Coordinate Search Algorithm Research Articles

Optimal experiment design for time-lapse traveltime tomography

Geophysical monitoring techniques offer the only noninvasive approach capable of assessing the spatial and temporal dynamics of subsurface fluid processes. Increasingly, permanent sensor arrays in boreholes and on the ocean floor are being deployed to improve the repeatability and increase the temporal sampling of monitoring surveys. Because permanent arrays require a large up-front capital investment and are difficult (or impossible) to reconfigure once installed, a premium is placed on selecting a geometry capable of imaging the desired target at minimum cost. We have taken a simple approach to optimizing downhole sensor configurations for monitoring experiments making use of differential seismic traveltimes. We used a design quality metric based on the accuracy of tomographic reconstructions for a suite of imaging targets. By not requiring an explicit singular value decomposition of the forward operator, evaluation of this objective function scaled to problems with a large numberof unknowns. We restricted the design problem by recasting the array geometry into a low-dimensional form more suitable for optimization at a reasonable computational cost. We tested two search algorithms on the design problem: the Nelder-Mead downhill simplex method and the multilevel coordinate search algorithm. The algorithm was tested for four crosswell acquisition scenarios relevant to continuous seismic monitoring, a two-parameter array optimization, several scenarios involving four-parameter length/offset optimizations, and a comparison of optimal multisource designs. In the last case, we also examined trade-offs between source sparsity and the quality of tomographic reconstructions. Asymmetrical array lengths improved localized image quality in crosswell experiments with a small number of sources and a large number of receivers. Preliminary results also suggested that high-quality differential images could be generated using only a small number of optimally positioned sources in tandem with a more extensive receiver array.

Joint Dynamics Modeling and Parameter Identification for Space Robot Applications

Long-term mission identification and model validation for in-flight manipulator control system in almost zero gravity with hostile space environment are extremely important for robotic applications. In this paper, a robot joint mathematical model is developed where several nonlinearities have been taken into account. In order to identify all the required system parameters, an integrated identification strategy is derived. This strategy makes use of a robust version of least-squares procedure (LS) for getting the initial conditions and a general nonlinear optimization method (MCS—multilevel coordinate search—algorithm) to estimate the nonlinear parameters. The approach is applied to the intelligent robot joint (IRJ) experiment that was developed at DLR for utilization opportunity on the International Space Station (ISS). The results using real and simulated measurements have shown that the developed algorithm and strategy have remarkable features in identifying all the parameters with good accuracy.

Laboratory evaluation of a hydrodynamic inverse modeling method based on water content data

The inverse modeling method of Lambot et al. [2002] for estimating the hydraulic properties of partially saturated soils, which was numerically validated, is further tested on laboratory‐scale transient flow experiments. The method uses the global multilevel coordinate search algorithm combined sequentially with the local Nelder‐Mead simplex algorithm to obtain the inverse of the one‐dimensional Richards equation using soil moisture time series measured at three different depths during natural infiltration. Flow experiments were conducted on a homogeneous artificial sand column and three undisturbed soil columns collected from agricultural fields. Three models describing the unsaturated soil hydraulic properties were used and compared: the model of Mualem and van Genuchten, the model of Assouline, and the decoupled van Genuchten–Brooks and Corey combination. The performances of all three models were similar, except for Assouline's model, which provided poorer results in two cases. The inversion method provided relatively good estimates for the water retention curves and also for the saturated conductivity when the moisture range explored was not too small. Water content time series were very well reproduced for the artificial soil and a sandy loam soil, but for two silt loam soils, larger errors were observed. The prediction of the water transfer behavior in the soil columns was poor when flow properties were estimated using directly determined hydraulic properties. The main limiting factor for applying the inversion method, particularly for nonsandy soils, was the characterization of the initial conditions in terms of the pressure head profile. Furthermore, the use of only soil moisture data is essential to enable the hydrogeophysical characterization of soils.