[An important goal of seismic prospecting is to image subsurface reflectors. Seismic processing is typically performed on raw seismic data obtained by a seismic survey. During a seismic survey, seismic energy is generated at or near ground level for land surveys or in the water for marine surveys by seismic sources, for example, vibrator trucks or air guns. The seismic energy results in ground motion, which is detected by seismic receivers, for example, geophones or accelerometers. Such receivers can include sensors that detect single or multiple components of the vector ground motion, for example motion along the vertical and one or more horizontal directions. In the later case, the survey is called a multi-component survey. The intent of both single and multi-component surveys is to receive reflected waves from underground structures to allow an accurate mapping of the underground environment, including identification of geological formations that may contain hydrocarbons. Seismic energy that is transmitted into the earth as propagating waves, reflected by an underground structure and returned to the surface to be measured by seismic receivers provides meaningful information about subsurface structures. Such energy propagated to and reflected from subsurface structures may be referred to as “body waves” or “reflections” herein. Body waves may be either compression (P-waves) or shear waves (S-waves).
Unfortunately, a large amount of the seismic energy generated by seismic sources is not in the form of body waves. Instead, considerable energy travels horizontally through the shallow region near the surface of the earth or along the ocean bottom and are recorded by the receivers that are desirably intended to receive reflected waves indicative of a property of an underground structure. The strong horizontally traveling surface waves, which may also be referred to as ground roll, can undesirably mask the weaker reflected body waves. Accordingly, it is desirable to filter the seismic data to reduce the surface wave amplitudes in order to generate an accurate image of subsurface formations. These filters treat the body waves as signals to be retained and the surface waves as noise to be removed.
Ground roll or surface wave filtering methods exploit differences between the properties of surface waves (ground roll noise) and body waves (reflection signal). One example of a distinguishing characteristic is that surface waves typically have lower frequency content than body waves. This distinguishing characteristic would suggest using a low cut filter to eliminate the effect of ground roll. Unfortunately, there is not a single characteristic that clearly distinguishes surface waves from body waves. As a result, a problem with typical ground-roll mitigation methods is that reflector amplitudes are often filtered and reduced along with the surface wave amplitudes. For this reason, the use of a low cut filter to arbitrarily reduce seismic amplitudes below a certain specified frequency would undesirably reduce low frequency amplitudes of incident body waves as well. This could potentially remove the ability to accurately image deep reflectors or to perform sophisticated reservoir property analyses.
As another example, ground roll typically has a much lower velocity than body waves. Therefore, ground roll typically has more moveout from trace to trace. Some methods of seismic data analysis attempt to exploit this difference by employing techniques such as velocity filtering, FK filtering, adaptive filtering, beam forming or the like. A problem with such methods is that they require a sufficiently short distance between each of the receivers so that the recorded ground roll is not aliased. If the distance is too large, then the apparent ground roll velocity is ambiguous at some frequencies and both ground roll and reflectors can be removed.
For a multi-component survey, measurements of two or more vector components of the ground motion (for example, both horizontal and vertical components) may provide additional data that can be used to distinguish between surface waves and body waves to facilitate removal of surface waves and retention of body waves. One known approach is polarization filtering, which exploits the fact that body waves have polarization characteristics that can be used to distinguish them from surface waves. Polarization characteristics relate the amplitude and phase differences on different components. These characteristics are indicative of different wave types. In particular, it is known that the typical surface wave (sometimes referred to as a Rayleigh wave) has a distinctive amplitude and phase relationship between the vertical and horizontal components. Specifically, Rayleigh waves are elliptically polarized. The process of polarization filtering includes analyzing the seismic data, and finding a threshold that distinguishes elliptically polarized Rayleigh waves from linearly polarized reflected body waves and then reducing the amplitude of those events that are above the polarization threshold.
U.S. Pat. No. 3,858,168 to Barr et al., the contents of which are hereby incorporated by reference as if fully set forth herein, discloses one example of a known method that uses polarization filtering to attempt separation of signal (body wave) and surface-wave noise. However, the success of such polarization filtering methods is limited because the surface wave energy does not consist of only Rayleigh waves but rather a mix of Rayleigh waves and other forms of surface waves that may not have simple distinctive polarization characteristics compared to signal (body waves). Examples of these other types of surface waves include Love waves, other types of guided waves, and scattered surface-waves and the like. In addition, interference between, for example, a P-wave and an S-wave can appear elliptical in nature, giving the erroneous impression that good signal data corresponds to surface wave noise. Furthermore, because surface waves tend to travel through a highly inhomogeneous, poorly consolidated soil layer, the polarization properties can change and become distorted. Finally, the very interference of several different types of surface waves, and surface waves interfering with reflected and converted body-waves at the same time and offset in a seismic record, makes the computation of polarization ambiguous.
