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...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.展开更多
A class of new PN sequence with prime number periods of 4t +1 form (t is an integer)is constructed.The advantage of these PN sequencs over the m(M) sequence is their large number of alternative periods.They hav...A class of new PN sequence with prime number periods of 4t +1 form (t is an integer)is constructed.The advantage of these PN sequencs over the m(M) sequence is their large number of alternative periods.They have good pseudo random characteristics demonstrated by the expression of periodic autocorrelation function found out in this paper.展开更多
According to the relationships among state transition probability matrixes with different step lengths, an improved Markov chain model based on autocorrelation and entropy techniques was introduced. In the improved Ma...According to the relationships among state transition probability matrixes with different step lengths, an improved Markov chain model based on autocorrelation and entropy techniques was introduced. In the improved Markov chain model, the state transition probability matrixes can be adjusted. The steps of the historical state of the event, which was significantly related to the future state of the event, were determined by the autocorrelation technique, and the impact weights of the event historical state on the event future state were determined by the entropy technique. The presented model was applied to predicting annual precipitation and annual runoff states, showing that the improved model is of higher precision than those existing Markov chain models, and the determination of the state transition probability matrixes and the weights is more reasonable. The physical concepts of the improved model are distinct, and its computation process is simple and direct, thus, the presented model is sufficiently general to be applicable to the prediction problems in hydrology and water resources.展开更多
文摘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.
文摘A class of new PN sequence with prime number periods of 4t +1 form (t is an integer)is constructed.The advantage of these PN sequencs over the m(M) sequence is their large number of alternative periods.They have good pseudo random characteristics demonstrated by the expression of periodic autocorrelation function found out in this paper.
基金Under the auspices of Major Special Technological Program of Water Pollution Control and Management (No.2009ZX07106-001)National Natural Science Foundation of China (No. 51079037, 50909063)
文摘According to the relationships among state transition probability matrixes with different step lengths, an improved Markov chain model based on autocorrelation and entropy techniques was introduced. In the improved Markov chain model, the state transition probability matrixes can be adjusted. The steps of the historical state of the event, which was significantly related to the future state of the event, were determined by the autocorrelation technique, and the impact weights of the event historical state on the event future state were determined by the entropy technique. The presented model was applied to predicting annual precipitation and annual runoff states, showing that the improved model is of higher precision than those existing Markov chain models, and the determination of the state transition probability matrixes and the weights is more reasonable. The physical concepts of the improved model are distinct, and its computation process is simple and direct, thus, the presented model is sufficiently general to be applicable to the prediction problems in hydrology and water resources.
