Search Results (14)

P-wave first-motion polarity is a critical parameter for determining earthquake focal mechanisms. Extracting relative P-wave arrival times and polarity information using waveform cross-correlation techniques can enhance the accuracy of earthquake location and focal mechanism inversion. However, seismic noise severely hampers the reliable detection of P-wave onsets and their first-motion polarities. To address this issue, we propose a noise-resistant polarity detection method based on the normal time–frequency transform (NTFT), termed the time–frequency similarity coefficient (TFSC). The TFSC method computes relative delays and similarity coefficients by calculating the real part of the NTFT coefficients between two seismic signals. We validated the proposed approach using both synthetic and real earthquake data. Without any filtering or preprocessing, the TFSC method demonstrated significantly greater robustness and reliability compared to the conventional time-domain normalized cross-correlation (NCC) method. These results indicate that the TFSC method has strong potential for practical application and provides a novel perspective for accurate detection of P-wave first-motion polarity in noisy seismic environments. Full article

Phase arrival times and polarities provide essential kinematic constraints for and dynamic insights into seismic sources, respectively. This information serves as fundamental data in seismological study. For microseismic events with smaller magnitudes, reliable phase picking and polarity determination are even more challenging but play a crucial role in source location and focal mechanism inversion. This study innovatively proposes a deep learning model suitable for simultaneous phase picking and polarity determination with continuous microseismic waveforms. Building upon the Earthquake Transformer (EQT) model, we implemented structural improvements through four distinct decoders specifically designed for three tasks of P-wave picking, S-wave picking, and P-wave first-motion polarity determination and named the model EQT-Plus (EQTP). Notably, the polarity determination task was decomposed into two independent decoders to enhance the learning of polarity characteristics. Through training on a northern California dataset and testing on microseismic events (Md < 3) in the Geysers region, the results demonstrate that the EQTP model achieves superior performance in both phase picking and polarity determination compared to the PhaseNet+ model. It not only provides accurate phase picking but also shows higher consistency with manual picking results in polarity determination. We further validated the good generalization ability of the model with the DiTing dataset from China. This study not only advances the adaptation of the Transformer model in seismology but also reliably delivers fundamental information essential for refined microseismic inversion, offering an alternative and advanced tool for the seismological community. Full article

Hydraulic fracturing is a crucial technology for resource development, such as shale gas, and its optimization is necessary to enhance development efficiency. However, evaluating fracture shapes involves technical uncertainties. Japan Organization for Metals and Energy Security (JOGMEC) and Kyoto University have conducted laboratory-scale hydraulic fracturing experiments using coagulable fluorescent resin as the injection fluid (resin fracturing test) to visualize hydraulic fractures and investigate their relationship with acoustic emissions (AEs) generated during fracturing. Since lab-scale experiments can only examine the phenomena near the injection hole owing to size limitations, we designed an experiment to apply the visualization method to the outcrop scale. This paper presents the results from an in situ, outcrop-scale hydraulic fracturing experiment conducted at the Kamioka Mine, Gifu Prefecture, Japan, from 2022 to 2023, with goals similar to those of the laboratory experiments. A resin fracturing borehole (RF1) with a diameter of 76 mm was core-drilled to a depth of approximately 10 m for the resin fracturing tests. AEs were observed in five boreholes drilled around RF1 at the same depth. Resin fracturing tests were performed at two different depths, with breakdown confirmed at both. A core of a larger diameter (205 mm) was recovered by coaxial overcoring around RF1, and resin-filled fractures were observed under black light on the core surfaces. After the resin fracturing experiment, two analyses were performed using the acquired core and AE data to predict the fracture extension and the mechanism of AE occurrence. We compared the distribution of AE events and visualized fractures in the core. Additionally, we compared the stress direction estimated from failure mechanism analysis using AE data with the maximum stress direction estimated from hydraulic fracturing. Our analysis provided several insights into fracture extension. The distribution of AE hypocenters was consistent with the direction of the hydraulic fractures confirmed by coring after the resin fracturing test. The failure mechanisms are classified based on the polarity of the first P-wave motion of the AE waveform. However, the actual scale of oil fields is significantly larger than that considered in this study. Discussing visible fractures created by hydraulic fracturing is deemed meaningful. We expect that the results of this study will provide valuable information for the precise estimation of hydraulic fractures. Full article

