Maximum Entropy Spectral Method Research Articles

Periodic spectral characteristics of seismicity before strong earthquakes and their application

Periodic spectral characteristics of earthquake activity in the seismic strengthening areas of 24 earthquakes with M≥6.0 are studied by the maximum entropy spectral method whose superiority is tested. Then the follow results have been obtained: (1) The periodic spectra of seismic activity in seismic strengthening areas are different in different stage in earthquake-generating processes. Long periodic spectra and short ones coexist in normal stage, while only short ones (on average, 43% of long ones) exist and long ones disappear prior to earthquakes. (2) The appearing time of short period before earthquakes has some relations with magnitude. The result shows that decades or even one hundred years is the common value for a great earthquake of M=8.0, 30 years for one with magnitude about 7 and 20–30 years for a strong quake of M=6.0. For the same magnitude earthquakes in different regions the appearing time is also different. For example, it is longer in North China than that in the western part of China. Then the characteristics are preliminarily explained applying the strong body earthquake-generating model. Applying the maximum entropy spectral method, the idea of tendency prediction for strong and great earthquakes is suggested and used into practice, for example, the tendency predictions of the Wuding earthquake with M=6.5 and the Lijiang earthquake of M=7.0 in Yunnan Province got some positive effects. So a new method of tendency prediction of M>-6.0 earthquakes is offered.

Estimating fractal dimension of profiles: A comparison of methods

This paper examines the characteristics of four different methods of estimating the fractal dimension of profiles. The semi-variogram, roughness-length, and two spectral methods are compared using synthetic 1024-point profiles generated by three methods, and using two profiles derived from a gridded DEM and two profiles from a laser-scanned soil surface. The analysis concentrates on the Hurst exponent H,which is linearly related to fractal dimension D,and considers both the accuracy and the variability of the estimates of H.The estimation methods are found to be quite consistent for Hnear 0.5, but the semivariogram method appears to be biased for Happroaching 0 and 1, and the roughness-length method for Happroaching 0. The roughness-length or the maximum entropy spectral methods are recommended as the most suitable methods for estimating the fractal dimension of topographic profiles. The fractal model fitted the soil surface data at fine scales but not at broad scales, and did not appear to fit the DEM profiles well at any scale.

Damping ratio estimation using hybrid methods

Much effort has been expended in attempts to estimate dynamic response parameters of structures. Damping ratios have been estimated with varying degrees of success. Some of this effort has been motivated by active research and development of structural integrity monitoring systems, for design purposes, accurate estimates of damping ratios are important. In this work we apply several modern spectral analysis methods (maximum likelihood method, maximum entropy method and the Lagunas-Hernandez method) plus the spectral moment relations near natural frequency to the estimation of damping ratios. We use simulated vibration data and test the different choices of the spectral method in order to obtain the best estimation procedure. The spectral shape of the response near the natural frequency is estimated first as the basis for further analysis. Half-power bandwidth and spectral moments are then derived from the spectral shape. The bandwidth and moments are subsequently related to the damping ratio. An iterated method for obtaining the damping ratio from the spectral moments has been proposed by Shyam Sunder. The half-power bandwidth and moments are crucial to this method as is the estimation of the spectral shape, there are many methods available to estimate spectral shape. Different combinations of the two methods gives varied damping ratios, in this work we try to identify the best combinations. We have used many sets of exact and simulated data and applies many different combinations. Results show that the maximum entropy spectral method and the method of Shyam Sunder is the best combination from our tests.

A Comparison of Traditional Fourier and Maximum Entropy Spectral Methods for Vibration Analysis

The structural vibrations monitored on a process plant often contain burst phenomena where several discrete frequencies are present. Spectral analysis of these vibration signals by Fourier transform methods is generally considered incapable of resolving discrete frequencies close together in a short time slice. The power spectral density estimates computed by traditional Fourier transform methods are compared with those computed by more recent maximum entropy spectral analysis (MESA) methods for both data from a test system where the outputs of two tuned filters, excited by pseudo-random binary noise, are summed, and the vibration signals from an accelerometer mounted on a die in an extrusion process. The frequency resolution characteristics of the two methods are compared, and the problems associated with the choice of an optimum order autoregressive prediction error filter for the MESA method are discussed.

An analysis of Manley's central England temperature data: I

AbstractManley's (1974) temperature data for central England are subjected to analyses which suggest that they should be treated as a set of monthly time series. Secular variation is found for the months October to April inclusive. Extended methods dealing with spectral analysis are used to focus attention on the mainly longer‐period oscillations. Oscillations of lengths 10–12 and 22–25 years are suggested for some months. Burg's maximum entropy spectral method has been used to resolve signals in the very low frequencies. The combined variance associated with these oscillations is shown to increase for months near mid‐year when the sun has its maximum north declination and to decrease for months on either side of June and July to a minimum when the sun has greatest south declination. Furthermore, the variance associated with months when the sun changes direction in declination and crosses the equator appears to receive boosts.