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This article proposes a matrix auto-transformer switched-capacitor dc–dc converter to achieve a high voltage conversion ratio, high efficiency, and high power density for 48-V data-center applications. On the high-voltage side, the proposed converter can fully leverage the benefits of high-performance low voltage stress devices similar to the multilevel modular switched-capacitor converter. Compared with the traditional isolated LLC converter with a matrix transformer, the proposed solution utilized a matrix autotransformer concept with merged primary and secondary side windings, thus leading to reduced transformer winding loss. The resonant inductor could be integrated into the transformer similar to the LLC converter. Because of the matrix autotransformer design, it can achieve a current doubler rectifier on the low voltage side. For less than 8-V low output voltage application, the current doubler rectifier design can fully utilize the best figure-of-merit 25-V device, which is more efficient than the full-bridge rectifier solution using two 25-V devices during the operation. All the devices can achieve zero voltage switching or zero current switching and can be naturally clamped without additional clamping circuits. A 500-W 48-V to 6-V dc–dc converter hardware prototype has been developed with optimized device selection and integrated matrix autotransformer design. Both simulation and experiment results have been provided to validate the features and benefits of the proposed converter. The maximum efficiency of the proposed converter can reach 98.33%.

Attitude sensors are critical elements in the field of automation control. In this study, we propose and demonstrate a whisker-inspired fiber attitude sensor (WI-FAS) capable of simultaneously measuring pitching and rolling angles. A photopolymerized whisker grows out of the central core of a seven-core fiber (SCF) and holds a solid pendulum. Six parallel Fabry-Perot interferometers formed between the SCF. The pendulum transforms variations in cavity length caused by pitching and rolling into the evolution of the interference signal. A fan-in/fan-out device is used to implement space division multiplexing of the SCF, enabling the proposed sensor to judge direction. We also develop a mathematical expression to explain the association between the pitching and rolling angles and the WI-FAS output signals. Mechanical simulations are carried out to investigate the primary factors that affect the sensitivity. With optimum settings, the proposed WI-FAS achieves continuous and simultaneous monitoring of pitching and rolling with a sensitivity of greater than 0.13 nm/$^\circ$ in the angle range of $-180 ^\circ$ to $+180 ^\circ$. The suggested WI-FAS exhibits the advantages of compact size, high sensitivity, and has the ability to monitor pitching and rolling simultaneously. Thus it is a competitive candidate in the field of attitude sensing.

To realize application of functional material Photo-Thermo-Refractive (PTR) glass for integrated photonic device production, PTR glass material is fabricated and employed as matrix material for planar optical waveguide manufacturing. Buried optical waveguide, with a core dimension of $8.7\,\,\mu \text{m}\,\,\times 9.0\,\,\mu \text{m}$ (width by height), and its front edge located at approximately $12.5~\mu \text{m}$ underneath the glass substrate surface, is obtained by using thermal ion exchange (TIE) and subsequent field-assisted ion migration (FAIM). Transmission loss of these waveguides can be reduced to a value as low as 0.08 dB/cm at the operating wavelength of 1550 nm, and the coupling loss to standard single-mode-fiber is measured to be 0.47 dB/facet, demonstrating the feasibility of low-loss integrated photonic devices fabrication on PTR glass materials by ion exchange technology.

In this article, we present and verify the mode dependence of refractive index (RI) sensitivity of long-period fiber grating induced by periodically embedded multimode fiber (PEM-LPFG). The PEM-LPFG is first fabricated by periodically embedding multimode fiber (MMF) into the single mode fiber (SMF). The resonant mode is translated by reducing the cladding. Numerical simulations based on beam propagation method (BPM) are applied to calculate the transmission spectrum, mode distribution, and RI sensitivities of the PEM-LPFG. The cladding diameter after etching is determined to be 89 and 68 $\mu \text{m}$ , which corresponds to the mode translated from LP012 to LP07 and LP05, respectively. The experimental results indicate that the maximum RI sensitivity of 1322.26 nm/RIU in the range of 1.333–1.437 RIU is achieved when the resonant mode is LP07, which is four times as large as the original one (325.58 nm/RIU @ LP012 mode). PEM-LPFG working at LP05 mode exhibits a lower sensitivity of 844.72 nm/RIU. All of the experimental results are in good agreement with the simulation. The RI sensitivity optimization of PEM-LPFG is able to realize by mode converting. Furthermore, the temperature responses are measured to be 13 pm/°C and 4 pm/°C. This work provides a theoretical reference and experimental evidence for the dependence of the PEM-LPFG RI sensitivity on the resonant mode for the first time.

