Construction of Ray Synthetic Seismograms Using Interpolation of Travel Times and Ray Amplitudes

1996 ◽  
pp. 503-538
Author(s):  
Johana Brokešová
Keyword(s):  
Synthetic Seismograms ◽  
Travel Times

On the synthesis of shear-coupled PL waves

1968 ◽  
Vol 58 (6) ◽  
pp. 1849-1877 ◽  
Author(s):  
Ramesh Chander ◽  
L. E. Alsop ◽  
Jack Oliver
Keyword(s):  
United States ◽  
Phase Velocity ◽  
Travel Time ◽  
Current Knowledge ◽  
Shear Waves ◽  
The United States ◽  
Synthetic Seismograms ◽  
Travel Times ◽  
Precise Knowledge ◽  
S Curve

ABSTRACT Using the shear-coupled PL wave hypothesis of Oliver as a basis, a method is developed for computing synthetic long-period seismograms between the onset of the initial S-type body phase and the beginning of surface waves. Comparison of observed and synthetic siesmograms shows that this hypothesis can explain, in considerable detail, most of the waves with periods greater than about 20 sec recorded during this interval. The synthetic seismograms are computed easily on a small digital computer; they resemble the observed seismograms much more closely than the synthetic seismograms obtained through the superposition of normal modes of the Earth that have been reported in the literature. The synthesis of shear-coupled PL waves depends on a precise knowledge of the phase-velocity curve of the PL wave and travel-time curves of shear waves. Hence, in principle, if one of these quantities is well-known the other can be determined by this method. Phase-velocity curves of the PL wave are determined for the Baltic shield, the Russian platform, the Canadian shield, the United States, and the western North-Atlantic ocean, on the assumption that J-B travel-time curves of shear waves apply to these areas. These dispersion curves show the type of variations to be expected on the basis of the current knowledge of the crustal structures in these areas. Examples are presented to show that J-B travel-times of shear waves along paths between Kenai Peninsula, Alaska and Palisades, equatorial mid-Atlantic ridge and Palisades, and Kurile Islands and Uppsala need to be revised. Shear-wave travel-time curves that are not unique for reasons explained in the study but that give synthetic seismograms in agreement with the observed seismograms were obtained. The new S curves are compared with the J-B travel-time curves for S; and they all predict S waves to arrive later than the time given by J-B tables for epicentral distances smaller than about 30°. The new S curve for the Alaska to Palisades path appears to agree with one of the branches of a multi-branched S curve proposed recently by Ibrahim and Nuttli for the ‘average United States’ insofar as travel-times are concerned, but there are some differences in the slopes of the two curves.

Travel times and amplitudes of waves in central asia—Theory and experiments

1966 ◽  
Vol 10 (2) ◽  
pp. 172-176
Author(s):  
A. S. Alekseev ◽  
I. L. Nersesov
Keyword(s):  
Central Asia ◽  
Travel Times ◽  
Theory And Experiments

Optimization of single-line bus timetables considering time-dependent travel times: A case study of Beijing, China

2021 ◽  
pp. 107444
Author(s):  
Wenyi Zhang ◽  
Dongyang Xia ◽  
Tao Liu ◽  
Yanjia Fu ◽  
Jihui Ma
Keyword(s):  
Time Dependent ◽  
Single Line ◽  
Travel Times ◽  
Beijing China

scholarly journals “In cities, it’s not far, but it takes long”: comparing estimated and replicated travel times to reach life-saving obstetric care in Lagos, Nigeria

2021 ◽  
Vol 6 (1) ◽  
pp. e004318
Author(s):  
Aduragbemi Banke-Thomas ◽  
Kerry L M Wong ◽  
Francis Ifeanyi Ayomoh ◽  
Rokibat Olabisi Giwa-Ayedun ◽  
Lenka Benova
Keyword(s):  
Travel Time ◽  
Friction Surface ◽  
Obstetric Care ◽  
Travel Times ◽  
Night Time ◽  
Google Maps ◽  
Surface Approach ◽  
Low Resource ◽  
Life Saving ◽  
The Cost

BackgroundTravel time to comprehensive emergency obstetric care (CEmOC) facilities in low-resource settings is commonly estimated using modelling approaches. Our objective was to derive and compare estimates of travel time to reach CEmOC in an African megacity using models and web-based platforms against actual replication of travel.MethodsWe extracted data from patient files of all 732 pregnant women who presented in emergency in the four publicly owned tertiary CEmOC facilities in Lagos, Nigeria, between August 2018 and August 2019. For a systematically selected subsample of 385, we estimated travel time from their homes to the facility using the cost-friction surface approach, Open Source Routing Machine (OSRM) and Google Maps, and compared them to travel time by two independent drivers replicating women’s journeys. We estimated the percentage of women who reached the facilities within 60 and 120 min.ResultsThe median travel time for 385 women from the cost-friction surface approach, OSRM and Google Maps was 5, 11 and 40 min, respectively. The median actual drive time was 50–52 min. The mean errors were >45 min for the cost-friction surface approach and OSRM, and 14 min for Google Maps. The smallest differences between replicated and estimated travel times were seen for night-time journeys at weekends; largest errors were found for night-time journeys at weekdays and journeys above 120 min. Modelled estimates indicated that all participants were within 60 min of the destination CEmOC facility, yet journey replication showed that only 57% were, and 92% were within 120 min.ConclusionsExisting modelling methods underestimate actual travel time in low-resource megacities. Significant gaps in geographical access to life-saving health services like CEmOC must be urgently addressed, including in urban areas. Leveraging tools that generate ‘closer-to-reality’ estimates will be vital for service planning if universal health coverage targets are to be realised by 2030.

Estimation of Path Travel Time Distributions in Stochastic Time-Varying Networks with Correlations

2021 ◽  
pp. 036119812110184
Author(s):  
Monika Filipovska ◽  
Hani S. Mahmassani ◽  
Archak Mittal
Keyword(s):  
Time Dependence ◽  
Travel Time ◽  
Correlation Structure ◽  
Time Varying ◽  
Travel Times ◽  
Trajectory Data ◽  
Time Varying Networks ◽  
Transportation Research ◽  
Search Approach ◽  
Stochastic Time

Transportation research has increasingly focused on the modeling of travel time uncertainty in transportation networks. From a user’s perspective, the performance of the network is experienced at the level of a path, and, as such, knowledge of variability of travel times along paths contemplated by the user is necessary. This paper focuses on developing approaches for the estimation of path travel time distributions in stochastic time-varying networks so as to capture generalized correlations between link travel times. Specifically, the goal is to develop methods to estimate path travel time distributions for any path in the networks by synthesizing available trajectory data from various portions of the path, and this paper addresses that problem in a two-fold manner. Firstly, a Monte Carlo simulation (MCS)-based approach is presented for the convolution of time-varying random variables with general correlation structures and distribution shapes. Secondly, a combinatorial data-mining approach is developed, which aims to utilize sparse trajectory data for the estimation of path travel time distributions by implicitly capturing the complex correlation structure in the network travel times. Numerical results indicate that the MCS approach allowing for time-dependence and a time-varying correlation structure outperforms other approaches, and that its performance is robust with respect to different path travel time distributions. Additionally, using the path segmentations from the segment search approach with a MCS approach with time-dependence also produces accurate and robust estimates of the path travel time distributions with the added benefit of shorter computation times.

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