Travel Time Data Research Articles

Velocity structure of crust and mid-strong earthquake preparation characteristics in the Huoshan region, East China

The Huoshan region, located on the northern margin of the Dabie Orogenic Belt at the junction of the North China Plate and the Yangtze Plate, is one of the most seismically active and concentrated areas in the Dabie Orogenic Belt and adjacent regions. Utilizing the travel time data from 4,427 seismic events observed by 202 stations, we investigated the deep medium structure of the Huoshan region using the double-difference tomography method. The results reveal the medium structure and characteristics of mid-strong earthquake preparation in the region. The crustal medium in the study area exhibits significant lateral heterogeneity. The Dabie Orogenic Belt shows notably high velocity, whereas the North China Plate and the Yangtze Plate display relatively lower velocities. The Tan-Lu Fault Zone exhibits segmentation characteristics; with the crustal medium velocity south of Lujiang being relatively high, north of Jiashan being relatively low, and between Lujiang and Jiashan being intermediate. The epicenters of mid-strong earthquakes are located on the gradient zones of velocity and Poisson’s ratio. The source regions of these earthquakes show significant anomalies of high Poisson’s ratio and low S-wave velocity, which may indicate the presence of fluids. These anomalies possibly reflect the intrusion of deep materials along the fault zone, which could be the driving force for the preparation of mid-strong earthquakes.

The Impacts of Perceived Safety and Service Quality on Perceived Accessibility by Public Transport in Melbourne

In recent years, accessibility to services and activities using sustainable transport modes has become an important goal for land use and transport planning policies. Traditional approaches rely on objective measures, such as travel time, distance, costs, and other spatial data, overlooking personal preferences, experiences, demographic features, and socio-cultural and economic dynamics. Attributes that affect perceptions of accessibility require attention but our understanding of the factors influencing perceived accessibility is inadequate, particularly in Australia’s expanding suburbs. Through a literature review and questionnaire surveys, this study aims to take into account various travel characteristics and enhance the understanding of how perceived safety and service quality impact mobility behaviour and perceived accessibility within the Australian context. The study found that perceived safety and service quality have significant impacts on perceived accessibility and hence influence the use of public transport. We have identified critical factors that require proper consideration in transport and land use planning and policies to enable a better contribution from public transport to the liveability and well-being of residents in Australian suburbs.

Refining Heterogeneities near the Core–Mantle Boundary Beneath East Pacific Regions: Enhanced Differential Travel-Time Analysis Using USArray

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.

Integration of cost-effective datasets to improve predictability of strategic noise mapping in transport corridors in Delhi city, India.

Assessing exposure to environmental noise levels at transport corridors remains complex in conditions where no standardized noise prediction model is available. In planning and policy implementation for noise control, noise mapping is an important step. In the present study, land use regression model has been developed to predict the environmental noise levels in Delhi city, India, using previously developed approaches along with machine learning techniques, however improved using new datasets. Lday, Lnight, LAeq,24h, and Ldn were modeled at daily resolution by utilizing an annual noise levels dataset from 31 locations in Delhi city. The noise-monitored data was integrated with travel time data, nighttime light data along with common parameters including land use, road networks, and meteorological parameters. The developed LUR models showed good fit with R2 of 0.72 for Lday, 0.55 for Lnight, 0.71 for LAeq,24h, and 0.61 for Ldn, which was further improved up to 0.88 for Lday, 0.79 for Lnight, 0.86 for LAeq,24h, and 0.81 for Ldn by integrating machine learning approaches. The developed models were validated through tenfold cross validation and by comparison to a separate noise level dataset. The average travel time variable was observed to be the most influential predictor of LUR models for Lday and LAeq,24h, which signifies the crucial impact of road traffic congestion on environmental noise levels. The study also analyzed the parametric sensitivity of various infrastructural factors reported in the study, which shall be helpful for planning for smart cities.

Imaging Complex Structures of the Los Angeles Basin via Adjoint-State Travel-Time Tomography