In this article, an ultralong period fiber grating (ULPFG) based on periodically embedding multimode fiber into single mode fiber (PEM-ULPFG) is proposed to simultaneously measure the liquid refractive index (RI) and temperature with high sensitivity. Five resonant dips belong to different cladding modes, and diffraction mode orders appear in the transmission spectrum. The responses of the dips to RI and temperature are different. Therefore, high sensitivity and dual-parameter measurement are realized. Numerical simulation based on the beam propagation method (BPM) is also carried out to design and optimize the parameters of the device. Experimental results show that the maximum RI sensitivity of −1230 nm/RIU in the range of 1.4021–1.4383 RIU is achieved by tracing the target dip (dip 4). The temperature response of dip 4 is also characterized to be as low as 52.6 pm/°C. Another dip (dip 5) is used for measuring temperature with the temperature sensitivity of 92.3 pm/°C. Different responses to the external changes of the dips obtained by the PEM-ULPFG make the proposed sensor competitive in the field of high sensitivity and dual-parameter sensing.

The array‐based frequency‐Bessel transform method has been demonstrated to effectively extract dispersion curves of higher‐mode surface waves from the empirical Green's functions (EGFs) of displacement fields reconstructed by ambient noise interferometry. Distributed acoustic sensing (DAS), a novel dense array observation technique, has been widely implemented in surface wave imaging to estimate subsurface velocity structure in practice. However, there is still no clear understanding in theory about how to accurately extract surface‐wave dispersion curves directly from DAS strain (or strain rate) data. To address this, we extend the frequency‐Bessel transform method by deriving Green’s functions (GFs) for horizontal strain fields, making it applicable to DAS data. First, we test its performance using synthetic GFs and verify the correctness of extracted dispersion spectrograms with theoretical results. Then, we apply it to three field DAS ambient‐noise data sets, two recorded on land and one in the seabed. The reliability and advantages of the method are confirmed by comparing results with the widely used phase shift method. The results demonstrate that our extended frequency‐Bessel transform method is reliable and can provide more abundant and higher‐quality dispersion information of surface waves. Moreover, our method is also adaptable for active‐source DAS data with simple modifications to the derived transform formulas. We also find that the gauge length in the DAS system significantly impacts the polarity and value of extracted dispersion energy. Overall, our study provides a theoretical framework and practical tool for multimodal surface wave dispersion measurement using DAS data. Ambient noise surface wave imaging is one of the most widely used methods for estimating the Earth's internal shear wave velocity structure, exploiting the dispersion characteristics of surface waves. The array‐based frequency‐Bessel transform method has been proven effective in extracting dispersion curves of higher‐mode surface waves from empirical Green's functions (EGFs) retrieved via ambient noise interferometry. Distributed acoustic sensing (DAS), which is a novel dense array observation technique, has become widely adopted in practical surface wave imaging. Nevertheless, there remains a theoretical gap in our understanding of how to accurately extract surface‐wave dispersion curves directly from DAS strain (or strain rate) data. To bridge this gap, starting from the perspective of strain field theory, we propose an extension of the frequency‐Bessel transform method, which can account for Green's functions of horizontal strain fields and the reconstructed EGFs from DAS ambient noise data. Both synthetic tests and applications to field DAS data demonstrate that our proposed frequency‐Bessel transform method can be confidently and effectively utilized for multimodal dispersion measurement of surface waves derived from DAS observation data. This work can offer a theoretical basis and practical tool for DAS‐based surface wave imaging. We derive Green’s functions for horizontal strain fields, extending the frequency‐Bessel transform method for applications in distributed acoustic sensing (DAS) dataThe extended frequency‐Bessel transform method reliably measures multimodal surface‐wave dispersion in both synthetic and field DAS recordsThe method excels in extracting high‐quality multimodal dispersion information from both active and passive DAS data for surface waves We derive Green’s functions for horizontal strain fields, extending the frequency‐Bessel transform method for applications in distributed acoustic sensing (DAS) data The extended frequency‐Bessel transform method reliably measures multimodal surface‐wave dispersion in both synthetic and field DAS records The method excels in extracting high‐quality multimodal dispersion information from both active and passive DAS data for surface waves