The use of many known polarization filtering techniques involves the selection of an optimal time window to analyze the polarization and to apply the filter. Since ground-roll has a low velocity, reflection energy arrives much earlier than the first ground-roll energy, and this region earlier than the first ground-roll energy can be excluded so that these portions are not harmed by the filter or distort the analysis. But it is difficult to optimize a small window to further isolate the ground roll given the dispersive nature of surface waves. Low-frequency components of the ground roll penetrate more deeply below the surface where the surface-wave velocity is faster, and thus they arrive before high-frequency components of the ground roll, which sample the shallow, slower part of the earth. This dispersion spreads out the ground roll and a large time window is needed to capture it. Such known methods may be used to separate seismic data into data about linearly polarized signals (P-waves and S-waves) and elliptically polarized signals (Rayleigh waves).
Because surface waves tend to be relatively low frequency compared to body waves, raw seismic data may be bandpass filtered or frequency windowed to isolate or predominantly isolate those waves prior to the application of polarization. One example of this technique is set forth in U.S. Pat. No. 4,757,480 to Gutowski, the contents of which are hereby incorporated by reference as if fully set forth herein. Gutowski discloses the use of a multiple frequency filter or a comb filter and performing polarization filtering in each frequency region. This technique removes some of the high frequency interference. However, there is still some low-frequency reflector energy within the window and interference between reflections and surface-wave noise can still be a problem. Also, some of the surface waves are more complex and do not have simple elliptical behavior.
U.S. Pat. No. 6,961,283 to Kappius, the contents of which are hereby incorporated by reference as if fully set forth herein, teaches the use of adaptive filtering to remove the surface wave component which exhibits an elliptical retrograde particle motion (ground roll). However this method does not remove other types of surface-wave noise such as guided waves that do not exhibit elliptical particle motion. The method shown in Kappius also uses both time windowing and band-pass filtering to isolate the seismic data containing ground roll prior to polarization filtering. Since the method relies on the phase difference being close to 90 degrees for the detection of ground roll, occurrence of random noise and interference between different wave types would greatly affect its efficiency.
There are limits to the ability to isolate the surface waves using separate time and frequency windows. It is well known that a narrow time window corresponds to a wide frequency window, i.e. a spike is spread over all frequencies. Similarly, a narrow frequency window results in data spread over time. A different approach is to transform the seismic data into a joint time-frequency domain. Standard methods to do this transform include the Wavelet Transform and the S-transform. These methods are reversible. It is possible to transform time domain data to the wavelet domain, for example, perform an operation such as a filter, and then transform back to the time domain. The advantage to working in the time-frequency domain for polarization filtering is that the surface waves are more isolated. This can make the analysis more precise and result in a filter that affects only a small region of the data and protects more of the reflectors.
Examples of seismic analysis in the time-frequency domains are set forth in the following publications: 1) Diallo, M. S., Kulesh, M., Holschneider, M., Scherbaum, F., 2005, Instantaneous Polarization Attributes in the Time-Frequency Domain and Wavefield-Separation, Geophys. Prosp. 53 (5), 723-731 (“the Diallo 2005 publication”); 2) Diallo, M. S., Kulesh, M., Holschneider, M., Scherbaum, F., Adler, F., 2006, Characterization of Polarization attributes of Seismic Waves Using continuous Wavelet Transform: Geophysics, 71 (3), V67-V77 (“the Diallo 2006 publication”); and 3) International Patent Publication No. WO 2007/006145 by Pinnegar (“the Pinnegar publication”). The entire contents of each of these three publications is hereby incorporated by reference as if fully set forth herein. The former two publications disclose the continuous wavelet transform (CWT) and the latter, the time-frequency S-transform. These methods provide analytical expressions that relate the analyzed signal to its polarization attributes. Unlike methods that compute polarization attributes in the time domain only, these methods are invertible (i.e. given the polarization attributes, it is possible to recover the signal). Polarization filtering is therefore achieved by delimiting the time-frequency domain of the signal using the polarization attributes followed by an inverse transform to obtain the filtered signal.
It is noted that the Pinnegar publication discloses a method of polarization analysis of three-component seismograms in the time-frequency domain to identify different types of surface wave modes (Love, Rayleigh waves or the like). The Pinnegar publication does not, however, disclose the determination of attributes that allow optimal separation of surface waves and body wave reflections.
U.S. Pat. No. 5,781,502 to Becquey, the contents of which are hereby incorporated by reference as if fully set forth herein, describes a method for attenuating the component of the surface wave with retrograde particle motion (i.e. ground roll) using a continuous wavelet transform. However, the method disclosed in Becquey does not constrain the application of the method to minimize the effect on the signal. Furthermore, the method set forth in Becquey does not relate to the removal of other types of surface wave noise that do not exhibit elliptical polarization.
Despite the improvement in surface wave attenuation that can be obtained with polarization analysis in the time-frequency domain, known methods all have limitations. Because of noise interference with signal, or interference between coherent-wave types in some regions of the time-frequency domain, patterns of polarization attributes typical of surface-wave noises may appear where no actual surface-wave noise arrivals are observed. For the same reason, one may also observe polarization patterns typical of signal in regions of the time-frequency domain covered with only surface-wave noises. Therefore, surface-wave noise mitigation based on the polarization attributes alone may not yield the desired result of removing the noise while preserving the signal. To fully preserve data corresponding to reflectors requires compromising on the filtering of surface waves. An improved system and method for performing accurate filtering on seismic data to minimize the influence of undesirable surface waves or ground roll is desirable.
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