Trajectory reconstruction from mobile positioning data using cell-to-cell travel time information

This paper proposes a technique for improving the accuracy of mobile device movement trajectory reconstruction using passive mobile positioning data. The major sources of uncertainty in trajectory reconstruction are imprecise cell shape data and ‘ping-pong’ effects caused by cell handovers. We used a novel technique for improved ‘ping-pong’ effect suppression by compensating for some cell shape distortions based on temporal cell-to-cell transit statistics. The results were evaluated by estimating traffic flow using trajectory reconstruction. The proposed technique improved the accuracy of results compared to ‘ping-pong’ suppression algorithms found in the literature.     

Two-dimensional non-linear inversion of VLF-R data using simulated annealing

The VLF-R (very low frequency-resistivity) data, i.e. the apparent resistivity ( ρ _a ) and phase ( φ ) data, were inverted individually and jointly using the VFSA (very fast simulated annealing) global inversion approach. Global inversion results for synthetic data without and with various amounts of random and normally distributed Gaussian noise reveal that the inversion of neither the ρ _a nor φ data alone yields the true parameters of the structures. However, the joint inversion of the ρ _a and φ data yields very good estimates of the model parameters. Five models, representing typical subsurface structures in the shield areas, are studied here. Various models achieved after 10 VFSA runs were used to compute the mean model and the corresponding covariance and correlation matrices, which were used to estimate the uncertainties in the mean model parameters and correlations between the model parameters. We observe that these correlations follow the physics associated with the problem. VLF-R field data due to a nearly vertical contact structure and a very thick dyke-like structure were also inverted to demonstrate the efficacy of the approach in the delineation of the parameters of 2-D structures.     

Non-linear inversion using general measures of data misfit and model structure

We investigate the use of general, non- l ₂ measures of data misfit and model structure in the solution of the non-linear inverse problem. Of particular interest are robust measures of data misfit, and measures of model structure which enable piecewise-constant models to be constructed. General measures can be incorporated into traditional linearized, iterative solutions to the non-linear problem through the use of an iteratively reweighted least-squares (IRLS) algorithm. We show how such an algorithm can be used to solve the linear inverse problem when general measures of misfit and structure are considered. The magnetic stripe example of Parker (1994 ) is used as an illustration. This example also emphasizes the benefits of using a robust measure of misfit when outliers are present in the data. We then show how the IRLS algorithm can be used within a linearized, iterative solution to the non-linear problem. The relevant procedure contains two iterative loops which can be combined in a number of ways. We present two possibilities. The first involves a line search to determine the most appropriate value of the trade-off parameter and the complete solution, via the IRLS algorithm, of the linearized inverse problem for each value of the trade-off parameter. In the second approach, a schedule of prescribed values for the trade-off parameter is used and the iterations required by the IRLS algorithm are combined with those for the linearized, iterative inversion procedure. These two variations are then applied to the 1-D inversion of both synthetic and field time-domain electromagnetic data.     

Measuring place-based accessibility under travel time uncertainty

Travel time uncertainty has significant impacts on individual activity-travel scheduling, but at present these impacts have not been considered in most accessibility studies. In this paper, an accessibility evaluation framework is proposed for urban areas with uncertain travel times. A reliable space-time service region (RSTR) model is introduced to represent the space-time service region of a facility under travel time uncertainty. Based on the RSTR model, four reliable place-based accessibility measures are proposed to evaluate accessibility to urban services by incorporating the effects of travel time reliability. To demonstrate the applicability of the proposed framework, a case study using large-scale taxi tracking data is carried out. The results of the case study indicate that the proposed accessibility measures can evaluate large-scale place-based accessibility well in urban areas with uncertain travel times. Conventional place-based accessibility indicators ignoring travel time reliability can significantly overestimate the accessibility to urban services.     

Simultaneous inversion of seismic data

Summary. The resolving power of different data sets, consisting of surface-wave dispersion measurements and S travel times, are compared for a continental structure. The shear velocity in the low-velocity zone can be resolved in some detail with higher-mode phase-velocity data. Sufficient resolution for small density contrasts (0.03 g cm⁻³) until depths of ∼ 300 km can be reached if higher-mode group velocities are available as well, even at a precision as low as 0.10 km/s. At greater depths the density is not resolved, and here travel-time data are superior to higher modes in resolving the shear velocity.     

CMT data inversion using a Bayesian information criterion to estimate seismogenic stress fields

We developed an inversion method to estimate the stress fields related to earthquake generation (seismogenic stress fields) from the centroid moment tensors (CMT) of seismic events by using Akaike's Bayesian information criterion (ABIC). On the idea that the occurrence of an earthquake releases some part of the seismogenic stress field around its hypocentre, we define the CMT of a seismic event by a weighted volume integral of the true but unknown seismogenic stress field. Representing each component of the seismogenic stress field by the superposition of a finite number of 3-D basis functions (tri-cubic B-splines), we obtain a set of linear observation equations to be solved for the expansion coefficients (model parameters). We introduce prior constraint on the roughness of the seismogenic stress field and combine it with observed data to construct a Bayesian model with hierarchic, highly flexible structure controlled by hyper-parameters. The optimum values of the hyper-parameters are objectively determined form observed data by using ABIC. Given the optimum values of the hyper-parameters, we can obtain the best estimates of model parameters by using a maximum likelihood algorithm. We tested the validity of the inversion method through numerical experiments on two synthetic CMT data sets, assuming the distribution of fault orientations to be aligned with the maximum shear stress plane in one case and to be random in the other case. Then we applied the inversion method to actual CMT data in northeast Japan, and obtained the pattern of the seismogenic stress field consistent with geophysical and geological observations.