Abstract:
A system and method for attenuating noise in seismic data representative of a subsurface region of interest including receiving the seismic data; transforming the seismic data into a domain wherein the seismic data have a sparse or compressible representation to create transformed seismic data; dividing the domain into windows wherein the windows represent known spatio-temporal locations in the seismic data; determining statistics of the transformed seismic data in each window; determining a filter for each window based on the statistics of the transformed data; applying the filter for each window to the transformed seismic data in each window to create filtered seismic data; and performing an inverse transform of the filtered seismic data to create noise-attenuated seismic data.

Description:
FIELD OF THE INVENTION 
       [0001]    The present invention relates generally to methods and systems for estimating and attenuating noise in seismic data and, in particular, methods and systems for estimating and attenuating noise in seismic data which allow spatial and temporal variation to accommodate variable noise levels. 
       BACKGROUND OF THE INVENTION 
       [0002]    Exploration and development of hydrocarbon reservoirs may be efficiently done with the help of seismic data, which must be properly processed in order to allow interpretation of subsurface features. In practice, seismic data is often contaminated by noise which may be coherent or incoherent (e.g. random) in nature. In addition, the noise level may vary both spatially and temporally. 
         [0003]    Conventional noise suppression methods often have difficulty estimating and removing spatially and temporally varying noise. Conventional methods may try to normalize the amplitudes across the seismic data prior to the attenuation step, often using an algorithm like Automatic Gain Control (AGC). This may lead to erroneous suppression of signal in areas with strong signal and weak noise. 
         [0004]    Efficient and effective methods for estimating and attenuating spatially and temporally varying noise in seismic data are needed to improve the final seismic image and allow proper interpretation of the subsurface features. 
       SUMMARY OF THE INVENTION 
       [0005]    Described herein are implementations of various approaches for a computer-implemented method for noise estimation and attenuation in seismic data. 
         [0006]    A computer-implemented method for attenuating noise in seismic data representative of a subsurface region of interest is disclosed. The method includes receiving the seismic data; transforming the seismic data into a domain wherein the seismic data have a sparse or compressible representation to create transformed seismic data; dividing the domain into windows wherein the windows represent known spatio-temporal locations in the seismic data; determining statistics of the transformed seismic data in each window; determining a filter for each window based on the statistics of the transformed data; applying the filter for each window to the transformed seismic data in each window to create filtered transformed seismic data; and performing an inverse transform of the filtered transformed seismic data to create filtered seismic data. 
         [0007]    In one embodiment, the domain may be a curvelet domain. In another embodiment, the domain may be a wavelet domain. 
         [0008]    In an embodiment, the filter may be a threshold. 
         [0009]    In one embodiment the filtered seismic data is noise-attenuated seismic data. 
         [0010]    In another embodiment, the filtered seismic data is a noise model which may then be subtracted from the seismic data to create noise-attenuated seismic data. 
         [0011]    In another embodiment, a computer system including a data source or storage device, at least one computer processor and a user interface is used to implement the method for attenuating noise in the seismic data is disclosed. 
         [0012]    In yet another embodiment, an article of manufacture including a computer readable medium having computer readable code on it, the computer readable code being configured to implement a method for attenuating noise in seismic data representative of a subsurface region of interest is disclosed. 
         [0013]    The above summary section is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description section. The summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter. Furthermore, the claimed subject matter is not limited to implementations that solve any or all disadvantages noted in any part of this disclosure. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0014]    These and other features of the present invention will become better understood with regard to the following description, claims and accompanying drawings where: 
           [0015]      FIG. 1  is a flowchart illustrating a method in accordance with an embodiment of the present invention; 
           [0016]      FIG. 2  shows a noisy synthetic seismic dataset and a histogram of the curvelet coefficients for the dataset; 
           [0017]      FIG. 3  is a representation of windows in a sparse or compressible domain; 
           [0018]      FIG. 4A  uses synthetic data to compare the result of an embodiment of the present invention with a conventional method; 
           [0019]      FIG. 4B  compares the error in the result of an embodiment of the present invention with the error in the result of the conventional method; 
           [0020]      FIG. 5  uses real seismic data to compare the result of an embodiment of the present invention with a conventional method; and 
           [0021]      FIG. 6  schematically illustrates a system for performing a method in accordance with an embodiment of the invention. 