Ambient Noise Imaging (ANI) of subsurface structures relies on seismic interferometry of diffuse seismic wavefields. However, the lack of effective methods to quantify and identify highly diffuse waves hampers applications of ANI, particularly in evaluating seismic attenuation and monitoring structural changes with high temporal resolution. Conventional ANI approaches require data normalization, which effectively suppresses the non‐diffuse component with large amplitude but also results in significant loss of amplitude and phase information in the continuous seismic records. In this study, we propose a frequency domain method to quantitatively evaluate the degree of diffuseness of seismic wavefields by analyzing their statistical characteristics of modal amplitudes for stationarity and randomness. Tests on synthetic waveform and field nodal records show that the proposed method can effectively distinguish between diffuse and non‐diffuse waveforms for either single‐ or three‐component data. As an application, we identify a 60‐s‐long diffuse coda of a local M 2.2 earthquake recorded by a dense nodal array on the San Jacinto Fault Zone, and successfully extract high‐quality dispersion curve and Q‐value without performing data normalization. These results are consistent with those obtained by conventional methods that assess the correlation between coherency and the Green's function, and by modeling ballistic waves generated by road traffic. Our proposed method can advance the imaging of subsurface velocity and attenuation structures as well as monitoring temporal changes for scientific studies and engineering applications. Earthquakes, explosions, and traffic events can generate seismic waves that travel through the Earth. As these direct waves encounter heterogeneous earth interior, they scatter and change direction, leading to more diffusive propagation. Fully diffuse waves can be used to image the subsurface structures. In this study, we develop a reliable and efficient method to measure how diffuse a seismic wavefield is at different frequencies. Tests on synthetic and field data show that the developed method can reliably differentiate between diffuse and non‐diffuse waves. This method can improve our ability to use spread out wavefields for imaging and monitoring the Earth's interior. We present an efficient methodology to quantify seismic wavefield diffuseness based on stationarity and randomness in the frequency domainThe method avoids waveform normalization and extracts reliable empirical Green's functions from interferometry using diffuse waveformsWe validate the method by extracting dispersion curves and Q‐values from the 60‐s‐long diffuse coda of a local M 2.2 earthquake We present an efficient methodology to quantify seismic wavefield diffuseness based on stationarity and randomness in the frequency domain The method avoids waveform normalization and extracts reliable empirical Green's functions from interferometry using diffuse waveforms We validate the method by extracting dispersion curves and Q‐values from the 60‐s‐long diffuse coda of a local M 2.2 earthquake

Analysis of continuous seismic waveforms from a temporary deployment at Sage Brush Flats on the San Jacinto fault reveals earthquake‐ and tremor‐like signals generated by the interaction of wind with obstacles above the surface. Tremor‐like waveforms are present at the site during wind velocities above 2 m/s, which occur for 70% of the deployment duration. The response to the wind has significant spatial variability with highest ground motions near large surface objects. The wind‐related signals show ground velocities that exceed the average ground motions of M1.0–1.5 earthquakes for 6–31% of the day. Waveform spectra indicate a modulation of amplitude that correlates with wind velocity and distance from local structures. Earthquake‐like signals are found to originate from local structures and vegetation, and are modified on length scales of tens of meters. Transient signals originating beyond the study area are also observed with amplitudes greater than some microseismic events. The wind‐related ground motions contribute to local high‐frequency seismic noise. Some of these signals may be associated with small failures of the subsurface material. During elevated wind conditions a borehole seismometer at a depth of 148 m shows increased energy in the 1–8‐Hz band that is commonly used for earthquake and tremor detection. The wind‐related earthquake‐ and tremor‐like signals should be accounted for in earthquake detection algorithms due to the similar features in both time and frequency domains. Proper recognition of wind‐related ground motions can contribute to understanding the composition of continuous seismic waveforms and characterize mechanical properties of the shallow crust. Seismic recordings contain information on atmospheric and anthropogenic phenomena which can occur over much larger portions of the daily records than tectonic events. We characterize signals with earthquake‐ and tremor‐like waveforms that are generated as wind gusts interact with objects on the surface and modulate the ambient environmental noise. The wind interaction may produce microfailures in the shallow crust generating high‐frequency energy that contributes to the local seismic noise. The classification of nontectonic signals is becoming increasingly important as earthquake detection algorithms employ machine learning techniques that utilize the data to build a detection model. Properly identifying different classes of signals will provide better detection models as the algorithms continue to improve. Wind gusts above 2‐m/s shaking surface objects produce earthquake‐ and tremor‐like waveforms that vary spatially over tens of metersWind‐related ground motions exceed the PGV of M1.0–1.5 earthquakes for 6–31% of the day and inhibit detection of small earthquakesRecognition of wind‐related signals can increase the performance of advanced algorithms for detecting small earthquakes and tremor

