Classical algorithms used for traveltime tomography are not necessarily well suited for handling very large seismic data sets or for taking advantage of current supercomputers. The classical approach of first-arrival traveltime tomography was revisited with the proposal of a simple gradient-based approach that avoids ray tracing and estimation of the Fréchet derivative matrix. The key point becomes the derivation of the gradient of the misfit function obtained by the adjointstate technique. The adjoint-state method is very attractive from a numerical point of view because the associated cost is equivalent to the solution of the forward-modeling problem, whatever the size of the input data and the number of unknown velocity parameters. An application on a 2D synthetic data set demonstrated the ability of the algorithm to image near-surface velocities with strong vertical and lateral variations and revealed the potential of the method.
The next European Space Agency (ESA) Earth Explorer mission BIOMASS will acquire Synthetic Aperture Radar (SAR) data to help characterizing carbon fluxes in densely vegetated areas. The ESA-sponsored AfriSAR campaign was designed to collect data from African tropical forests in order to support the future BIOMASS mission. It was conducted in two parts over the tropical forests of Gabon, by ONERA in July 2015 and by DLR in Febuary 2016. This paper addresses the potential of tomographic SAR for retrieving vegetation parameters from the multibaseline P-band airborne data acquired by ONERA over the forest of La Lopé. It is shown that a correction of phase disturbances (phase screens) is necessary before tomographic analysis. Under the hypothesis of phase screens resulting only from inaccurancies in the platform motion, a correction procedure based on recent works from Tebaldini et al. is detailed and applied. The tomographic profiles after correction are shown to present good correspondances with the available LIDAR data.
The determination of the correct velocity structure of the near surface is a crucial step in seismic data processing and depth imaging. Generally, first-arrival traveltime tomography based on refraction data or diving waves is used to assess a velocity model of the subsurface that best explains the data. Such first-arrival traveltime tomography algorithms are very attractive for land data processing because early events in the seismic records are very often dominated by noise, and reflected events are very difficult or even impossible to identify. On the other hand, first arrivals can generally be identified quite clearly and are very often the only data available to reconstruct the near-surface velocity structure.
Current urban mobility systems in Europe, characterized by high car mobility shares, have negative environmental and health impacts but struggle to mitigate these for fear of sacrificing accessibility. Ironically, before the car mobility transition (in the 1950s and 1960s in Western countries and the 1990s in Eastern Europe), most cities were accessible by walking, cycling, public transport, and by the few cars there were. Through a longitudinal case study of a medium-sized urban area in Clermont-Ferrand, France (1950–2022), this paper explores the potential to ‘de-transition’, i.e., to reverse the urban transition process towards ‘accessible, low-car cities’ by reshaping infrastructures to constrain car use whilst accommodating walking, cycling, and public transport. We answer the following questions: To what extent can cities reverse the urban car mobility transition? How could such a process be further encouraged? Our analysis adopts a social practices perspective and uses a mixed-methods approach by combining semi-structured interviews, a survey, and a document analysis. On the one hand, our findings highlight the difficulty of an urban modality shift to car alternatives: (1) the limited reach of public transformation networks (in Clermont-Ferrand, the tramline); (2) the fact that many feel unsafe or assume they need excellent health conditions to cycle, which is associated with leisure and sports; and (3) strong convictions concerning the usefulness of vehicle ownership, which is believed to maximise comfort. On the other hand, based on a historic analysis, we offer practical recommendations to de-transition to low-car urban areas: (1) the creation of an extensive regional tramway network; (2) the development of a full cycling network; and (3) the promotion of an extensive car-free city centre.
Classical 3-D refraction traveltime tomography algorithms may suffer from computational limitations due to the large datasets that come from current seismic acquisition surveys. To overcome these issues, we suggest a 3-D refraction tomography algorithm based on adjoint state techniques to derive the gradient of the traveltime misfit function. We use the Eikonal equation for the forward modelling, and iterate with a conjugate gradient method. A 3-D synthetic example with a realistic size acquisition demonstrates the efficiency and the great potential of the adjoint state method for 3-D applications of refraction tomography.
Standard refraction traveltime tomography based on ray tracing techniques has difficulties to handle large datasets that come from current seismic acquisition surveys. To overcome this problem we suggest a refraction tomography method based on adjoint state techniques to derive the gradient of the traveltime misfit function. We use the eikonal equation for the forward modelling, and iterate with a conjugate gradient method. Synthetic examples demonstrate the efficiency and the great potential of the method for 3D applications of refraction tomography.
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