ABSTRACT In this study, we present high-resolution seismic velocity models for the Los Angeles basin (LAB) and its adjacent area using adjoint-state travel-time tomography, fitting an extensive database of P- and S-wave travel times accumulated from 1980 to 2021. We select 151,193 first P-wave travel times and 149,997 first S-wave travel times from local earthquakes archived in the Southern California Earthquake Data Center to determine the velocity models, with earthquake locations updated at each iteration. With seismic stations spaced more than 3.5 km apart, our dataset has limited resolution in the uppermost 1–2 km. However, starting from three different initial models, our VP models, which are optimally imaged between 3 and 15 km, show similar velocity heterogeneity and provide a better fit to the observed first travel-time data compared to the Community Velocity Model-Harvard 15.1.0 and Community Velocity Model-Southern California Earthquake Center 4.26. Our models provide a detailed delineation of the subsurface structure beneath the LAB, revealing significant velocity variations across active faults, a 10-km-thick sequence of sedimentary rocks within the basin, and a distinct basin margin marked by transitions from low to high-velocities. In addition, these models highlight basement structures with elevated VP and VS located at depths of 9 to 12 km and beyond. Specifically, beneath the northeastern part of the basin, the models demonstrate improved accuracy and reliability in reflecting the linear relationship between VP and VS in mafic rocks. The accurate delineation of the basin’s structure provided by our models could also offer robust constraints for seismic response modeling and seismic hazard assessment in the region.

Deep learning-based cross-hole GPR travel time tomography

Abstract As a non-destructive geophysical detection method that is not limited by the target depth, cross-hole radar can accurately identify the positions and properties of underground cavities. Traditional cross-hole radar methods face significant challenges in stability, accuracy, and computational efficiency. In recent years, with the rapid development of deep learning and its remarkable achievements in the field of imaging, it has been applied in various fields. As a deep learning network structure for image segmentation, the Unet model can realize “end-to-end” image output. Therefore, the Unet model of convolutional neural network is introduced into cross-hole radar travel time tomography in this paper. A deep-learning trave time tomography method based on the nonlinear relationship between travel time data and models is proposed. Firstly, the models with cavities of different permittivities are constructed. The 2D traveltime images of transmitting and receiving pairs are calculated based on the GPR travel time fast-picking algorithm. Then, a Unet model is built to learn the nonlinear mapping relationship between the 2D travel time images and models with cavities. Finally, the intelligent travel time tomography method based on the Unet model of underground cavities is realized. Experiment results of models demonstrate the effectiveness of the proposed method.

Seismic Image of the Central to Southern Andean Subduction Zone Through Finite‐Frequency Tomography

AbstractThis study presents new seismic imaging of the Andean subduction zone through P‐wave hybrid finite‐frequency and ray‐theoretical tomography. We measured both differential and absolute traveltimes using broadband seismic waveforms from stations in an array of ocean‐bottom seismographs near the Chile Triple Junction (CTJ) and stations within 30° of the array. These data were combined with the global traveltime data set to obtain a global P‐wave velocity structure with a focus on central to southern South America. The new tomographic image showed the Nazca slab geometry as a continuous fast anomaly, which is consistent with seismic activity and prior slab models. Furthermore, two notable structures were observed: a broad extension of the fast anomaly beneath the Nazca slab at 26–35°S and a slow anomaly east of the CTJ. The checkerboard resolution and recovery tests confirmed the reliability of these large‐scale features. The fast anomaly, isolated from the Nazca slab, was interpreted as a relic Nazca slab segment based on its strong amplitude and spatial coincidence with the current Pampean and past Payenia flat slab segments. The slow anomaly near the CTJ was consistent with the previously inferred extent of the Patagonian slab window. Moreover, the active adakitic volcanoes are aligned with the southern edge of the anomaly, and the plateau basalts are located within the anomaly. Our model showed that the slow anomaly extended to a depth of up to 250 km, suggesting a depth limit that the asthenospheric window can influence.

Control of slab tears and slab flat wedging on volcanism in the Alaska subduction zone

Multistage plate subduction plays a crucial role in magmatism; however, the mechanisms by which deep geodynamic processes govern volcanism in the Alaska subduction zone remain controversial. Using numerous travel-time data from several seismic arrays, we constructed high-resolution tomographic models to investigate the velocity structure of the Pacific Plate and Yakutat slab. Our tomographic results revealed high-velocity anomalies in the Pacific Plate and Yakutat slab, while the low-velocity areas within the Pacific Plate were identified as slab tears. We suggest that the Pacific Plate transitioned from oblique subduction along the Aleutian volcano chain to lower-angle subduction beneath the Pacific-Yakutat Plate interaction zone, forming two slab tears that enhance hot asthenosphere materials upwelling. The partial melting of the mantle wedge induced by Pacific slab dehydration and the concurrent upwelling of mantle materials jointly drove volcanism in the transition zone. Conversely, the flat subduction of the Yakutat slab into the mantle wedge overlying the Pacific slab effectively hindered the upwelling of hot hybrid materials, cooling the Pacific mantle wedge. These results offer a new perspective on the influence of slab dynamics on volcanic and magmatic processes in the region and represent an advancement in our understanding compared to previous studies, which did not resolve the tears within the slab or their geodynamic implications at this level of detail.