The detection and monitoring of mining-induced seismicity are essential for understanding the mechanisms behind earthquakes and mitigating seismic hazards. However, traditional underground seismic monitoring networks for mining-induced seismicity are challenging to install and operate, which has limited their widespread application. In recent years, an alternative approach has emerged: utilizing dense seismic arrays at the surface to monitor mining-induced seismicity. This paper proposes a rapid and efficient data processing scheme for the detection and monitoring of mining-induced seismicity based on the surface dense array. The proposed workflow includes machine learning-based phase picking and P-wave first-motion-polarity picking, followed by rapid phase association, precise earthquake location, and template matching for detecting small earthquakes to enhance the completeness of the earthquake catalog. Additionally, it also provides focal mechanism solutions for larger mining-induced events. We applied this workflow to the continuous waveform data from 90 seismic stations over a period of 27 days around the Dongchuan Copper Mine, Yunnan Province, China. Our results yielded 1536 high-quality earthquake locations and two focal mechanism solutions for larger events. By analyzing the spatiotemporal distribution of these events, we are able to investigate the mechanisms of the induced seismic clusters near the Shijiangjun and Lanniping deposits. Our findings highlight the excellent monitoring capability and application potential of the workflow based on machine learning and template matching compared with conventional techniques. Full article

This study first employs TurbSim and OpenFAST (Fatigue, Aerodynamics, Structures, Turbulence) programs for secondary development to comprehensively model the NREL-5MW semi-submersible wind turbine and OC4-DeepC wind floating platform with wind–wave interaction. Next, we investigate the dynamic response of floating wind turbines under the complex coupling of turbulent winds and irregular waves. Turbulent wind fields were simulated using the IEC Kaimal model with turbulence intensities of 5% and 20%. Additionally, two irregular waves were simulated with the Pierson–Moskowitz (P–M) spectrum. The results indicate that in turbulent wind conditions, the aerodynamic power of the wind turbine and the root bending moments of the blades are significantly influenced by turbulence, while the impact of waves is minimal. The coupled motion response of the floating platform demonstrates that turbulence intensity has the greatest impact on the platform’s heave and pitch motions, underscoring the importance of turbulence in platform stability. This study provides essential insights for designing and optimizing floating wind turbines in complex wind–wave coupling offshore environments. Full article

The relation between de Broglie’s double-solution approach to quantum dynamics and the hydrodynamic pilot-wave system has motivated a number of recent revisitations and extensions of de Broglie’s theory. Building upon these recent developments, we here introduce a rich family of pilot-wave systems, with a view to reformulating and studying de Broglie’s double-solution program in the modern language of classical field theory. Notably, the entire family is local and Lorentz-invariant, follows from a variational principle, and exhibits time-invariant, two-way coupling between particle and pilot-wave field. We first introduce a variational framework for generic pilot-wave systems, including a derivation of particle-wave exchange of Noether currents. We then focus on a particular limit of our system, in which the particle is propelled by the local gradient of its pilot wave. In this case, we see that the Compton-scale oscillations proposed by de Broglie emerge naturally in the form of particle vibrations, and that the vibration modes dynamically adjust to match the Compton frequency in the rest frame of the particle. The underlying field dynamically changes its radiation patterns in order to satisfy the de Broglie relation $p=\hslash k$ at the particle’s position, even as the particle momentum p changes. The wave form and frequency thus evolve so as to conform to de Broglie’s harmony of phases, even for unsteady particle motion. We show that the particle is always dressed with a Compton-scale Yukawa wavepacket, independent of its trajectory, and that the associated energy imparts a constant increase to the particle’s inertial mass. Finally, we see that the particle’s wave-induced Compton-scale oscillation gives rise to a classical version of the Heisenberg uncertainty principle. Full article

In this paper, a novel flow perturbation model meant to investigate the effects of incoming wind shear on a hypersonic inlet/isolator is presented. This research focuses on the transient shock/boundary layer interaction and shock train flow evolution in a hypersonic inlet/isolator with an on-design Mach number of 6.0 under incoming wind shear at high altitudes, precisely at an altitude of 30 km with a magnitude speed of 80 m/s. Despite the low intensity of wind shear at high altitudes, the results reveal that wind shear significantly disrupts the inlet/isolator flowfield, affecting the shock wave/boundary layer interaction in the unthrottled state, which drives the separation bubble at the throat to move downstream and then upstream. Moreover, the flowfield behaves as a hysteresis phenomenon under the effect of wind shear, and the total pressure recovery coefficients at the throat and exit of the inlet/isolator increase by approximately 10% to 12%. Furthermore, this research focuses on investigating the impact of wind shear on the behavior of the shock train. Once the inlet/isolator is in a throttled state, wind shear severely impacts the motion of the shock train. When the downstream backpressure is 135 times the incoming pressure (p₀), the shock train first moves upstream and gradually couples with a cowl shock wave/boundary layer interaction, resulting in a more significant separation at the throat, and then moves downstream and decouples from the separation bubble at the throat. However, if the downstream backpressure increases to 140 p₀, the shock train enlarges the separation bubble, forcing the inlet/isolator to fall into the unstart state, and it cannot be restarted. These findings emphasize the need to consider wind shear effects in the design and operation of hypersonic inlet/isolator. Full article