Abstract Punishment widely exists in social and biological systems and it has been proved to be an effective way to promote cooperation in evolutionary game in previous work. In this paper, we introduce a new mechanism of punishment that the punishment fine can be multiplied if there are several punishers decide to punish a defector, which leads to a low payoff of defector and promotes the level of cooperation. This mechanism can simulate group hunt which is common in natural animal world, for example, in biological system, a pack of animal in which every individual cooperates with each other can hunt a prey which is stronger than an individual inside the pack. This phenomenon can be explained by that the ability of punishment is multiplied by individuals cooperating with each other to carry out the punishment. A pack of wolves can make the process of hunt easier than the hunt implemented by a lone wolf, inspired by which we construct this mechanism of punishment to simulate the group punishment. Interestingly, we find that this mechanism can dramatically promotes the level of cooperation when we compare the simulation result with traditional evolutionary public good game. In order to figure out how our new mechanism of punishment promotes the cooperation, we have drawn several figures below to explain the mechanism. Our work simulates a widely existing phenomenon in biological and social systems and reveals some essential principles in the process of evolution. [ABSTRACT FROM AUTHOR]

We image the shallow seismic structure across the Southern San Andreas Fault (SSAF) using signals from freight trains and trucks recorded by a dense nodal array, with a linear component perpendicular to SSAF and two 2D subarrays centered on the Banning Fault and Mission Creek Fault (MCF). Particle motion analysis in the frequency band 2–5 Hz shows that the examined traffic sources can be approximated as moving single‐ or multi‐point sources that primarily induce Rayleigh waves. Using several techniques, we resolve strong lateral variations of Rayleigh wave velocities and Q‐values across the SSAF, including 35% velocity reduction across MCF toward the northeast and strong attenuation around the two fault strands. We further resolve 10% mass density reduction and 45% shear modulus decrease across the MCF. These findings suggest that the MCF is currently the main strand of the SSAF in the area with important implications for seismic hazard assessments. Imaging the internal structure of fault zones is essential for understanding earthquake properties and processes. Here we utilize seismic data generated by trains and trucks in the Coachella valley and recorded by a dense seismic array to image the subsurface structure of two main strands of the Southern San Andreas Fault (SSAF). Several types of analyses allow us to resolve seismic velocities, attenuation coefficients, and mass density across the entire San Andreas Fault zone. The results show a clear contrast in physical properties across the Mission Creek strand of the SSAF, highlighting the presence of a bimaterial fault interface and suggesting that it is the main active strand of SSAF. The research opens up possibilities for using common rail and road traffic signals to derive high resolution imaging results of subsurface seismic properties at other locations. We detect frequent seismic signals from rail and road traffic in a dense array across the southern San Andreas fault zoneWe use the traffic signals to image shallow structural properties across the Banning and Mission Creek fault strandsThe resolved velocity and density contrasts across the Mission Creek fault suggest it is the main active strand of the Southern San Andreas Fault in the area We detect frequent seismic signals from rail and road traffic in a dense array across the southern San Andreas fault zone We use the traffic signals to image shallow structural properties across the Banning and Mission Creek fault strands The resolved velocity and density contrasts across the Mission Creek fault suggest it is the main active strand of the Southern San Andreas Fault in the area