Identification of strategic location for environmental noise control through the application of land use regression (lur) in a neural network platform

The significant causes of rising noise pollution within urban areas lie in extensive transportation networks, industrial operations, and commercial activities. Addressing this complexity, our study employs Land Use Regression (LUR) modelling to assess the intricate relationship between land-use features and environmental noise in urban landscape. The objective of this study is to provide a framework for predicting environmental noise levels in Delhi, India. For which, Lday, Lnight, LAeq, 24h, and Ldn noise descriptors were modelled at daily resolution data from 31 fixed-site monitors. The prediction at first stage for LUR was found to be R2 = 0.53 which was validated using the n-1 method. The model was later optimized using a NN. Output from the NN is included in the GIS environment to identify the hotspot of the noise. Previously identified engineering interventions applicable to urban contexts that were listed from the literature review were allocated as per hotspots because of the modelled output. The study also explored the avenue of using Delhi-specific transport variables developed from Google travel time data for prediction improvement of the noise level. The research contributes to advancement of noise pollution assessment and management methodologies, particularly in situations with no available validated traffic noise models.

Seismic Facies-Controlled Porosity Prediction in a Tight Sandstone Reservoir Based on the XGBoost Algorithm

Porosity is one of the most important properties for evaluating hydrocarbon reservoirs. There is a strong nonlinearity between seismic data and rock physical properties. Machine learning methods possess the powerful ability to capture complex nonlinear relationships between these two parameters, holding significant potential for porosity prediction in tight sandstone reservoirs. In this study, we propose a seismic facies-controlled porosity prediction method based on the extreme gradient boosting algorithm. Through seismic meme inversion method,we obtain high-resolution P-impedance data. In the inversion process, lateral variations of seismic waveforms were utilized instead of the variogram function. We create labels for model training using porosity curves and borehole side P-impedance data, applying them to the extreme gradient boosting regressor. To demonstrate its feasibility, we apply the model to a real 3D case from an oil field in the Songliao Basin. Our application results demonstrate that this method can provide porosity predictions for tight sandstone reservoirs, maintaining consistency with seismic waveforms and adhering to seismic facies control patterns. The method uses only acoustic travel time, density, gamma rays, and spontaneous potential well-log data and post-stack 3D seismic data as inputs, allowing for quick porosity predictions in the field. Compared to the seismic facies-controlled inversion combined with support vector machine and multi-Layer perceptron methods, the method proposed in this study provides more reliable porosity predictions, offering insights and references for the exploration and development of tight sandstone reservoirs.

Analysis of Vehicular Travel Time along the Urban Corridor Using Bluetooth Based Data

There has been a tremendous increase in vehicular growth in the last few decades, which has increased congestion in urban areas. Travel time is one of the measures to understand the congestion levels of the urban corridors. The increased use of Bluetooth devices by vehicular drivers may enable the dynamic data collection of travel time through Bluetooth sensors in urban areas. In this context, the objective of the present study is to evaluate the travel time of vehicles using Bluetooth devices and check the reliability of this Bluetooth technology for traffic studies. To fulfil the stated objective, a Bluetooth application was installed on a smartphone in order to collect the vehicular travel time on a selected corridor. A video camera was setup for traffic data collection at a mid-block section, which is used for the validation of the collected travel time data. The K nearest neighbor (KNN) model was developed with travel time (TT) data. The average TT of cars and other vehicles is estimated and found to be higher in the evening compared to the morning. From the developed KNN model, it is also found that the predicted TT for the next time intervals shows that they follow the trend of the actual travel time data obtained from the Bluetooth app. From the results of this study, it is feasible to estimate the travel time in urban areas using Bluetooth devices, which is useful for engineers and planners for suitable policy decision.

When linear inversion fails: Neural-network optimization for sparse-ray travel-time tomography of a volcanic edifice

In this study, we present an artificial neural network (ANN)-based approach for travel-time tomography of a volcanic edifice under sparse-ray coverage. We employ ray tracing to simulate the propagation of seismic waves through the heterogeneous medium of a volcanic edifice, and an inverse modeling algorithm that uses an ANN to estimate the velocity structure from the “observed” travel-time data. The performance of the approach is evaluated through a 2-dimensional numerical study that simulates i) an active source seismic experiment with a few (explosive) sources placed on one side of the edifice and a dense line of receivers placed on the other side, and ii) earthquakes located inside the edifice with receivers placed on both sides of the edifice. The results are compared with those obtained from conventional damped linear inversion. The average Root Mean Square Error (RMSE) between the input and output models is approximately 0.03 km/s for the ANN inversions, whereas it is about 0.4 km/s for the linear inversions, demonstrating that the ANN-based approach outperforms the classical approach, particularly in situations with sparse ray coverage. Our study emphasizes the advantages of employing a relatively simple ANN architecture in conjunction with second-order optimizers to minimize the loss function. Compared to using first-order optimizers, our ANN architecture shows a ∼25% reduction in RMSE. The ANN-based approach is computationally efficient. We observed that even though the ANN is trained based on completely random velocity models, it is still capable of resolving previously unseen anomalous structures within the edifice with about 5% anomalous discrepancies, making it a potentially valuable tool for the detection of low velocity anomalies related to magmatic intrusions or mush.

