Search Results (75)

Intelligent Transportation Systems (ITSs) play a vital role in improving urban and regional mobility by reducing traffic congestion and enhancing trip planning. A key element of ITS is travel-time prediction, which supports informed decisions for both travelers and traffic management. While non-parametric models offer flexibility, they often require large datasets and significant computation. Parametric models, though easier to fit and interpret, are less adaptable. Fuzzy logic models, by contrast, provide robustness and scalability, adjusting to new data and changing conditions. This paper proposes a cascaded fuzzy logic system for highway travel-time prediction, using the Greenshields model as its reasoning foundation. The system consists of multiple fuzzy subsystems, each representing a highway segment. These subsystems transform traffic flow and density inputs into speed predictions through fuzzification, Greenshields-based rules, and defuzzification. The approach enables localized and segment-specific predictions, enhancing route planning and congestion avoidance. The system’s accuracy is evaluated by comparing its predictions with those of a regression model using real traffic data from the Sun Yat-Sen Highway in Taiwan. Simulation results confirm that the proposed model achieves reliable, adaptable travel-time forecasts, including for long-distance trips. Full article

The South China block (SCB) is characterized by complex tectonics, large-scale lithospheric deformation, and extensive mineralization in its southeastern region. However, the geodynamic processes and mechanisms driving mineralization remain controversial, partly due to the lack of information on its fine crustal structure. The resolution of crustal seismic tomography is relatively low due to the uneven distribution of local earthquakes in South China. In this study, we conduct a joint inversion of Bouguer gravity and seismic travel-time data to investigate the detailed 3-D P-wave velocity (Vp) structure of the crust beneath the SCB. Our results show the following: (1) strong lateral heterogeneities exist in the crust, which reflect the surface geology and tectonics well; (2) the Vp patterns at different depths beneath the Yangtze block are almost consistent, but those beneath the Cathaysia block vary significantly, which might be related to the lithosphere thinning in the Mesozoic; (3) decoupling between the upper crust and the lower crust occurs at ~20 km depth beneath the eastern SCB; (4) the Vp patterns vary beneath different metallogenic belts; and (5) distinct low-Vp anomalies exist in the lower crust beneath mineral deposits. These results suggest that the deep mineralization is closely associated with the lithospheric thinning and upwelling thermal flow in the Mesozoic beneath the eastern SCB. Our Vp tomographic result also strongly supports the viewpoint that the mineralization mechanism varies for different metallogenic belts. Full article

As a significant geological hazard in large–scale engineering construction, deep subsurface voids demand effective and precise detection methods. Cross–hole radar tomography overcomes depth limitations by transmitting/receiving electromagnetic (EM) waves between boreholes, enabling the accurate determination of the spatial distribution and EM properties of subsurface cavities. However, conventional inversion approaches, such as travel–time/attenuation tomography and full–waveform inversion, still face challenges in terms of their stability, accuracy, and computational efficiency. To address these limitations, this study proposes a deep learning–based imaging method that introduces the concept of travel–time fingerprints, which compress raw radar data into structured, low–dimensional inputs that retain key spatial features. A large synthetic dataset of irregular subsurface cavity models is used to pre–train a UNET model, enabling it to learn nonlinear mapping, from fingerprints to velocity structures. To enhance real–world applicability, transfer learning (TL) is employed to fine–tune the model using a small amount of field data. The refined model is then tested on cross–hole radar datasets collected from a highway construction site in Guizhou Province, China. The results demonstrate that the method can accurately recover the shape, location, and extent of underground cavities, outperforming traditional tomography in terms of clarity and interpretability. This approach offers a high–precision, computationally efficient solution for subsurface void detection, with strong engineering applicability in complex geological environments. Full article

Accurate measurement of atmospheric turbulent fluctuations is critical for understanding environmental dynamics and improving models in applications such as wind energy. Advanced remote sensing technologies are essential for capturing instantaneous velocity and temperature fluctuations. Acoustic tomography (AT) offers a promising approach that utilizes sound travel times between an array of transducers to reconstruct turbulence fields. This study presents a systematic evaluation of the time-dependent stochastic inversion (TDSI) algorithm for AT using synthetic travel-time measurements derived from large-eddy simulation (LES) fields under both neutral and convective atmospheric boundary-layer conditions. Unlike prior work that relied on field observations or idealized fields, the LES framework provides a ground-truth atmospheric state, enabling quantitative assessment of TDSI retrieval reliability, sensitivity to travel-time measurement noise, and dependence on covariance model parameters and temporal data integration. A detailed sensitivity analysis was conducted to determine the best-fit model parameters, identify the tolerance thresholds for parameter mismatch, and establish a maximum spatial resolution. The TDSI algorithm successfully reconstructed large-scale velocity and temperature fluctuations with root mean square errors ($RMSE\mathrm{s}$) below 0.35 m/s and 0.12 K, respectively. Spectral analysis established a maximum spatial resolution of approximately 1.4 m, and reconstructions remained robust for travel-time measurement uncertainties up to 0.002 s. These findings provide critical insights into the operational limits of TDSI and inform future applications of AT for atmospheric turbulence characterization and system design. Full article