       
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
       [0022]    The present invention may be described and implemented in the general context of a system and computer methods to be executed by a computer. Such computer-executable instructions may include programs, routines, objects, components, data structures, and computer software technologies that can be used to perform particular tasks and process abstract data types. Software implementations of the present invention may be coded in different languages for application in a variety of computing platforms and environments. It will be appreciated that the scope and underlying principles of the present invention are not limited to any particular computer software technology. 
         [0023]    Moreover, those skilled in the art will appreciate that the present invention may be practiced using any one or combination of hardware and software configurations, including but not limited to a system having single and/or multiple processor computers, hand-held devices, programmable consumer electronics, mini-computers, mainframe computers, and the like. The invention may also be practiced in distributed computing environments where tasks are performed by servers or other processing devices that are linked through a one or more data communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices. The present invention may also be practiced as part of a down-hole sensor or measuring device or as part of a laboratory measuring device. 
         [0024]    Also, an article of manufacture for use with a computer processor, such as a CD, pre-recorded disk or other equivalent devices, may include a computer program storage medium and program means recorded thereon for directing the computer processor to facilitate the implementation and practice of the present invention. Such devices and articles of manufacture also fall within the spirit and scope of the present invention. 
         [0025]    Referring now to the drawings, embodiments of the present invention will be described. The invention can be implemented in numerous ways, including, for example, as a system (including a computer processing system), a method (including a computer implemented method), an apparatus, a computer readable medium, a computer program product, a graphical user interface, a web portal, or a data structure tangibly fixed in a computer readable memory. Several embodiments of the present invention are discussed below. The appended drawings illustrate only typical embodiments of the present invention and therefore are not to be considered limiting of its scope and breadth. 
         [0026]    The present invention relates to estimating and attenuating noise in seismic data. One embodiment of the present invention is shown as method  100  in  FIG. 1 . In this embodiment, seismic data is received  10 . The seismic data may be, by way of example and not limitation, from seismic surveys on land, marine seismic surveys or synthetic seismic data. The seismic data may be 1D data, 2D data, 2.5D data, 3D data and/or time lapse or 4D data. 
         [0027]    The seismic data is transformed at  11  into a domain in which the transformed data have a sparse or compressible representation. In such a domain, the signal is represented by a relatively small number of significant coefficients, while the random noise is represented by a large number of small-valued or zero-valued coefficients. Examples of such domains include wavelet domains and curvelet domains. 
         [0028]      FIG. 2  includes an example of noisy synthetic data  20 . The data includes  5  linear events  20 A and is full of random noise. After transformation into the curvelet domain, a histogram  21  may be created that represents the curvelet coefficients of the transformed data. The approximate median value of curvelet coefficients is indicated  22 . In this case, one skilled in the art would be able to tell that the median value is representative of the noise  23  which has a large number of small coefficients while the remainder of the histogram is representative of the signal  24  with barely perceptible numbers of large coefficients. There is some overlap  25  of the noise and signal. 
         [0029]    The domain into which the data is transformed is divided into windows  12 . The nature of this division, such as the size and shape of the divided sections, depends on the domain and the distribution of the transformed data. For example, if the domain is a curvelet domain, the division may be into windows in each angular wedge at each scale. Each scale represents a bandpass-filtered version of the data and each angular wedge within a scale corresponds to the finite range of dips. This is demonstrated in  FIG. 3  which illustrates eight angular wedges,  30 A- 30 H, for a single scale. A window,  32 A- 32 H, representing the same, or approximately the same, spatio-temporal location is shown in each wedge. 
         [0030]    Once the domain has been divided, the statistics of the transformed seismic data for each window in all angular wedges at a particular scale may be determined  13 . These statistics may include, for example, a median or other quantile of the transformed data. The statistics for the windows will be similar to that seen for the overall dataset, as seen in  FIG. 2 , meaning that a value such as the median may be indicative of the noise. The statistics for each window may be calculated for each scale. 
         [0031]    Once the statistics of the transformed data in each window are determined at step  13  of method  100 , it is possible to determine a filter based on those statistics at step  14  of method  100 . The filter may be a threshold. This filter can be used to separate the noise from the signal in the transformed seismic data in each window by applying the filter at step  15  of method  100 . The filter may be different for each window of the domain, meaning that the filter can vary in both space and time. After each window has had its filter applied, the filtered data is inverse transformed back into the original domain at step  16 . The filter can be designed to suppress either the noise or the signal which means that the inverse transform may produce a signal model or a noise model, respectively. 