Estimating travel time in the Helsinki region utilising sequential Bayesian inference

This paper explores application of Bayesian inference (BI) to dynamic travel time estimation in the Helsinki region. Accurate travel time prediction is crucial in a wide range of fields, including departure time and routing. Limited real-time data challenge modelling accuracy. To address this, this paper utilises BI, particularly sequential Bayesian inference (SBI) for evolving observed values. Incorporating 2018 real-time data and 2015 information as prior knowledge, travel time distribution will be updated. Validation yields a 4.0% mean absolute error between the updated 2018 distribution and actual travel time. Also, using the 2015 posterior distribution as prior by way of SBI yields a 4.6% mean absolute error. Results highlight that SBI is an effective tool for updating distributions. This paper underscores potential of BI in addressing data scarcity and enhancing accuracy of transportation models. By supplying precise travel time estimates, this approach benefits congestion relief and travel planning. With evolving travel time data, BI promises to advance transportation modelling.

Discussion of sound propagation through the turbulent Martian atmosphere and implications for inference of turbulence spectra.

Chide et al. [J. Acoust. Soc. Am. 155, 420-435 (2024)] provide a first attempt to infer the spectrum of temperature fluctuations on Mars from experimental data on the variances of travel-time and log-amplitude fluctuations recorded by the microphone on board the Perseverance rover. However, the theoretical formulations that were used to interpret the travel-time data have limitations. In addition to explaining those issues, this article also outlines approaches for predicting statistical characteristics of acoustic signals in the Martian atmosphere. In particular, the experimentally observed dependence of the travel-time variance on the propagation range can be attributed to ground-blocking of buoyantly produced turbulent velocity fluctuations and the non-Markov character of phase fluctuations.

On the Footsteps of Active Faults from the Saronic Gulf to the Eastern Corinth Gulf: Application of Tomographic Inversion Using Recent Seismic Activity

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.

Evaluating temporal variations in access to multi-tier hospitals using personal vehicles and public transit: Implications for healthcare equity

Understanding healthcare accessibility, or the ability to access healthcare services, has significant implications for both individual well-being and community equity. However, existing studies seldom account for temporally varying factors such as traffic conditions and hospital schedules, resulting in miscalculation of accessibility. This study addresses this gap by introducing a framework that evaluates accessibility to multi-tier hospitals, factoring in both spatial and temporal aspects, using public transit (PT) and personal vehicles (PVs), and assesses its impact on horizontal and vertical equity. Implemented in Shanghai, China, we employ the Gaussian two-step floating catchment area method for accessibility quantification and utilize map APIs for dynamic travel time data. Our analysis reveals: (i) notable temporal fluctuations in healthcare accessibility, especially for PT, and their significant impact on both horizontal and vertical equity due to varying travel times and hospital schedules; (ii) larger disparities in higher-tier hospital accessibility compared to lower-tier ones; (iii) greater horizontal equity using PV-based accessibility and higher vertical equity using PT-based accessibility. These findings highlight the need to offer customized transit to healthcare facilities, expand telehealth services, incorporate equity in healthcare resource allocation, incentivize healthcare professionals to work in underserved areas, and develop outreach programs to improve accessibility and equity.

Bus Priority Procedure for Signalized Intersections Based on Bus Occupancy and Delay

This study proposes a bus prioritisation strategy at signalised intersections to enhance public transport reliability and attractiveness. Nowadays, bus prioritisation at intersections is conducted according to a first-come, first-served principle, lacking compatibility with future vehicle-to-infrastructure communication. A framework for prioritising buses based on their delay and occupancy was developed and tested in a SUMO microscopic traffic simulation subnetwork of the city of Ingolstadt. Buses are prioritised using a 25-level hierarchy. Four degrees of prioritisation interventions are implemented based on bus priority levels with signal cycles adjusted to advance preferred green phases. The timing of the prioritisation is based on an Estimated Time of Arrival (ETA) prediction that considers past speed and travel time data as well as bus stops that are on the way to the intersection. The prioritisation logic was tested in simulation scenarios with on- and off-peak conditions and with several buses requesting priority and varying degrees of priority. The results show that the developed prioritisation concept works, and prioritised buses benefit from a strong reduction in their travel times (up to 87 %) and number of stops. Buses with lower priority levels may experience deterioration in their travel time (up to 126 %) when arriving at the same time as a high-priority bus, but considering the fewer affected passengers and smaller delay, this seems acceptable.