Efficient traffic signal control plays a critical role in promoting sustainable mobility by reducing congestion and minimizing vehicle emissions. This paper proposes an enhanced max-pressure (MP) signal control strategy that explicitly accounts for phase switching time losses in grid road networks. While the traditional MP control strategy is recognized for its decentralized architecture and simplicity, it often neglects the delays introduced by frequent phase changes, limiting its real-world effectiveness. To address this issue, three key improvements are introduced in this study. First, a redefined phase pressure formulation is presented, which incorporates imbalances in traffic demand across multiple inlet roads within a single phase. Second, a dynamic green phase extension mechanism is developed, which adjusts phase durations in real time based on queue lengths to improve traffic flow responsiveness. Third, a current-phase protection mechanism is implemented by applying an amplification factor to the current-phase pressure calculations, thereby mitigating unnecessary phase switching. Simulation results using SUMO on a grid network demonstrate that the proposed strategy significantly reduces average vehicle delays and queue lengths compared with traditional MP, travel-time based MP, and fixed-time control strategies, leading to improved overall traffic efficiency. Specifically, the proposed method reduces total delay by 24.83%, 26.67%, and 47.11%, and average delay by approximately 16.18%, 18.91%, and 36.22%, respectively, while improving traffic throughput by 2.25%, 2.76%, and 5.84%. These improvements directly contribute to reducing traffic congestion, fuel consumption, and greenhouse gas emissions, thereby reinforcing the role of adaptive signal control in achieving smart and sustainable cities. The proposed approach can serve as a practical reference for improving real-world traffic signal control systems, particularly in regions seeking to improve sustainability and operational efficiency. Full article

Preliminary tunnel surveys are essential for identifying geological hazards such as aquifers, faults, and karstic zones. While first-arrival tomography is effective for imaging shallow anomalies, traditional seismic sources face significant limitations in forested mountainous regions due to mobility, cost, and environmental impact. To address this, we deployed a seismic source delivered by an unmanned aerial vehicle (UAV) for a highway tunnel survey in Lijiang, China. The UAV system, paired with nodal geophones, enabled rapid, low-impact, and high-resolution data acquisition in rugged terrain. To enhance the weak far-offset refractions affected by near-surface attenuation, we applied supervirtual refraction interferometry (SVI), which significantly improved the signal-to-noise ratio and expanded the usable first-arrival dataset. The combined use of UAV excitation and SVI processing produced a high-precision P-wave velocity model through traveltime tomography, aligned well with borehole data. This model revealed the spatial distribution of weathered zones and bedrock interfaces, and allowed us to infer potential fracture zones. The results offer critical guidance for tunnel alignment and hazard mitigation in complex geological settings. Full article

Ground-penetrating radar (GPR) has emerged as a promising technology for estimating the soil water content (SWC) in the vadose zone. However, most current studies focus on partial GPR data, such as travel-time or amplitude, to achieve SWC estimation. Full waveform inversion (FWI) can produce more accurate results than inversion based solely on travel-time. However, it is subject to local minima when using a local optimization algorithm. In this paper, we propose a novel and powerful GPR waveform inversion scheme based on Harris hawks optimization (HHO) algorithm. The proposed strategy is tested on synthetic data, as well as on field experimental data. To further validate our approach, the results of the HHO algorithm are also compared with those of partial swarm optimization (PSO) and grey wolf optimizer (GWO). The inversion results from both synthetic and real experimental data demonstrate that the proposed inversion scheme can efficiently invert both SWC and layer thicknesses, thus achieving very fast convergence. These findings further confirm that the HHO algorithm can be effectively applied for the quantitative interpretation of GPR data. Full article