The incidence of autism spectrum disorder (ASD) is increasing year by year in children. The aim of the study was to find possible biomarkers for ASD diagnosis as well as examine MicroRNA (miRNA) signatures and crucial pathways. We conducted a two-stage study to explore potential target genes and functional miRNAs. Peripheral blood samples of children with ASD were enrolled and performed RNA sequencing analysis. The overlapped candidate genes were further screened in combination with differentially expressed genes (DEGs) of GSE77103 datasets. STRING established a protein–protein interaction network comprising DEGs. The hub genes were filtered out using the CytoHubba. Then, we set up a miRNA-mRNA regulatory network. Correlational analyses between hub genes and immune cells associated with ASD were carried out using the CIBERSORT software to assess the diversity of immune cell types in ASD. RNA-sequencing analysis was used to confirm the differential expression of 3 hub genes. Briefly, after blood samples were sequenced interrogating 867 differential genes in our internal screening dataset. After screening GEO databases, 551 DEGs obtained from GSE77103. Fourteen common genes were overlapped through DEGs of GEO datasets and internal screening dataset. Among protein–protein interaction network, 10 hub genes with high degree algorithm were screened out and 3 hub genes of them – ADIPOR1, LGALS3, and GZMB –that were thought to be most associated with the emergence of ASD. Then, we developed a network of miRNA-mRNA regulatory interactions by screening miRNAs (such as hsa-miR-20b-5p, hsa-miR-17-5p, and hsa-miR-216b-5p) that were closely associated to 3 hub genes. Additionally, we discovered 18 different immune cell types associated with ASD using the CIBERSORT algorithm, and we discovered that mononuclear macrophages differed considerably between the 2 groups. Overall, 3 hub genes (ADIPOR1, LGALS3, and GZMB) and 15 candidates miRNAs-target 3 genes regulatory pathways representing potentially novel biomarkers of ASD diseases were revealed. These findings could enhance our knowledge of ASD and offer possible therapeutic targets of ASD patients in the future.

We observe frequent air traffic events in continuous seismic waveforms recorded for about 30 days by 1,108 vertical geophones in a tight array on the San Jacinto fault zone. The waveforms of the air traffic events resemble tremor or collections of small earthquakes. However, time‐frequency analysis reveals clear Doppler effects that can be modeled with basic equations and fitted well with parameters corresponding to airplanes and helicopters. The flying traces can be inverted by fitting the parameters at each station across the entire array. About 31 air traffic events are detected per day in the relatively remote study area, with peak activity from about 8:00 to 18:00 and significantly fewer events between 23:00 and 4:00 local time. The average event duration is about 200 s, so they cover together >7% of the day. To estimate the total time covered by earthquakes, we derive a scaling relation logτ(M) = 0.41M+ 0.89between earthquake duration and magnitudes using data of 266 earthquakes in the magnitude range 0 ≤ M≤ 3. The results indicate that in most places, the duration of air traffic events is likely to exceed considerably the total time covered by earthquakes. We observe frequent air traffic events in continuous seismic waveforms recorded by a tight array on the San Jacinto fault zoneTime‐frequency analysis shows clear Doppler effects that can be modeled with basic equations and fitted well with reasonable parametersThe daily duration of air traffic events is likely to exceed in many places the total time covered by earthquakes

In previous work, punishment has been proved to an effective way to promote cooperation, and punish is introduced as a strategy just like cooperate or defect. In this paper, however, we introduce a new synergy punishment mechanism into a spatial public good game which is different from the previous works, every cooperator has a probability to be a punisher, additionally if punishment is carried out by several punishers, the cost of punishment will be reduced by synergistic effect. The simulation results show that punishment can promote cooperation and the synergistic effect has an obvious influence on cooperation for a small punishment fine but has little influence for a big punishment fine. [ABSTRACT FROM AUTHOR]

We use a dense seismic array of 1,108 vertical‐component geophones within a 600‐m footprint to detect thousands of small earthquakes near an active strand of the San Jacinto fault zone in southern California during a 26‐day period. We first correct site effects using multichannel cross‐correlations of the P‐waves of 256 cataloged earthquakes, and then perform beamforming analysis on the continuous waveforms in a slowness range from −0.4 to 0.4 s/km in both the east and north directions. At each time step, we identify the beam slowness with maximum amplitude and apply a picking algorithm to identify 13,408 events. These detections include over 55.6% of the events in the Quake Template Matching (QTM) catalog for all of southern California during the same time period and 70% of those within 100 km of the array. In addition, we detect over 10,000 new events, not in the QTM catalog. Many of these events can also be seen in records from nearby borehole seismic stations. Measured slownesses for the catalog and newly‐discovered events group into clusters that can be associated with QTM earthquake locations, but with slowness values considerably distorted from predictions based on a 1‐D velocity model, presumably owing to strong velocity heterogeneity near the San Jacinto Fault. Amplitudes of the detected events obey a Gutenberg‐Richter distribution with a b‐value close to one. Foreshocks are common among these detected events, increasing in rate before mainshocks following an inverse Omori's law. We show that an array of over 1,000 seismometers located on a small patch of the San Jacinto Fault in southern California can detect many times more earthquakes than those in standard earthquake catalogs. Although accurate locations cannot be obtained for most of these newly identified events, their amplitudes indicate they obey similar scaling with size as larger magnitude earthquakes. Many of the larger events are preceded by foreshocks and the foreshock rate increases in the time leading up to the mainshocks. Our results suggest that future deployments of small‐aperture seismometer arrays could be used to complement existing seismic networks to probe earthquake activity in great detail. We detect over 13,000 seismic events during 26 days in 2014 using a dense nodal array on the San Jacinto fault zone in southern CaliforniaMost of the detections are newly discovered earthquakes not in existing catalogsForeshocks are common among these earthquakes, increasing in the rate before their mainshocks following an inverse Omori's law We detect over 13,000 seismic events during 26 days in 2014 using a dense nodal array on the San Jacinto fault zone in southern California Most of the detections are newly discovered earthquakes not in existing catalogs Foreshocks are common among these earthquakes, increasing in the rate before their mainshocks following an inverse Omori's law