         [0032]    The result of method  100  may be seen in  FIG. 4A . Here, the pure signal is shown as a single sinusoidal signal  40 . The signal has been combined with random noise that is relatively weak in the upper left corner and increases in strength to the lower right to create a synthetic input dataset  41 . A conventional noise attenuation method that uses a constant threshold across space and time was applied to the input dataset  41  to generate the conventional output  42 . The method  100  implemented with a threshold that varies in space and time has been used to isolate and suppress the noise to generate the new output  44 . Lines  43  and  45  indicate the RMS amplitude for each trace of  42  and  44 , respectively. Both the conventional noise attenuation method and method  100  were parameterized to remove, on average, the same amount of energy from the entire ensemble of traces.  FIG. 4B  shows the error in the results; the difference between the result of the conventional method  42  and the pure signal  40  is seen at  46  and the difference between the result of an embodiment of the present invention  44  and the pure signal  40  is at  47 . Since the conventional method does not vary its suppression in time and space, it has removed more signal  46 A than the embodiment of method  100  resulting in  44 . The conventional method also shows slightly higher average error than the embodiment of method  100 , as shown by line  48  which shows the average RMS level of the conventional error  46  and line  49  which shows the average RMS level of the error in the result from the present invention. The RMS amplitude for each trace of the errors is shown as line  48 A for the conventional method error  46  and as line  49 A for the error in the result of present invention  47 . 
         [0033]      FIG. 5  compares the result of method  100  with a conventional noise-suppression method on real seismic data. The seismic data  50  contains spatially and temporally varying noise and has several events and regions of strong signal and weak noise, including area  50 A. The seismic data  50  was input to the method  100  of  FIG. 1 . The noise removed using method  100  is shown in  51  of  FIG. 5 . The seismic data  50  was also input to a conventional noise-suppression method using Automatic Gain Control (AGC) to normalize the amplitudes across the seismic data prior to the attempted noise suppression. The noise removed by a conventional method using a constant threshold across all traces may be seen in  53  of  FIG. 5 . Strong signal region  50 A is indicated in  51  and  53  as regions  50 B and  50 C, respectively. There is considerably more “noise” energy in  53 , which is actually part of the strong signal which has been erroneously considered noise by the conventional method. The AGC normalization scalars, which dictate how strong the noise attenuation is in the conventional method are derived from the sum of signal and noise. This has led to overly aggressive noise attenuation in region  50 C of  53  since the signal is strong in  50 A. On the other hand, the present invention shows little sensitivity to the presence of signal (strong reflectors), estimated noise is weak, effective thresholds are small, and the strong reflectors are not damaged as seen in region  50 B of  51 . 
         [0034]    The energy of the noise in  FIG. 5  is also seen as line  52  for the present invention and line  54  for the conventional method. This also shows that the “noise” removed by the conventional method is higher across region  50 B. However, the average energy of the noise removed by both methods is the same as indicated by lines  55 . 
         [0035]    A system  600  for performing the method  100  of  FIG. 1  is schematically illustrated in  FIG. 6 . The system includes a data source/storage device  60  which may include, among others, a data storage device or computer memory. The device  60  may contain recorded seismic data and/or synthetic seismic data. The data from device  60  may be made available to a processor  61 , such as a programmable general purpose computer. The processor  61  is configured to execute computer modules  62  that implement method  100 . These computer modules may include a transformation module  62 A to perform step  11  of method  100 , a windowing module  62 B to perform step  12 , a statistics module  62 C to perform step  13 , a determination module  62 D to perform step  14 , an application module  62 E to perform step  15  and an inverse transform module  62 F to perform step  16 . The system may include interface components such as user interface  63 . The user interface  63  may be used both to display data and processed data products and to allow the user to select among options for implementing aspects of the method. By way of example and not limitation, the noise-attenuated seismic data and removed noise computed on the processor  61  may be displayed on the user interface  63 , stored on the data storage device or memory  60 , or both displayed and stored. 
         [0036]    While in the foregoing specification this invention has been described in relation to certain preferred embodiments thereof, and many details have been set forth for purpose of illustration, it will be apparent to those skilled in the art that the invention is susceptible to alteration and that certain other details described herein can vary considerably without departing from the basic principles of the invention. In addition, it should be appreciated that structural features or method steps shown or described in any one embodiment herein can be used in other embodiments as well.