The construction of accurate anisotropic velocity models is essential for effective microseismic monitoring in hydraulic fracturing. Ignoring anisotropy can result in significant distortions in microseismic event locations and their interpretation. Although methods exist to simultaneously invert anisotropic parameters and event locations using microseismic arrival times, the results heavily depend on accurate initial models and sufficient ray coverage due to strong trade-offs among multiple parameters. Microseismic waveform inversion for anisotropic parameters remains challenging due to the low signal-to-noise ratio of the data and the high computational cost. To address these challenges, we propose a method for jointly inverting event locations and velocity updates based on arrival times and vertical slowness estimates, under the assumption of small horizontal velocity variations. Vertical slowness estimates, which are independent of source information and easily obtainable, provide an additional constraint that enhances inversion stability. We test the proposed method in four synthetic examples under various conditions. The results demonstrate that incorporating vertical slowness effectively constrains and stabilizes conventional travel-time inversion, especially in scenarios with poor raypath coverage. Additionally, we apply this method to a field case and find that it produces more reasonable event locations compared to inversions using arrival times alone. This joint inversion method can enhance the accuracy of anisotropic structures and event locations, which thus help with fracture characterization in tight and low-permeability reservoirs. It may serve as an effective downhole monitoring approach for hydrocarbon and geothermal energy production. Full article

Velocity gradient is an important parameter for interpreting tomographic velocity field and identifying geological boundaries. It is usually translated from the results of seismic travel-time tomography. Recent researches show seismic polarization direction appears to be a promising data source for obtaining velocity gradient field directly. However, what remains unclear is the sensitivity of polarization direction to velocity gradient, which causes difficulty for correctly inverting polarization direction data. To clarify this problem, the sensitivity of velocity gradient parameters on polarization direction is discussed in this paper. It was found that the sensitivity of polarization direction is related to the spatial position of the model parameter. The further the parameter position is from the sensor, the lower the sensitivity is. Such nonuniform distribution of sensitivity may cause distortion of inversion results with incomplete projection data. Based on this analysis, adjustment factors are introducing to the polarization direction inversion algorithm for correctly inverting polarization direction data. Numerical tests are conducted to verify our theoretical analysis and inversion algorithm. Test results show that our theoretical analysis is accurate in both homogeneous velocity field and near velocity interfaces. The inversion method with the adjustment factor can more accurately recover the velocity gradient, offering a promising approach for geological boundary imaging. Full article

Recent advancements in seismic data analysis have enhanced our grasp of the seismic heterogeneities near the core–mantle boundary (CMB). Through seismic tomography, persistent lower-mantle structures like the large low shear velocity provinces (LLSVPs) beneath the Pacific and South Africa have been identified. However, variations in the finer-scale features across different models raise questions about their origins. This study utilizes differential travel-time measurements from the USArray, operational across the contiguous United States from 2007 to 2014, to examine the impact of upper-mantle heterogeneities on tomographic models. By averaging the P-wave travel times and calibrating them with diffracted P-waves at the same stations, we mitigate the effects of shallow heterogeneities. The findings confirm that this method accurately maps the seismic anomalies beneath the USArray, consistent with other North American studies. Calibrated Pdiff travel-time data indicate significant anomalies in the mid-Pacific and Bering Sea and lesser anomalies in the northern Pacific, aligning with the global tomographic images. Moreover, the study highlights sharp travel-time variations over short distances, such as those across the northern boundary of the mid-Pacific anomaly, suggesting a chemically heterogeneous Pacific LLSVP. Full article

This study is focused on one of the most active features of the Hellenic Volcanic Arc Southern Aegean Sea, the Santorini Island Volcanic Complex (SVC). The recent volcano-tectonic crisis in the intracalderic area has emerged the need for closer monitoring of the region. The 2011–2012 unrest has been attributed to the augmentation of fluid flow inside local mapped fracture zones. After March 2012, the seismic activity dropped significantly, raising questions about whether we would have a long period of quiescence or be on a break before the next period of unrest. In this research, a re-examination of the seismic outbreak of 2011–2012 was conducted by adding more travel-time data from 2013 while we further analyzed the waveform data from 2014 to May 2024 to explore the differences of the SVC body-wave velocity structure by performing seismic tomography in these two time windows. The new dataset serves to identify the state of the Santorini Volcanic Complex. The results show a significant reduction in Vp and Vs anomalies at shallow depths since the period of unrest. At the same time, the distribution of Vp/Vs ratio remains high (>1.87) in the area NNE of Kameni at a shallower depth (2 km). The areas of Christiana Islands and Columbo volcano are mainly characterized by negative body-wave anomalies and low Vp/Vs ratio (1.56–1.64) at shallow depths for the study period, while a possible explanation to results in the submarine volcano may be explained by dry steam/gas phases that may have resulted in the generation of the swarms that occurred in the region. Full article

