Communications – electrical: acoustic wave systems and devices – Seismic prospecting – Land-reflection type
Patent
1996-05-22
1998-06-23
Moskowitz, Nelson
Communications, electrical: acoustic wave systems and devices
Seismic prospecting
Land-reflection type
367 45, 364421, G01V 136
Patent
active
057712034
DESCRIPTION:
BRIEF SUMMARY
The present invention relates to seismic prospection, and in particular to a method of attenuating noise affecting seismic traces.
The general principle of seismic prospection consists in using a seismic source to cause a disturbance in the sub-soil, and in using sensors (geophones or hydrophones) to record seismic data so as to deduce therefrom information about the geology of the sub-soil, in particular so as to detect the presence of hydrocarbons.
The sensors are usually regularly distributed at the nodes of a grid in space, and the recordings y(t) as a function of time t of the signals picked up by the sensors, also known as "seismic traces", are grouped together and juxtaposed as a function of the x coordinate values of the sensors over the grid (where x is a space coordinate), to form a seismic section t-x constituted by seismic data y(t,x).
Seismic data comprises useful information (e.g. a succession of reflected seismic echoes) embedded in overall background noise that is to be eliminated by appropriate processing, which processing must also accommodate the possibility that some of the traces in the seismic section may be missing. In practice, it can happen that some of the points of the grid do not provide any useful recording, either because it was not possible to install a sensor at the corresponding location, given the nature of the terrain, or because of a failure in the data acquisition installation, or indeed because the signal delivered by a particular sensor was saturated during recording by a very loud noise.
To eliminate noise, seismic data can be processed in an f-x plane using Y(f,x) data after applying the Fourier transform to the initial seismic data y(t,x) for the time variable t, or in an f-k plane for data Y(f,k) after applying the Fourier transform to the data Y(f,x) for a space variable x, where f designates a frequency variable, and where k designates a wave number variable.
It is common practice to distinguish noise that is non-coherent, random, and of a physical origin that is usually not identified (e.g. due to natural phenomena such as swell or microseisms), from noise that is coherent, and usually generated by physical phenomena that are identified such as a particular mode of seismic wave propagation (e.g. waves propagating at the surface), which noise needs to be eliminated because it contains no useful information concerning the sub-soil or because it would be too difficult to extract any useful information it does contain (due to so-called "multiple" reflections).
Numerous methods have been proposed for processing seismic data in order to attenuate noise, and those methods can be classified in several broad categories, depending on whether or not they are intended more particularly to eliminate noise of localized nature.
In a first type of method, known as a "deterministic" attenuation method, it is conventional to use the f-k plane in the hope that noise affecting the seismic data Y(f,k) can be identified and located in a particular region of that plane. As an example, noise propagating at constant speed appears as a straight line in the f-k plane and can be eliminated by simple filtering relating to the wave number variable k. More generally, data in the f-k plane is processed by being multiplied by a so-called "projective" filter whose value is ideally equal to 0 in regions where noise is assumed to be present (attenuation domain of the filter) and equal to 1 elsewhere (pass domain of the filter). It is nevertheless necessary to provide a broad transition between the pass and attenuation domains of the filter in order to restrict the extent thereof in the t-x seismic section, since otherwise there are side effects when the filter goes beyond the seismic section and the missing data is taken as being equal to zero (which is equivalent to setting certain coefficients of the filter to zero and thus to deteriorating performance). The side effects are particularly troublesome in the space domain where the number of samples, which corresponds to the number of traces, is p
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Mahalanabis, et al., "A Fast Optimal Deconvolution Algorithm for Real Seismic Data Using Kalman Predictor Model," pp. 216-221 IEEE Transactions on Geoscience and Remote Sensing(Oct. 1981).
Compagnie Generale de Geophysique
Moskowitz Nelson
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