Seismic comprehensive forecast based on modified project pursuit regression

In the research of projection pursuit for seismic comprehensive forecast, the algorithm of projection pursuit regression （PPR） is one of most applicable methods. But generally, the algorithm structure of the PPR is very complicated. By partial smooth regressions for many times, it has a large amount of calculation and complicated extrapolation, so it is easily trapped in partial solution. On the basis of the algorithm features of the PPR method, some solutions are given as below to aim at some shortcomings in the PPR calculation： to optimize project direction by using particle swarm optimization instead of Gauss-Newton algorithm, to simplify the optimal process with fitting ridge function by using Hermitian polynomial instead of piecewise linear regression. The overall optimal ridge function can be obtained without grouping the parameter optimization. The modeling capability and calculating accuracy of projection pursuit method are tested by means of numerical emulation technique on the basis of particle swarm optimization and Hermitian polynomial, and then applied to the seismic comprehensive forecasting models of poly-dimensional seismic time series and general disorder seismic samples. The calculation and analysis show that the projection pursuit model in this paper is characterized by simplicity, celerity and effectiveness. And this model is approved to have satisfactory effects in the real seismic comprehensive forecasting, which can be regarded as a comprehensive analysis method in seismic comprehensive forecast.

Robust Tracking Control of Uncertain MIMO Nonlinear Systems with Application to UAVs

In this paper, we consider the robust adaptive tracking control of uncertain multi-input and multi-output(MIMO) nonlinear systems with input saturation and unknown external disturbance. The nonlinear disturbance observer(NDO)is employed to tackle the system uncertainty as well as the external disturbance. To handle the input saturation, an auxiliary system is constructed as a saturation compensator. By using the backstepping technique and the dynamic surface method,a robust adaptive tracking control scheme is developed. The closed-loop system is proved to be uniformly ultimately bounded thorough Lyapunov stability analysis. Simulation results with application to an unmanned aerial vehicle(UAV) demonstrate the effectiveness of the proposed robust control scheme.

Adaptive fuzzy controller for a class of strict-feedback nonaffine nonlinear systems

A robust adaptive fuzzy control scheme is presented for a class of strict-feedback nonaffine nonlinear systems with modeling uncertainties and external disturbances by using a backstepping approach.Fuzzy logic systems are employed to approximate the unknown parts of the desired virtual controls,and the approximation errors of fuzzy systems are only required to be norm-bounded.The function tanh（·） is introduced to avoid problems associated with sgn（·）.The tracking error is guaranteed to be uniformly ultimately bounded with the aid of an additional adaptive compensation term.Chua＇s circuit system and R o¨ssler system are presented to illustrate the feasibility and effectiveness of the proposed control technique.

Research status of earthquake forecasting in hydraulicfracturing induced earthquakes

In the new types of industrial activities including unconventional energy extraction associated with shale gas and hot dry rock,gas reservoir operations,CO2 geological storage,undergoing research on induced earthquake forecasting has become one of the forward positions of current seismology.As for the intense actual demand,the immature research on induced earthquake forecasting has already been applied in pre-assessment of site safety and seismic hazard and risk management.This work will review systematically recent advances in earthquake forecasting induced by hydraulic fracturing during industrial production from four aspects:earthquake occurrence probability,maximum expected magnitude forecasting,seismic risk analysis for engineering and social applications and key scientific problems.In terms of earthquake occurrence probability,we introduce statistical forecasting models such as an improved ETAS and non-stationary ETAS and physical forecasting models such as Seismogenic Index(SI)and hydro-mechanism nucleation.Research on maximum expected magnitude forecasting has experienced four stages of linear relationship with net injection volume of fluid,power exponential relationship and physical forecasting regarding fault parameters.For seismic risk analysis,we focus on probabilistic seismic hazard assessment and quantitative geological susceptibility model.Furthermore,this review is extended to key scientific problems that contain obtaining accurate fault scale and environmental stress state of reservoir,critical physical process of runaway rupture,complex mechanism of fault activation as well as physical mechanism and modeling of trailing effect.This work in understanding induced earthquake forecasting may contribute to unconventional energy development and production,seismic hazard mitigation,emergency management and scientific research as a reference.

Adaptive integral dynamic surface control based on fully tuned radial basis function neural network

An adaptive integral dynamic surface control approach based on fully tuned radial basis function neural network （FTRBFNN） is presented for a general class of strict-feedback nonlinear systems,which may possess a wide class of uncertainties that are not linearly parameterized and do not have any prior knowledge of the bounding functions.FTRBFNN is employed to approximate the uncertainty online,and a systematic framework for adaptive controller design is given by dynamic surface control. The control algorithm has two outstanding features,namely,the neural network regulates the weights,width and center of Gaussian function simultaneously,which ensures the control system has perfect ability of restraining different unknown uncertainties and the integral term of tracking error introduced in the control law can eliminate the static error of the closed loop system effectively. As a result,high control precision can be achieved.All signals in the closed loop system can be guaranteed bounded by Lyapunov approach.Finally,simulation results demonstrate the validity of the control approach.

