Why don’t we measure seismic signatures?

Geophysics ◽  
1991 ◽  
Vol 56 (2) ◽  
pp. 190-201 ◽  
Author(s):  
A. Ziolkowski

There are three related problems with our approach to signature deconvolution. First, there is a confusion among geophysicists about the basis of the convolutional model itself, which leads to doubts about the value of measurements of the source signature. Secondly, it is not generally recognized that statistical methods of wavelet estimation are unreliable. Thirdly, many explorationists are unaware that it is practical in many cases to make meaningful measurements of the source signature. The convolutional model of the reflection seismogram applies only for a point source, and is the convolution of the source signature with the impulse response of the earth, of Green’s function, which contains all possible arrivals, including reflections, refractions, multiples and diffractions. Stabilized deconvolution of the data with a known band‐limited signature is straightforward. The signature can be obtained by independent measurements, as described in the literature. The recovery of the elastic layer parameters from the band‐limited impulse response of the earth, after removal of the source signature by deconvolution, is the problem of inversion, and is not discussed in this paper. The theory of wave propagation does not support the commonly held view that a reflection seismogram can be regarded as a convolution of a wavelet with the series of normal‐incidence primary reflection coefficients. This is true of both prestack and poststack data. Poststack seismic inversion schemes, based on this model, that use well logs to extract the wavelet for predicting lateral variations in lithology away from the wells, rely on the wavelet to be laterally invariant. Even if there is perfect shot‐to‐shot repeatability, this model must yield a different wavelet at every well, and therefore the extracted wavelet does vary laterally. These schemes are therefore self‐contradictory and, in the worst cases, their results are likely to be worthless. Published methods for determining the source signature from measurements for the land vibrator, marine seismic source arrays, and dynamite on land are summarized. None of these methods appears to be in use. A Vibroseis example is included to show that the signal transmitted into the ground by the vibrators does not closely resemble the predetermined sweep, as is normally assumed. The transmitted signal could be determined in processing from measurements of the vibrator behaviour that are made in production for vibrator control, if only these measurements were recorded. Normally they are not. Instead of using measurements to determine the signature, the exploration industry relies on wavelet estimation methods that depend on both a model and statistical assumptions that have no theoretical justification.

2014 ◽  
Vol 54 (1) ◽  
pp. 69
Author(s):  
Andrew Long ◽  
Cyrille Reiser

Ultra-low seismic frequencies less than about 7 Hz cannot be produced by conventional air gun arrays, for any configuration and for any towing depth. There is a profound difference between improving low-frequency recovery by removing source and receiver ghosts (achievable) and improving low-frequency injection on the source side (an unrealised dream). If 1–7 Hz amplitudes could be usefully injected into the earth, it would be possible to facilitate much sharper seismic representation of geological contacts and internal features, and seismic inversion would yield robust and precise predictions of reservoir properties—without well control. The net result is fewer exploration and appraisal wells, greatly reduced exploration and development risks, and optimised recoverable reserves. Furthermore, an emerging seismic pursuit known as full waveform inversion (FWI) makes the bold promise that raw seismic field gathers can be directly used to invert for the highest achievable velocity models, almost without any human intervention. These models will bypass the traditional lack of low-frequency information in band-limited seismic data, and facilitate the aforementioned ambition of seismic inversion without well control. FWI, however, is confronted by the paradox that ultra-low-frequency seismic gathers are the necessary input for stable results. This paper describes new technologies that may enable the injection of strong 2–7 Hz amplitudes into the earth, and explains in simple terms how FWI can already be pursued as a robust complement to the prediction of accurate reservoir properties. The low-frequency revolution is already here.


Geophysics ◽  
1989 ◽  
Vol 54 (6) ◽  
pp. 780-784 ◽  
Author(s):  
Bruno Alessandrini ◽  
Marco Gasperini

The purpose of this note is to compare an iterative algorithm to the Wiener technique for deconvolution of the sparker signature. The iterative algorithm, first proposed by Brigham et al. (1968), has been compared earlier to the Fourier method to obtain the inverse impulse response function of a long‐period seismic monitoring station (Alessandrini and Perazzolo, 1987).


2017 ◽  
Vol 36 (10) ◽  
pp. 858-861 ◽  
Author(s):  
Martin Blouin ◽  
Erwan Gloaguen

Whether it is deterministic, band-limited, or stochastic, seismic inversion can bear many names depending on the algorithm used to produce it. Broadly, inversion converts reflectivity data to physical properties of the earth, such as acoustic impedance (AI), the product of seismic velocity and bulk density. This is crucial because, while reflectivity informs us about boundaries, impedance can be converted to useful earth properties such as porosity and fluid content via known petrophysical relationships.


