The generalized one‐dimensional synthetic seismogram

Geophysics ◽  
1987 ◽  
Vol 52 (5) ◽  
pp. 589-605 ◽  
Author(s):  
P. R. Gutowski ◽  
S. Treitel

The normal‐incidence synthetic seismogram for an elastic and horizontally stratified medium has been thoroughly studied for a relatively restricted number of source and receiver locations. Most existing treatments are concerned with the special case in which the source as well as the receiver are situated at the surface; few attempts have dealt with completely arbitrary source and receiver geometries. Here we examine arbitrary geometries with the aid of the layer matrix approach, in which upgoing and downgoing wave motion at each interface is expressed in terms of z-transform polynomials. Such an approach brings to light a number of physically important relations that the model satisfies. For example, the synthetic seismograms generally have the familiar autoregressive‐moving average (ARMA) structure for the surface‐source, surface‐receiver case. For particular combinations of reflection coefficients, however, the seismograms reduce to purely autoregressive (AR) representations. In all cases, we work out the delay properties that the respective autoregressive and moving average components must obey. The present solutions are easily reduced to a useful form for practical computation. One application of particular current interest is the simulation of vertical seismic profiling (VSP) surveys, where we have extended the theoretical treatment to include expressions for the derivatives of the seismograms with respect to the reflection coefficients. The resulting time series, which we call Jacobograms, are indicative of the sensitivity of the seismogram to the various reflection coefficients and are thus diagnostic of the model’s behavior.

Geophysics ◽  
1980 ◽  
Vol 45 (11) ◽  
pp. 1697-1705 ◽  
Author(s):  
Peter Hubral ◽  
Sven Treitel ◽  
Paul R. Gutowski

The normal incidence unit impulse reflection response of a perfectly stratified medium is expressible as an autoregressive‐moving average (ARMA) model. In this representation, the autoregressive (AR) component describes the multiple patterns generated within the medium. The moving average (MA) component, on the other hand, bears a simple relation to the sequence of reflection coefficients (i.e., primaries only) of the layered structure. An alternate representation of the reflection response can be formulated in terms of a superposition of purely AR time‐varying minimum‐delay wavelets. Each successive addition of a deeper interface to the layered system gives rise to an AR wavelet whose leading term is equal to the magnitude of the primary reflection originating at this interface. We accordingly call these wavelets “generalized primaries.” The AR component of every generalized primary contains only those multiple reflections that arise from the addition of its particular interface to the layered medium.


2020 ◽  
Vol 2020 (66) ◽  
pp. 101-110
Author(s):  
. Azhar Kadhim Jbarah ◽  
Prof Dr. Ahmed Shaker Mohammed

The research is concerned with estimating the effect of the cultivated area of barley crop on the production of that crop by estimating the regression model representing the relationship of these two variables. The results of the tests indicated that the time series of the response variable values is stationary and the series of values of the explanatory variable were nonstationary and that they were integrated of order one ( I(1) ), these tests also indicate that the random error terms are auto correlated and can be modeled according to the mixed autoregressive-moving average models ARMA(p,q), for these results we cannot use the classical estimation method to estimate our regression model, therefore, a fully modified M method was adopted, which is a robust estimation methods, The estimated results indicate a positive significant relation between the production of barley crop and cultivated area.


2021 ◽  
Vol 73 (1) ◽  
Author(s):  
Xin Jin ◽  
Xin Liu ◽  
Jinyun Guo ◽  
Yi Shen

AbstractPolar motion is the movement of the Earth's rotational axis relative to its crust, reflecting the influence of the material exchange and mass redistribution of each layer of the Earth on the Earth's rotation axis. To better analyze the temporally varying characteristics of polar motion, multi-channel singular spectrum analysis (MSSA) was used to analyze the EOP 14 C04 series released by the International Earth Rotation and Reference System Service (IERS) from 1962 to 2020, and the amplitude of the Chandler wobbles were found to fluctuate between 20 and 200 mas and decrease significantly over the last 20 years. The amplitude of annual oscillation fluctuated between 60 and 120 mas, and the long-term trend was 3.72 mas/year, moving towards N56.79 °W. To improve prediction of polar motion, the MSSA method combining linear model and autoregressive moving average model was used to predict polar motion with ahead 1 year, repeatedly. Comparing to predictions of IERS Bulletin A, the results show that the proposed method can effectively predict polar motion, and the improvement rates of polar motion prediction for 365 days into the future were approximately 50% on average.


2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Joshua C. C. Chan ◽  
Eric Eisenstat ◽  
Gary Koop

AbstractThis paper is about identifying structural shocks in noisy-news models using structural vector autoregressive moving average (SVARMA) models. We develop a new identification scheme and efficient Bayesian methods for estimating the resulting SVARMA. We discuss how our identification scheme differs from the one which is used in existing theoretical and empirical models. Our main contributions lie in the development of methods for choosing between identification schemes. We estimate specifications with up to 20 variables using US macroeconomic data. We find that our identification scheme is preferred by the data, particularly as the size of the system is increased and that noise shocks generally play a negligible role. However, small models may overstate the importance of noise shocks.


2015 ◽  
Vol 22 (11) ◽  
pp. 1931-1935 ◽  
Author(s):  
Andreas Loukas ◽  
Andrea Simonetto ◽  
Geert Leus

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