P-wave first-motion polarity is important for the inversion of earthquake focal mechanism solutions. The focal mechanism solution can further contribute to our understanding of the source rupture process, the fault structure, and the regional stress field characteristics. By using the abundant focal mechanism solutions of small and moderate earthquakes, we can deepen our understanding of fault geometry and the seismogenic environment. In this paper, we propose an automatic workflow, FocMech-Flow (Focal Mechanism-Flow), for identifying P-wave first-motion polarity and focal mechanism inversion with deep learning and applied it to the 2021 Yangbi earthquake sequence. We use a deep learning model named DiTingMotion to detect the P-wave first-motion polarity of 2389 waveforms, resulting in 98.49% accuracy of polarity discrimination compared with human experts. The focal mechanisms of 112 earthquakes are obtained by using the CHNYTX program, which is 3.7 times more than that of the waveform inversion method, and the results are highly consistent. The analysis shows that the focal mechanisms of the foreshock sequence of the Yangbi earthquake are highly consistent and are all of the strike-slip type; the focal mechanisms of the aftershock sequence are complex, mainly the strike-slip type, but there are also reverse and normal fault types. This study shows that the deep learning method has high reliability in determining the P-wave first-motion polarity, and FocMech-Flow can obtain a large number of focal mechanism solutions from small and moderate earthquakes, having promising application in fine-scale stress inversion. Full article

Current chronic pain treatments primarily target symptoms and are often associated with harmful side-effects and complications, while safer non-invasive electrotherapies like H-Wave^® device stimulation (HWDS) have been less explored. The goal of this study is to evaluate first responder-reported effects of HWDS on job-related and quality-of-life measures. This is a retrospective cohort study where first responders were surveyed following voluntary use of HWDS regarding participant experience, frequency of use, job-related performance, and quality-of-life. Responses were analyzed using means comparison tests, while bivariate analysis assessed responses associated with HWDS usage. Overall, 92.9% of first responder HWDS users (26/28) reported a positive experience (p < 0.0001), with 82.1% citing pain reduction (p = 0.0013), while 78.6% indicated it would be beneficial to have future device access (p = 0.0046). Participants using H-Wave^® were at least six times more likely to report higher rates of benefit (100% vs. 0%, p = 0.022), including pain reduction (91.3% vs. 8.7%, p = 0.021) and improved range-of-motion (93.3% vs. 69.2%, p = 0.044). Spending more time with family was associated with better job performance following frequent HWDS use (50% vs. 8.3%, p = 0.032). Repetitive first responder H-Wave^® use, with minimal side effects and easy utilization, resulted in significant pain reduction, improvements in job performance and range-of-motion, and increased time spent with family, resulting in overall positive experiences and health benefits. Level of Evidence: III. Full article

Under the assumptions of linear elasticity and small deformation in traditional elastodynamics, the anisotropy of the medium has a significant effect on rotations observed during earthquakes. Based on the basic theory of the first-order velocity-stress elastic wave equation, this paper simulates the seismic wave propagation of the translational and rotational motions in two-dimensional isotropic and VTI (transverse isotropic media with a vertical axis of symmetry) media under different source mechanisms with the staggered-grid finite-difference method with respect to nine different seismological models. Through comparing the similarities and differences between the translational and rotational components of the wave fields, this paper focuses on the influence of anisotropic parameters on the amplitude and phase characteristics of the rotations. We verify that the energy of S waves in the rotational components is significantly stronger than that of P waves, and the response of rotations to the anisotropic parameters is more sensitive. There is more abundant information in the high-frequency band of the rotational components. With the increase of Thomsen anisotropic parameters ε and δ, the energy of the rotations increases gradually, which means that the rotational component observation may be helpful to the study of anisotropic parameters. Full article

