Application of the χ2\chi^2 principle and unbiased predictive risk estimator for determining the regularization parameter in 3D focusing gravity inversion

The $\chi^2$ principle and the unbiased predictive risk estimator are used to determine optimal regularization parameters in the context of 3D focusing gravity inversion with the minimum support stabilizer. At each iteration of the focusing inversion the minimum support stabilizer is determined and then the fidelity term is updated using the standard form transformation. Solution of the resulting Tikhonov functional is found efficiently using the singular value decomposition of the transformed model matrix, which also provides for efficient determination of the updated regularization parameter each step. Experimental 3D simulations using synthetic data of a dipping dike and a cube anomaly demonstrate that both parameter estimation techniques outperform the Morozov discrepancy principle for determining the regularization parameter. Smaller relative errors of the reconstructed models are obtained with fewer iterations. Data acquired over the Gotvand dam site in the south-west of Iran are used to validate use of the methods for inversion of practical data and provide good estimates of anomalous structures within the subsurface

Automatic estimation of the regularization parameter in 2-D focusing gravity inversion: an application to the Safo manganese mine in northwest of Iran

We investigate the use of Tikhonov regularization with the minimum support stabilizer for underdetermined 2-D inversion of gravity data. This stabilizer produces models with non-smooth properties which is useful for identifying geologic structures with sharp boundaries. A very important aspect of using Tikhonov regularization is the choice of the regularization parameter that controls the trade off between the data fidelity and the stabilizing functional. The L-curve and generalized cross validation techniques, which only require the relative sizes of the uncertainties in the observations are considered. Both criteria are applied in an iterative process for which at each iteration a value for regularization parameter is estimated. Suitable values for the regularization parameter are successfully determined in both cases for synthetic but practically relevant examples. Whenever the geologic situation permits, it is easier and more efficient to model the subsurface with a 2-D algorithm, rather than to apply a full 3-D approach. Then, because the problem is not large it is appropriate to use the generalized singular value decomposition for solving the problem efficiently. The method is applied on a profile of gravity data acquired over the Safo mining camp in Maku-Iran, which is well known for manganese ores. The presented results demonstrate success in reconstructing the geometry and density distribution of the subsurface source

TKA patients with unsatisfying knee function show changes in neuromotor synergy pattern but not joint biomechanics

Nearly 20% of patients who have undergone total knee arthroplasty (TKA) report persistent poor knee function. This study explores the idea that, despite similar knee joint biomechanics, the neuro-motor synergies may be different between high-functional and low-functional TKA patients. We hypothesized that (1) high-functional TKA recruit a more complex neuro-motor synergy pattern compared to low-functional TKA and (2) high-functional TKA patients demonstrate more stride-to-stride variability (flexibility) in their synergies. Gait and electromyography (EMG) data were collected during level walking for three groups of participants: (i) high-functional TKA patients (n = 13); (ii) low-functional TKA patients (n = 13) and (iii) non-operative controls (n = 18). Synergies were extracted from EMG data using non-negative matrix factorization. Analysis of variance and Spearman correlation analyses were used to investigate between-group differences in gait and neuro-motor synergies. Results showed that synergy patterns were different among the three groups. Control subjects used 5–6 independent neural commands to execute a gait cycle. High functional TKA patients used 4–5 independent neural commands while low-functional TKA patients relied on only 2–3 independent neural commands to execute a gait cycle. Furthermore, stride-to-stride variability of muscles’ response to the neural commands was reduced up to 15% in low-functional TKAs compared to the other two groups

Ecological changes in historically polluted soils: Metal(loid) bioaccumulation in microarthropods and their impact on community structure

International audienceSoil pollution by persistent metal(loid)s present environmental and sanitary risks. While the effects of metal(loid)s on vegetation and macrofauna have been widely studied, their impact on microarthropods (millimetre scale) and their bioaccumulation capacity have been less investigated. However, microarthropods provide important ecosystem services, contributing in particular to soil organic matter dynamics. This study focussed on the impact of metal(loid) pollution on the structure and distribution of microarthropod communities and their potential to bioaccumulate lead (Pb). Soil samples were collected from a contaminated historical site with a strong horizontal and vertical gradient of Pb concentrations. Microarthropods were extracted using the Berlese method. The field experiments showed that microarthropods were present even in extremely polluted soils (30,000 mg Pb kg− 1). However, while microarthropod abundance increased with increasing soil C/N content (R2 = 0.79), richness decreased with increasing pollution. A shift in the community structure from an oribatid-to a springtail-dominated community was observed in less polluted soils (R2 = 0.68). In addition, Pb bioamplification occurred in microarthropods, with higher Pb concentrations in predators than in detritivorous microarthropods. Finally, the importance of feeding and reproductive ecological traits as potentially relevant descriptors of springtail community structures was highlighted. This study demonstrates the interest of microarthropod communities with different trophic levels and ecological features for evaluating the global environmental impact of metal(loid) pollution on soil biological quality