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A computational approach is adapted to analyze the parameter identifiability of a compartmental model. The model is intended to describe the progression of the COVID-19 pandemic in Chile during the initial phase in early 2020 when government declared quarantine measures. The computational approach to analyze the structural and practical identifiability is applied in two parts, one for synthetic data and another for some Chilean regional data. The first part defines the identifiable parameter sets when these recover the true parameters used to create the synthetic data. The second part compares the results derived from synthetic data, estimating the identifiable parameter sets from regional Chilean epidemic data. Experiments provide evidence of the loss of identifiability if some initial conditions are estimated, the period of time used to fit is before the peak, and if a significant proportion of the population is involved in quarantine periods. [ABSTRACT FROM AUTHOR]

The hypocenter parameters of an earthquake may give us an insight into the Earth's structure and tectonic processes. Among the hypocenter parameters, the focal depth is normally more difficult to estimate than the earthquake location (latitude and longitude). We propose to use the pPKIKP-PKIKP arrival time intervals for estimating the focal depth. We analyze the sensitivity of the pPKIKP-PKIKP arrival time interval to the earthquake depth. We measure the pPKIKP-PKIKP arrival time interval on seismograms (the vertical component), and invert the time interval data set using the simulated annealing inversion algorithm. We illustrate the inversion approach on two teleseismic earthquakes which have shallow and deep focal depths, and demonstrate that the approach is indeed appropriate to the shallow and deep event. We can obtain a reliable estimate on focal depth, even though the seismic station is sparse or in a remote part of the epicenter. [ABSTRACT FROM AUTHOR]

Energy consumption in the world is mainly dependent on fossil energy, and oil is one of the main energy sources. Accurate prediction of oil consumption can provide an important basis for national energy security, which can provide reference and early warning for the implementation of the environmental strategy developed by the government. According to the nonlinearity of the energy system, this paper uses the principle of the grey nonlinear prediction model NGBM(1,1) to improve the background value of the model, and by the simulated annealing algorithm, we put forward the optimized grey nonlinear model ONGBM(1,1). At the same time, the model is applied to the oil consumption of China, Chile, Mexico, and Japan. Based on the validity analysis of the existing data of the four countries, the model ONGBM(1,1) is basically superior to the other six grey forecast models. Finally, ONGBM(1,1) is used to predict the oil consumption of the four countries in the next five years, which can provide effective information for energy economic policy. [ABSTRACT FROM AUTHOR]

The traditional genetic algorithm and simulated annealing methods have been widely used in geophysical modeling. However, these nonlinear inversion methods require a lot of calculations, many control parameters and are unstable. In this paper, a particle swarm optimization algorithm combined with black hole strategy (BH-PSO) is proposed to solve these problems. The comprehensive experiments show that the BH-PSO method consumes less time than the simulated annealing (SA) method and has a higher accuracy than the genetic algorithm (GA). It is more applicable to the inversion of parameters of volcanic magma chamber, and easier to be generalized to other kinematic source parameters inversion. Based on BH-PSO method, Sentinel-1 data, composite dislocation model (CDM), Yang model and Mogi model, the magma chamber parameters of Calbuco eruption in 2015 were retrieved. The results show that the RMSE of CDM model is 1.1 cm, which can better fit the surface deformation than the Mogi model and Yang model. The final results show that the magma chamber is located about 0.8 km northeast of the crater, about 9 km below the surface, and the total volume of the erupted volcanic material obtained with the CDM Model is of 0.209 km3, without considering dense rock equivalent. [ABSTRACT FROM AUTHOR]