Study on the scales of heterogeneous geologic bodies in random media

In order to study the scale characteristics of heterogeneities in complex media, a random medium is constructed using a statistical method and by changing model parameters （autocorrelation lengths a and b）, the scales of heterogeneous geologic bodies in the horizontal and the vertical Cartesian directions may be varied in the medium. The autocorrelation lengths a and b represent the mean scale of heterogeneous geologic bodies in the horizontal and vertical Cartesian directions in the randQm medium, respectively. Based on this model, the relationship between model autocorrelation lengths and heterogeneous geologic body scales is studied by horizontal velocity variation and standard deviation. The horizontal velocity variation research shows that velocities are in random perturbation. The heterogeneous geologic body scale increases with increasing autocorrelation length. The recursion equation for the relationship between autocorrelation lengths and heterogeneous geologic body scales is determined from the velocity standard deviation research and the actual heterogeneous geologic body scale magnitude can be estimated by the equation.

Parameters effects on spiking deconvolution of land seismic data

Spiking deconvolution is a standard Wiener Levinson algorithm. The autocorrelation of the design time gate is computed and there is a specified taper on the design gate before the autoeorrelation is done. The standard equations are set up, prewhitening is added to the zero lag value of the autocorrelation and the matrix is inverted to derive the spiking operator. In this study, the authors describe a technique for performing spiking deconvolution on prestack time migration （PSTM） data, to test the effect of operator length and percent prewhitening in spiking deconvolution and apply spiking deconvolution trace by trace, with operator lengths 15ms, 10 ms and 5 ms when percent prewhitening 0% , 40ms and 60ms for percent prewhitening 1%. The results show when prewhitening is 0% the shorter operator gives better results, but when value of prewhitening is bigger than 0% it is better to use longer operator lengths.

NEW CONCEPT FOR DETERMINING THE ORDERS OF SAC AND PC

The autocorrelation of a Boolean function possesses the capability to reflect such characteristics as linear structure, Strict Avalanche Criterion(SAC) and Propagation Criterion(PC)of degree k. But it can do nothing in determining the order of SAC or PC. A calculating table for the autocorrelation is constructed in this paper so as to show what is beyond the autocorrelation and how the three cryptographic characteristics are exhibited. A deeper study on the calculating table in a similar way has helped us to develop a new concept, named as the general autocorrelation, to address efficiently the problem how to determine the orders of SAC and PC. The application on the Advanced Encryption Standard(AES) shows the SAC and PC characteristics of Boolean functions of AES S-box.

A GENERAL APPROACH BASED ON AUTOCORRELATION TO DETERMINE INPUT VARIABLES OF NEURAL NETWORKS FOR TIME SERIES FORECASTING

Input selection is probably one of the most critical decision issues in neural network designing, because it has a great impact on forecasting performance. Among the many applications of artificial neural networks to finance, time series forecasting is perhaps one of the most challenging issues. Considering the features of neural networks, we propose a general approach called Autocorrelation Criterion (AC) to determine the inputs variables for a neural network. The purpose is to seek optimal lag periods, which are more predictive and less correlated. AC is a data-driven approach in that there is no prior assumptiona bout the models for time series under study. So it has extensive applications and avoids a lengthy experimentation and tinkering in input selection. We apply the approach to the determination of input variables for foreign exchange rate forecasting and conductcomparisons between AC and information-based in-sample model selection criterion. The experiment results show that AC outperforms information-based in-sample model selection criterion.