Geophysics ◽  
1998 ◽  
Vol 63 (6) ◽  
pp. 2108-2119 ◽  
Author(s):  
Are Osen ◽  
Bruce G. Secrest ◽  
Lasse Amundsen ◽  
Arne Reitan

A new and alternative procedure for the deterministic estimation of the seismic source time function (wavelet) is proposed. This paper follows a series of reports on source signature estimation, requiring neither statistical assumptions on the signature nor any knowledge about the earth below the receivers. The proposed estimation method, which in principle is exact, uses conventional recordings of the pressure on a surface below the source and recordings of the pressure at one or more locations above the receiver surface. The derivation of the method is based on the Kirchhoff‐Helmholtz integral equation. The formulation ensures that the scattered energy is filtered from the wavelet estimation, enabling the wavelet to be detected from the direct pressure field. Along with the derivation, we present a numerical example.


Geophysics ◽  
1983 ◽  
Vol 48 (7) ◽  
pp. 854-886 ◽  
Author(s):  
Ken Larner ◽  
Ron Chambers ◽  
Mai Yang ◽  
Walt Lynn ◽  
Willon Wai

Despite significant advances in marine streamer design, seismic data are often plagued by coherent noise having approximately linear moveout across stacked sections. With an understanding of the characteristics that distinguish such noise from signal, we can decide which noise‐suppression techniques to use and at what stages to apply them in acquisition and processing. Three general mechanisms that might produce such noise patterns on stacked sections are examined: direct and trapped waves that propagate outward from the seismic source, cable motion caused by the tugging action of the boat and tail buoy, and scattered energy from irregularities in the water bottom and sub‐bottom. Depending upon the mechanism, entirely different noise patterns can be observed on shot profiles and common‐midpoint (CMP) gathers; these patterns can be diagnostic of the dominant mechanism in a given set of data. Field data from Canada and Alaska suggest that the dominant noise is from waves scattered within the shallow sub‐buttom. This type of noise, while not obvious on the shot records, is actually enhanced by CMP stacking. Moreover, this noise is not confined to marine data; it can be as strong as surface wave noise on stacked land seismic data as well. Of the many processing tools available, moveout filtering is best for suppressing the noise while preserving signal. Since the scattered noise does not exhibit a linear moveout pattern on CMP‐sorted gathers, moveout filtering must be applied either to traces within shot records and common‐receiver gathers or to stacked traces. Our data example demonstrates that although it is more costly, moveout filtering of the unstacked data is particularly effective because it conditions the data for the critical data‐dependent processing steps of predictive deconvolution and velocity analysis.


Geophysics ◽  
1990 ◽  
Vol 55 (11) ◽  
pp. 1416-1428 ◽  
Author(s):  
N. Ross Hill

Just as synthetic seismic data can be created by expressing the wave field radiating from a seismic source as a set of Gaussian beams, recorded data can be downward continued by expressing the recorded wave field as a set of Gaussian beams emerging at the earth’s surface. In both cases, the Gaussian beam description of the seismic‐wave propagation can be advantageous when there are lateral variations in the seismic velocities. Gaussian‐beam downward continuation enables wave‐equation calculation of seismic propagation, while it retains the interpretive raypath description of this propagation. This paper describes a zero‐offset depth migration method that employs Gaussian beam downward continuation of the recorded wave field. The Gaussian‐beam migration method has advantages for imaging complex structures. Like finite‐difference migration, it is especially compatible with lateral variations in velocity, but Gaussian beam migration can image steeply dipping reflectors and will not produce unwanted reflections from structure in the velocity model. Unlike other raypath methods, Gaussian beam migration has guaranteed regular behavior at caustics and shadows. In addition, the method determines the beam spacing that ensures efficient, accurate calculations. The images produced by Gaussian beam migration are usually stable with respect to changes in beam parameters.


2009 ◽  
Vol 28 (11) ◽  
pp. 1334-1335 ◽  
Author(s):  
Ben F. Giles

Author(s):  
M.B. Mueller ◽  
D.F. Halliday ◽  
D.J. van Manen ◽  
J.O.A. Robertsson

Geophysics ◽  
2008 ◽  
Vol 73 (5) ◽  
pp. V37-V46 ◽  
Author(s):  
Mirko van der Baan ◽  
Dinh-Tuan Pham

Robust blind deconvolution is a challenging problem, particularly if the bandwidth of the seismic wavelet is narrow to very narrow; that is, if the wavelet bandwidth is similar to its principal frequency. The main problem is to estimate the phase of the wavelet with sufficient accuracy. The mutual information rate is a general-purpose criterion to measure whiteness using statistics of all orders. We modified this criterion to measure robustly the amplitude and phase spectrum of the wavelet in the presence of noise. No minimum phase assumptions were made. After wavelet estimation, we obtained an optimal deconvolution output using Wiener filtering. The new procedure performs well, even for very band-limited data; and it produces frequency-dependent phase estimates.


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