water transport deep into Earth. Previous estimates of water flux 1 – 3 exhibit large variations in the amount of water that is subducted deeper than 100 kilometres. The main source of uncertainty in these calculations is the initial water content of the subducting uppermost mantle. Previous active-source seismic studies suggest that the subducting slab may be pervasively hydrated in the plate-bending region near the oceanic trench 4 – 7 . However, these studies do not constrain the depth extent of hydration and most investigate young incoming plates, leaving subduction-zone water budgets for old subducting plates uncertain. Here we present seismic images of the crust and uppermost mantle around the central Mariana trench derived from Rayleigh-wave analysis of broadband ocean-bottom seismic data. These images show that the low mantle velocities that result from mantle hydration extend roughly 24 kilometres beneath the Moho discontinuity. Combined with estimates of subducting crustal water, these results indicate that at least 4.3 times more water subducts than previously calculated for this region 3 . If other old, cold subducting slabs contain correspondingly thick layers of hydrous mantle, as suggested by the similarity of incoming plate faulting across old, cold subducting slabs, then estimates of the global water flux into the mantle at depths greater than 100 kilometres must be increased by a factor of about three compared to previous estimates 3 . Because a long-term net influx of water to the deep interior of Earth is inconsistent with the geological record 8 , estimates of water expelled at volcanic arcs and backarc basins probably also need to be revised upwards 9 . Seismic images of Earth’s crust and uppermost mantle around the Mariana trench show widespread serpentinization, suggesting that much more water is subducted than previously thought.

The left‐lateral Altyn Tagh Fault (ATF) system is the northern boundary of the Tibetan Plateau resulted from the India–Eurasia continental collision. How intracontinental deformation across the central ATF responds to the distal collision remains elusive, primarily due to unclear crustal structure. We obtained detailed crustal structure across the central ATF using receiver functions recorded by ∼NW–SE oriented linear dense array. The images reveal the Tarim lower crust is underthrusting beneath the Tibetan Plateau and reaches to a maximum depth of ∼75 km and undergoing partial eclogitization. The two south‐dipping interfaces imaged beneath the Altyn Tagh Range (ATR) represent the thrusting Northern Altyn Fault and its branch fault. Oblique convergent forces extruded upper crustal materials along the thrust faults, creating the pop‐up structure of ATR, supported by low Vp/Vs ratios. Our balanced cross‐section for the Moho suggests intracontinental deformation in the ATR has accelerated since the late Miocene. Plain Language Summary The Altyn Tagh Fault (ATF), serving as the northern boundary of the Tibetan Plateau, demarcates the Tarim Basin from the Qaidam Basin. Understanding how intracontinental deformation across the boundary region would better inform the uplift and expansion of the plateau. This study reveals the fine crustal structure by analyzing seismic data from a ∼NW–SE oriented linear dense array across the central ATF. Combined with fault slip rates, we propose that the Tarim lower crust is underthrusting beneath the Tibetan Plateau, leading to the extrusion of upper crustal materials and the rapid uplift of the Altyn Tagh Range since the late Miocene, which provides insight into the lateral growth of the plateau. Key Points Detailed crustal structure beneath the central Altyn Tagh Fault was imaged by receiver functions of a dense 2‐D seismic array The Tarim lower crust is underthrusting to ∼75 km depth beneath the Tibetan Plateau The Altyn Tagh range was uplifted rapidly since late Miocene through the thickening of the upper crust

Signal processing techniques play an important role in seismic data analysis. Variational mode decomposition (VMD), as a powerful signal processing method, has been extensively applied in seismic signal processing. A large number of papers on the application of VMD in seismic data analysis have appeared in various journals, conference proceedings, and technical communications. The paper aims to investigate and summarize the recent advancements of VMD and its application in seismic data analysis and give a comprehensive reference for scholars that may be interested in this topic so that researchers can select a more in-depth research direction. Firstly, the VMD principle is briefly introduced, and the advantage and limitations of this approach are illustrated in detail. Secondly, recent applications of the VMD in seismic data analysis are summarized in terms of specific scenarios, such as seismic time–frequency analysis (TFA), seismic denoising, and other applications. Finally, the key problems of VMD in seismic data analysis are discussed, and the potential research directions are listed. It is expected that the review would be constructive to the basic understanding of the VMD concept for beginners and insightful exploration of VMD’s applications in seismic data analysis for advanced researchers.Article HighlightsSeismic data analysis plays an important role in extracting valuable information from seismic recordsThis paper surveys the VMD and its applications in the field of seismic data analysis in a comprehensive wayPromising research prospects of VMD in seismic data analysis are proposed

Earthquakes are supershear when their rupture speed is faster than that of the seismic shear waves produced. These events are rare, but they can be highly destructive owing to the associated strong ground shaking, and understanding why they occur may provide insights into fault mechanics. Only a few supershear earthquakes have been reported previously, most of which were continental. Here we perform a systematic global search for supershear earthquakes by analysing seismic data from all large ( M w  ≥ 6.7) shallow strike-slip earthquakes occurring between 2000 and 2020. Based on the rupture speeds determined by slowness-enhanced back-projection, and the identification of Rayleigh Mach waves, we identify four oceanic earthquakes consistent with supershear events. We find that at least 14.0% of large earthquakes during the study period were supershear, with oceanic events occurring as frequently as continental ones. We further observe a wider range of stable rupture speeds during supershear events than predicted by two-dimensional fracture mechanics theory, which we attribute to the presence of fault damage zones or slip obliqueness. The transition to and propagation of supershear earthquakes may be promoted in oceanic settings due to the thicker crustal seismogenic zones and the material contrast at oceanic–continental boundaries. Supershear earthquakes occur more frequently than previously thought, as suggested by the identification of four oceanic events from a global analysis of large shallow strike-slip earthquakes.