This study presents a generic model for constructing shear-wave velocity (V_S) profiles for various conditions that can be used for modeling the upper-crustal modification effects in ground motion simulations for seismic hazard analysis. The piecewise P-wave velocity (V_P) profiling model is adopted in the first place, and the V_S profile model is obtained by combining the V_P profiling model and V_S/V_P model. The used V_S/V_P model is constructed from various field measurements, experimental data, or CRUST1.0 data collected worldwide. By making the best use of the regionally/locally geological information, including the thickness of sedimentary and crystalline layers and reference V_S values at specific depths, the V_S profile can be constructed, and thus the amplification behavior of V_S for a given earthquake scenario can be predicted. The generic model has been validated by four case studies of different target regions world around. The constructed profiles are found to be in fair agreement with field recordings. The frequency-dependent upper-crustal amplification factors are provided for use in stochastic ground motion simulations for each respective region. The proposed V_S profiling model is proposed for region-specific use and can thus make the ground motion predictions to be partially non-ergodic. Full article

Brushing motion has been proposed for endodontic single-file reciprocating systems to eliminate coronal interferences, but it may hesitate in a less conservative root canal shaping. The aim of the study was to compare the maintenance of the original root canal anatomy using the WaveOne Gold (WOG) technique with or without a brushing motion. Sixty extracted human mandibular first molars were selected. Manual canal scouting and mechanical glide path were performed. Samples were randomized into two groups (n = 30): WOG Primary was used to shape the mesio-lingual canals without (NB group) or with (B group) an intentional brushing motion. Specimens were scanned with micro-computed tomography (micro-CT) before and after instrumentation in order to match the volumes. Increases in canal volume and surface area were measured. Centroid shift and thickness of removed dentine from the inner curvature were assessed at the furcation, 1.5 mm and 3 mm apically from the furcation, and in relation to the point of maximum curvature. Data were analyzed by one-way ANOVA and post-hoc Student–Newmann–Keuls tests (p < 0.05). No brushing technique resulted in being significantly associated with a better maintenance of the canal anatomy, especially in the coronal third (p < 0.05). When using the WOG system, a no-brushing technique resulted in less invasive shaping, reducing the risk of stripping. Full article

Earthquakes are one of the major natural calamities as well as a prime subject of interest for seismologists, state agencies, and ground motion instrumentation scientists. The real-time data analysis of multi-sensor instrumentation is a valuable knowledge repository for real-time early warning and trustworthy seismic events detection. In this work, an early warning in the first 1 micro-second and seismic wave detection in the first 1.7 milliseconds after event initialization is proposed using a seismic wave event detection algorithm (SWEDA). The SWEDA with nine low-computation-cost operations is being proposed for smart geospatial bi-axial inclinometer nodes (SGBINs) also utilized in structural health monitoring systems. SWEDA detects four types of seismic waves, i.e., primary (P) or compression, secondary (S) or shear, Love (L), and Rayleigh (R) waves using time and frequency domain parameters mapped on a 2D mapping interpretation scheme. The SWEDA proved automated heterogeneous surface adaptability, multi-clustered sensing, ubiquitous monitoring with dynamic Savitzky–Golay filtering and detection using nine optimized sequential and structured event characterization techniques. Furthermore, situation-conscious (context-aware) and automated computation of short-time average over long-time average (STA/LTA) triggering parameters by peak-detection and run-time scaling arrays with manual computation support were achieved. Full article

By means of the time derivatives of Global Navigation Satellite System (GNSS) carrier-phase measurements, the instantaneous velocity of a stand-alone, single GNSS receiver can be estimated with a high precision of a few mm/s; it is feasible to even obtain the level of tenths of mm/s. Therefore, only data from the satellite navigation message are needed, thus discarding any data from a reference network. Combining this method with an efficient movement-detection algorithm opens some interesting applications for geohazard monitoring; an example is the detection of strong earthquakes. This capability is demonstrated for a case study of the 6.5 Mw earthquake of October 30, 2016, near the city of Norcia in Italy; in that region, there are densely deployed GNSS stations. It is shown that GNSS sensors can detect seismic compressional (P) waves, which are the first to arrive at a measurement station. These findings are substantiated by a comparison with data of strong-motion (SM) seismometers. Furthermore, it is shown that the GNSS-only hypocenter localization comes close (less than a kilometer) to the solutions provided by official seismic services. Finally, we conclude that this method can provide important contributions to a real-time geohazard early-warning system. Full article