A precise finite‐fault model including the fault geometry and slip distribution is essential to understand the physics of an earthquake. However, the conventional linear inversion of geodetic data for a finite‐fault model cannot fully resolve the fault geometry. In this study, we developed a Bayesian inversion framework that can comprehensively solve a non‐planar fault geometry, the corresponding fault slip distribution with spatially variable directions, and objective weighting for multiple data types. In the proposed framework, the probability distributions of all the model parameters are sampled using the Monte Carlo method. The developed methodology removes the requirement for manual intervention for the fault geometry and data weighting and can provide an ensemble of plausible model parameters. The performance of the developed method is tested and demonstrated through inversions for synthetic oblique‐slip faulting models. The results show that the constant rake assumption can significantly bias the estimates of fault geometry and data weighting, whereas additional consideration of the variability of slip orientations can allow plausible estimates of a non‐planar fault geometry with objective data weighting. We applied the method to the 2013 Mw6.5 Lushan earthquake in Sichuan province, China. The result reveals dominant thrust slips with left‐lateral components and a curved fault geometry, with the confidence interval of the dip angles being between 20°–25° and 56°–58°. The proposed method provides useful insights into the scope of imaging a non‐planar fault geometry, and could help to interpret more complex earthquake sources in the future. An earthquake originates from a rapid relative movement between two blocks on both sides of a fault. The earthquake can produce ground deformation, which can be measured by geodetic instruments deployed on the ground. Geophysicists can thus utilize the geodetic measurements to invert the earthquake attributes, including the fault geometry and the associated slips (i.e., the zone and amount of the relative movement between two blocks). Simultaneous estimation of the fault geometry parameters and the slip parameters is a nonlinear problem. The conventional inversion of geodetic data uses a linear adjustment method that cannot fully resolve the fault geometry. In addition, geodetic measurements have different precisions in terms of the types of instruments. We developed a nonlinear inversion based Bayesian inference framework to solve both these problems. The proposed method can correctly interpret an earthquake source with a non‐planar fault geometry. We applied the method to the 2013 Mw6.5 Lushan earthquake in southwest China and determined a statistical estimate of the fault geometry and slips. The results indicate that the proposed method could help to improve our knowledge of complicated earthquake sources in the future. We develop a Bayesian finite‐fault inversion for non‐planar fault geometry and spatially variable slip amplitude and rakeAccounting for variable directions of fault slips allows to better constrain non‐planar fault geometry and tune objective data weightingAccounting for the non‐planar fault geometry, the 2013 Lushan earthquake modeling reveals a dominant thrust slip with sinistral component We develop a Bayesian finite‐fault inversion for non‐planar fault geometry and spatially variable slip amplitude and rake Accounting for variable directions of fault slips allows to better constrain non‐planar fault geometry and tune objective data weighting Accounting for the non‐planar fault geometry, the 2013 Lushan earthquake modeling reveals a dominant thrust slip with sinistral component

