Sequential and Simultaneous Joint Inversion of Resistivity and IP Sounding Data Using Particle Swarm Optimization

In order to interpret the vertical electrical sounding data more reliably and effectively in the case of lacking proper priori information, two inverse schemes are proposed to invert combined re- sistivity and induced polarization data by using particle swarm optimization technique. Based on the computational formula of induced polarization, the inversion for chargeability/polarizability data can be transformed into inverting equivalent resistivity data. Then, the inversion for combined data can be decomposed into two procedures： inverting resistivity data and inverting equivalent resistivity data. A sequential inversion scheme is presented to run the two procedures sequentially. Contrast to the se- quential scheme, a simultaneous one is proposed to invert resistivity and induced polarization data si- multaneously. Both the sequential and simultaneous schemes are performed via centered-progressive particle swarm optimization algorithm for more exploratory purpose. Numerical experiments show that both the designed inversion algorithms can invert resistivity and induced polarization data suc- cessfully with fast convergence and high accuracy, even performed in a large search space. The inverse results are comparable to the results from generalized linear method. As an approximate importance sampler, the particle swarm optimization based algorithm can provide posterior analysis conveniently. We employ the posterior probability distributions of inverted model parameters to evaluate the per- formance and uncertainty of inversion. The posterior analysis and further field data testing show that the proposed inversion algorithms perform good sampling of the equivalence region and make sure that the global optimum can locate in the high probability areas.

Sensitivity analysis for the total nitrogen pollution of the Danjiangkou Reservoir based on a 3-D water quality model

Inter-basin water deal of nitrogen are great transfers containing a great threats to human health, biodiversity, and air and water quality in the recipient area. Danjiangkou Reservoir, the source reservoir for China＇s South-to-North Water Diversion Middle Route Project, suffers from total nitrogen pollution and threatens the water transfer to a number of metropolises including the capital, Beijing. To locate the main source of nitrogen pollution into the reservoir, especially near the Taocha canal head, where the intake of water transfer begins, we constructed a 3-D water quality model. We then used an inflow sensitivity analysis method to analyze the sig- nificance of inflows from each tributary that may contribute to the total nitrogen pollution and affect water quality. The results indicated that the Han River was the most significant river with a sensitivity index of 0.340, followed by the Dan River with a sensitivity index of 0.089, while the Guanshan River and the Lang River were not significant, with the sensitivity indices of 0.002 and 0.001, respectively. This result implies that the concentration and amount of nitrogen inflow outweighs the geographical position of the tributary for sources of total nitrogen pollution to the Taocha canal head of the Danjiangkou Reservoir.