Temporal variation of noise correlation function(NCF) in Beijing and surroundings:Its relation with climate events and implications

Noise correlation function （NCF） was calculated using the data of the Beijing Capital-Area Telemetered Digital Seismograph Network from June 12 to September 12, 2005. Signal-to-noise ratio （SNR） is used to characterize the quality of NCF at each station pair. The SNR （in dB） is shown to be dependent on the separation distance R of the station pair via SNR= A -BlogR. ＇Normalized average SNR＇ for all the station pairs can then be calculated, as represented by the value of SNR taking R = 250 km in the empirical SNR-R relation, to measure the overall quality of the NCF result. The ＇normalized average SNR＇ of the NCF shows temporal variation and is apparently dependent on the root-mean-square （RMS） velocity of the microseism. The result obtained by this experiment provides clues to the explanation of the properties of NCF, such as the dominant mechanism underlying （diffuse wave fields or uncorrelated sources）, and the dependence of SNR on the time length of recordings.

Properties of three seismic events in September 2017 in the northern Korean Peninsula from moment tensor inversion

In September 2017 three noticeable seismic events occurred in the northern Korean Peninsula(Fig.1;Table 1).Democratic People’s Republic of Korea(DPRK)authorities announced that the first M6.3 event on September 3rd was a‘‘successfully conducted hydrogen-bomb test".The M4.6 event,some 8 min followed,was regarded as an implosion.However,it is still not clear

Retrospective forecasting test of a statistical physics model for earthquakes in Sichuan-Yunnan region

Pattern informatics (PI) model is one of the recently developed predictive models of earthquake phys- ics based on the statistical mechanics of complex systems. In this paper, retrospective forecast test of the PI model was conducted for the earthquakes in Sichuan-Yunnan region since 1988, exploring the possibility to apply this model to the estimation of time-dependent seismic hazard in continental China. Regional earthquake catalogue down to ML3.0 from 1970 to 2007 was used. The ‘target magnitude’ for the forecast test was MS5.5. Fifteen-year long ‘sliding time window’ was used in the PI calculation, with ‘anomaly training time window’ being 5 years and ‘forecast time window’ being 5 years, respectively. Receiver operating characteristic (ROC) test was conducted for the evaluation of the forecast result, showing that the PI forecast outperforms not only random guess but also the simple number counting approach based on the clustering hypothesis of earthquakes (the RI forecast). If the ‘forecast time window’ was shortened to 3 years and 1 year, respectively, the forecast capability of the PI model de- creased significantly, albeit outperformed random forecast. For the one year ‘forecast time window’, the PI result was almost comparable to the RI result, indicating that clustering properties play a more important role at this time scale.

Characterization of submicron particles during autumn in Beijing,China

In this study, we performed a highly time-resolved chemical characterization of nonrefractory submicron particles（NR-PM_1） in Beijing by using an Aerodyne high-resolution time-of-flight aerosol mass spectrometer（HR-ToF-AMS）. The results showed the average NR-PM_1 mass concentration to be 56.4 ± 58.0 μg/m^3, with a peak at 307.4 μg/m^3. Due to the high frequency of biomass burning in autumn, submicron particles significantly increased in organic content, which accounted for 51% of NR-PM_1 on average. Secondary inorganic aerosols（sulfate ＋ nitrate ＋ ammonium） accounted for 46% of NR-PM_1, of which sulfate,nitrate, and ammonium contributed 15%, 20%, and 11%, respectively. To determine the intrinsic relationships between the organic and inorganic species, we used the positive matrix factorization（PMF） model to merge the high-resolution mass spectra of the organic species and NO＋and NO_2~＋ions. The PMF analysis separated the mixed organic and nitrate（NO＋and NO_2~＋） spectra into four organic factors, including hydrocarbon-like organic aerosol（HOA）, oxygenated organic aerosol（OOA）, cooking organic aerosol（COA）, and biomass burning organic aerosol（BBOA）, as well as one nitrate inorganic aerosol（NIA） factor. COA（33%） and OOA（30%） contributed the most to the total organic aerosol（OA） mass, followed by BBOA（20%） and HOA（17%）. We successfully quantified the mass concentrations of the organic and inorganic nitrates by the NO＋and NO2＋ions signal in the organic and NIA factors. The organic nitrate mass varied from 0.01-6.8 μg/m^3, with an average of 1.0 ±1.1 μg/m^3, and organic nitrate components accounted for 10% of the total nitrate mass in this observation.

Delay-dependent robust H_(∞) controller design for a class of nonlinear uncertainty time-delay systems with input delay

Based on an appropriate Lyapunov function,this paper analyzes the design of a delay-dependent robust H_(∞)state feedback control,with a focus on a class of non-linear uncertainty linear time-delay systems with input delay using linear matrix inequalities.Under the condition that the nonlinear uncertain functions are gain bounded,a sufficient condition dependent on the delays of the state and input is presented for the existence of H_(∞)controller.The proposed controller not only stabilized closed-loop uncertain systems but also guaranteed a prescribed H_(∞)norm bound of closed-loop transfer matrix from the disturbance to controlled output.By solving a linear matrix inequation,we can obtain the robust H_(∞)controller.An example is given to show the effectiveness of the proposed method.