The 2021 Mw6.1 Yangbi earthquake in southwest China is preceded by three major foreshocks: 05/18 Mw4.3, 05/19 Mw4.6, and 05/21 Mw5.2. It provides a valuable chance to revisit two end‐member foreshock models: cascade‐up and pre‐slip models. We first determine the fault structure with relocated aftershocks and the focal mechanisms obtained from a multipoint‐source inversion. We find that the mainshock and two smaller foreshocks occur on an unmapped near‐vertical fault, and the largest foreshock occurs on a mapped stepover fault that dips to northeast. Second, for each major foreshock, we estimate and delineate the rupture area based on its aftershock location and a spectral ratio analysis. Based on the inferred rupture model, we finally calculate the evolution of Coulomb stress and examine potential interactions between each major event. Results show that the Yangbi sequence can be explained by the cascade triggering mechanism, while we also find evidence for aseismic slip that contributes to the triggering process: the first foreshock is preceded by a short‐term localized cluster, three repeater sequences are detected in the foreshock period, and the aftershock zones expand logarithmically with time. The Yangbi mainshock is probably triggered by multiple major foreshocks through both seismic and aseismic processes. This detailed seismological characterization of Yangbi sequence lends supports for an improved understanding on the foreshock mechanism: (a) the controlling mechanisms are not limited to cascade‐up and pre‐slip. Instead, multiple mechanisms can operate together; and (b) aseismic slip does not always provide more deterministic information on the mainshock. The 2021 magnitude 6.1 Yangbi earthquake in southwest China is preceded by three magnitude 4–5 earthquakes, which are known as foreshocks. Whether the foreshock sequence can be used to forecast future occurrence of large earthquakes is of great scientific interest. Here, we analyze the interaction between these foreshocks with a high‐resolution seismic catalog and the modeling of stress transfer after each major foreshock. We find that the migration directions of foreshocks are not consistent, and that the stress transfer following each foreshock is large enough to explain the triggering of the Yangbi mainshock. This supports the cascade triggering model for foreshock generation, where the foreshocks are not precursors. However, we also find several lines of evidence for aseismic fault slip. These include: the first foreshock is preceded by a short‐term localized cluster, three repeater sequences are detected in the foreshock sequence, and the aftershock zones of three foreshocks expand logarithmically with time, suggesting that they are driven by afterslip. Our results suggest that multiple mechanisms (e.g., cascade‐triggering and aseismic slip) can act at the same time to drive foreshocks, and it is relatively challenging to use foreshocks to forecast future occurrence of large earthquakes. The Yangbi mainshock and two smaller foreshocks occur on an unmapped near‐vertical fault, and the largest foreshock occurs on a mapped stepover fault that dips to NEThe rupture directivity and source parameters of major foreshocks are estimated by aftershock distribution and spectral ratio analysisThe Yangbi sequence can be explained as a cascade sequence, but aseismic processes can also be inferred, including pre‐slip cluster and logarithmic expansion of aftershocks The Yangbi mainshock and two smaller foreshocks occur on an unmapped near‐vertical fault, and the largest foreshock occurs on a mapped stepover fault that dips to NE The rupture directivity and source parameters of major foreshocks are estimated by aftershock distribution and spectral ratio analysis The Yangbi sequence can be explained as a cascade sequence, but aseismic processes can also be inferred, including pre‐slip cluster and logarithmic expansion of aftershocks

Frequency-dependent rupture behavior of subduction zone interplate megathrust faults has been observed by back-projection method in different frequency bands (from 0.05 to 5 Hz). It has been suggested that the down-dip region of the Tohoku megathrust radiated strongly at high frequencies (>10 Hz) compared with that of the up-dip region. By assuming the same source time function of each fault patch, we perform a synthetic sensitivity analysis to compare the energy received from the shallower parts (and further way from the receiver sites) with that from the deeper parts (and closer to the receiver sites) of the rupture. Our results indicate that regional on-shore recordings are probably not adequate to constrain the presence of far-off shore high frequency radiations because of the strong attenuation of this region.