Several studies suggest that shale sweet spots are likely associated with a low Poisson’s ratio in the shale layer. Compared with conventional geophysical techniques with active seismic data, it is straightforward and cost-effective to delineate the distribution of 3D Poisson’s ratios using microseismic data. In this study, an alternating method is proposed to determine microseismic event locations, 3D P-wave velocity, and Poisson’s ratio models with data recorded from downhole monitoring arrays. The method combines the improved 3D traveltime tomography, which inverts P and S arrivals for 3D P-wave velocity and Poisson’s ratio structures simultaneously, and a 3D grid search approach for event locations in an iterative fashion. The traveltime tomography directly inverts the Poisson’s ratio structure instead of calculating the Poisson’s ratios from P- and S-wave velocities (i.e., Vp and Vs) that are inverted by conventional traveltime tomography separately. The synthetic results and analysis suggest that the proposed method recovers the true Poisson’s ratio model reasonably. Additionally, we apply the method to a field dataset, which indicates that it may help delineate the reservoir structure and identify potential shale sweet spots. Full article

This study examines the body-wave velocity structure of Attica, Greece. The region is located between two major rifts, the Gulf of Corinth and the Euboekos Gulf, and has experienced significant earthquakes throughout history. The distribution of seismic activity in the area necessitates a thorough investigation of geophysical properties, such as seismic velocities, to reveal the extent of significant fault zones or the presence of potential hidden faults. This case study utilized over 3000 revised events to conduct a local earthquake tomography (LET). P- and S-wave travel-time data were analyzed to explore small- to medium-scale (~10 km) anomalies that could be linked to local neotectonic structures. The study presents a detailed 3-D seismic velocity structure for Attica and its adjacent regions. The results of the study revealed strong lateral body-wave velocity anomalies in the upper crust were related to activated faults and that a significant portion of the observed seismicity is concentrated near the sites of the 1999 and 2019 events. Full article

Improving the estimation of origin–destination (O-D) travel times poses a formidable challenge due to the intricate nature of transportation dynamics. Current deep learning models often require an overwhelming amount of data, both in terms of data points and variables, thereby limiting their applicability. Furthermore, there is a scarcity of models capable of predicting travel times with basic trip information such as origin, destination, and starting time. This paper introduces novel models rooted in the k-nearest neighbor (KNN) algorithm to tackle O-D travel time estimation with limited data. These models represent innovative adaptations of weighted KNN techniques, integrating the haversine distance of neighboring trips and incorporating correction factors to mitigate prediction biases, thereby enhancing the accuracy of travel time estimations for a given trip. Moreover, our models incorporate an adaptive heuristic to partition the time of day, identifying time blocks characterized by similar travel-time observations. These time blocks facilitate a more nuanced understanding of traffic patterns, enabling more precise predictions. To validate the effectiveness of our proposed models, extensive testing was conducted utilizing a comprehensive taxi trip dataset sourced from Santiago, Chile. The results demonstrate substantial improvements over existing state-of-the-art models (e.g., MAPE between 35 to 37% compared to 49 to 60% in other methods), underscoring the efficacy of our approach. Additionally, our models unveil previously unrecognized patterns in city traffic across various time blocks, shedding light on the underlying dynamics of urban mobility. Full article

Engineers need to know properties of shallow marine sediments to build piers, pipelines and even offshore windfarms. We present a method for estimating the density, P velocity and thickness of these sediments. The traveltime inversion of primary and multiple reflections enables their semiquantitative estimation in marine surveys when using a minimal acquisition system such as a monochannel Boomer. Picking errors, ambient noise and interfering events lead to significant errors in the estimates. Similar, albeit milder, instabilities occur when inverting the signal amplitudes to determine the reflectivity of the layer interfaces. In this paper, we introduce a coupling between the separate inversion of amplitudes and traveltimes to obtain a better Earth model. The P velocity shows up in two stable terms provided by the separate inversions: the acoustic impedance of shallow sediments (through the amplitudes) and the transit time across the sediment layer (through the traveltimes). We couple the two inversion engines by imposing a smoothness condition on velocity and density and thickness of the layer while keeping the impedance and traveltime constant. We thus exploit the ambiguity of the solution to introduce geological criteria and reduce the noise contribution. We validated the proposed method with synthetic and real data. Full article