Seis-PnSn: A Global Million-Scale Benchmark Data Set of Pn and Sn Seismic Phases for Deep Learning

Abstract The seismic phases Pn and Sn play a crucial role in investigating the velocity and anisotropic characteristics of the uppermost mantle. However, manually annotating these phases can be time-intensive and prone to subjective interpretation. Consequently, the use of travel-time data for these seismic phases remains limited. Despite the potential of deep learning to address this challenge, the scarcity of extensive training data sets for Pn and Sn presents significant constraints. To address this challenge, our research compiled a global million-scale benchmark data set of Pn and Sn seismic phases, namely Seis–PnSn. The data set is derived from earthquake events with epicenter distances ranging from 1.8° to 18°. The high-quality travel-time data used in this study are all from the International Seismological Centre and span the period 2000 to 2019. The waveform data were sourced from data centers located in different regions of the world under the International Federation of Digital Seismograph Networks. By leveraging the unique attributes of this data set, we trained baseline models and explored the prevailing challenges in deep-learning-based Pn and Sn phase picking as the scope transitions from local to regional epicenter distances. Our results show that the performance of the model is considerably enhanced after training on the proposed data set. Our study is a significant complement to the data foundation for future data-driven Pn and Sn seismic phase-picking studies, which will contribute to enhancing our understanding of the uppermost mantle structure of Earth, for example, the seismic velocity, anisotropy, and attenuation characteristics.

20-year permafrost evolution documented through petrophysical joint inversion, thermal and soil moisture data

This study investigates the ground characteristics of the high altitude (3410 m a.s.l.) permafrost site Stockhorn in the Swiss Alps using a combination of surface and subsurface temperature, soil moisture, electrical resistivity and P-wave velocity time series data including a novel approach to explicitly quantify changes in ground ice content. This study was motivated by the clear signal of permafrost degradation visible in the full dataset at this long-term monitoring site within the PERMOS (Permafrost Monitoring Switzerland) network. Firstly, we assess the spatio-temporal evolution of the ground ice and water content by a combined analysis of all available in situ thermal (borehole and ground surface temperature), hydrological (soil moisture) and geophysical (geoelectric and seismic refraction) data over two decades (2002–2022) regarding the driving factors for the spatially different warming. Secondly, we explicitly quantify the volumetric water and ice content and their changes in the subsurface from 2015 to 2022 using a time-consistent petrophysical joint inversion scheme within the open-source library pyGIMLi. The petrophysical joint inversion scheme has been improved by constraining the rock content to be constant in time for six subsequent inversions to obtain consistent changes in ice and water content over the monitoring period based on jointly inverted resistivity and traveltime data. All different data show a warming trend of the permafrost. The ice content modeled from the petrophysical joint inversion has decreased by about 15 vol.% between 2015 and 2022. Changes in ice content are first observed in the lower, south-facing part of the profile. As a result, resistivity and P-wave velocity have been decreasing significantly. Permafrost temperatures measured in the boreholes have increased between 0.5 °C and 1 °C in 20 years. Our study shows the high value of joint and quantitative analysis of datasets comprising complementary subsurface variables for long-term permafrost monitoring.

Weakly anelliptical traveltime analysis: Ambiguity between subsurface and elasticity

The building of a subsurface and (anisotropic) velocity model from a single gather of reflection traveltime (kinematic) data is inherently ambiguous because the processing of such data can only determine a horizontal slowness component, not a vertical one. Based thereon, I derive a simple algorithm that generates an infinite series of combinations of the subsurface-velocity models, all of which will show nearly the same seismic kinematic response, as further demonstrated by simulating wave propagation through a model with different interface dips. This algorithm assumes, first, that all interface dips remain constant over the distance considered and, second, that an approximation of the elasticity model — that is, the linearization of a phase velocity — valid for weak anisotropy can be used. Furthermore, when applied to the classic and analytically solvable case of traveltime analysis for a stack of flat layers with weak transverse isotropy, the algorithm theoretically explains the combination of anisotropy parameters that govern the nonhyperbolic term of a traveltime series: the established [Formula: see text] and its new counterpart [Formula: see text] for a [Formula: see text] and [Formula: see text] wave, respectively.