Understanding earthquake foreshocks is essential for deciphering earthquake rupture physics and can aid seismic hazard mitigation. With regional dense seismic arrays, we identify immediate foreshocks of 527 0.9 ≤M≤5.4 events of the 2019 Ridgecrest earthquake sequence, including 48 earthquakes with series of immediate foreshocks. These immediate foreshocks are adjacent to the mainshocks occurring within 100 s of the mainshocks, and their P waves share high resemblances with the mainshock P waves. However, attributes of the immediate‐foreshock P waves, including the amplitudes and preceding times, do not clearly scale with the mainshock magnitudes. Our observations suggest that earthquake rupture may initiate in a universal fashion but evolves stochastically. This indicates that earthquake rupture development is likely controlled by fine‐scale fault heterogeneities in the Ridgecrest fault system, and the final magnitude is the only difference between small and large earthquakes. Understanding earthquake foreshocks have both scientific and societal implications regarding earthquake physics and seismic hazards. Using dense arrays in the Ridgecrest region, we find immediate foreshocks of 527 earthquakes that occurred within a month of the 2019 Mw7.1 Ridgecrest earthquake. These immediate foreshocks are adjacent to their mainshocks and likely near‐instantaneously trigger the following slip within 100 s. Attributes of the P waves of these immediate foreshocks do not seem to correlate with the mainshock magnitudes. Our observations suggest that earthquakes may initiate via similar means and it remains challenging to use such foreshocks to predict the mainshock magnitudes. Immediate foreshocks are observed for 527 Ridgecrest earthquakesCharacteristics of their P waves do not scale with the eventual earthquake magnitudesThese Ridgecrest earthquakes may have initiated as a rate‐dependent cascading process Immediate foreshocks are observed for 527 Ridgecrest earthquakes Characteristics of their P waves do not scale with the eventual earthquake magnitudes These Ridgecrest earthquakes may have initiated as a rate‐dependent cascading process

Proper classification of nontectonic seismic signals is critical for detecting microearthquakes and developing an improved understanding of ongoing weak ground motions. We use unsupervised machine learning to label five classes of nonstationary seismic noise common in continuous waveforms. Temporal and spectral features describing the data are clustered to identify separable types of emergent and impulsive waveforms. The trained clustering model is used to classify every 1 s of continuous seismic records from a dense seismic array with 10–30 m station spacing. We show that dominate noise signals can be highly localized and vary on length scales of hundreds of meters. The methodology demonstrates the complexity of weak ground motions and improves the standard of analyzing seismic waveforms with a low signal‐to‐noise ratio. Application of this technique will improve the ability to detect genuine microseismic events in noisy environments where seismic sensors record earthquake‐like signals originating from nontectonic sources. Improvements in microseismic detection will advance observations of failure processes on faults subjected to slowly accumulating tectonic stress. Continuous seismic waveforms contain copious variations of nontectonic signals that inhibit the detection of genuine microearthquakes. Developing a framework to identify emergent and impulsive signals originating from natural and anthropogenic activity will advance seismic network monitoring capabilities. We apply unsupervised machine learning techniques to classify multiple types of weak ground motions that occur ubiquitously in continuous seismic records. A trained model is used to label every 1 s of a dense geophone array to provide a high‐resolution description of the anatomy of continuous seismic records. Further methodology developments and application in various environments have high potential for improving the ability to monitor earthquakes and other sources of ground motion. Emergent and impulsive signals in continuous seismic waveforms are identified using cluster analysis on a dense array dataAn unsupervised learning model is trained to identify multiple classes of noise using temporal and spectral data featuresA more complete understanding of seismic noise signals will improve the ability to detect genuine microseismic events

We develop a semiautomated method for estimating with second seismic moments the directivity, rupture area, duration, and centroid velocity of earthquakes. The method is applied to 41 southern California earthquakes with magnitude in the range 3.5–5.2 and provides stable results for 28 events. Apparent source time functions (ASTFs) of Pand Sphases are derived using deconvolution with three stacked empirical Green's functions (seGf). The use of seGf suppresses nongeneric source effects, improves the focal mechanism correspondence to the analyzed earthquakes, and typically allows inclusion of 5 to 15 more ASTFs compared with analysis using a single eGf. Most analyzed earthquakes in the Trifurcation area of the San Jacinto Fault have directivities toward the northwest, while events around Cajon Pass and San Gabriel Mountain tend to propagate toward the southeast. These results are generally consistent with predictions for dynamic rupture on bimaterial interfaces associated with the imaged velocity contrasts in the area. The second moment inversions also provide constraints on the upper and lower bounds of rupture areas in our data set. Stress drops and uncertainties are estimated for elliptical ruptures using the derived characteristic rupture length and width. The semiautomated second moment method with seGfs can be used for routine application to moderate earthquakes in locations with good station coverage. We develop a semiautomated method for estimating finite fault parameters of earthquakes with second seismic momentsResolved rupture directivities are generally consistent with expectations for dynamic ruptures on the imaged velocity contrastsStress drops and uncertainties are estimated for elliptical ruptures using the derived characteristic rupture length and width