Patent Abstract:
Apparatuses and methods for collecting and analyzing seismic data (D) include a frequency dependent noise factor (ε 2 ) for stabilizing a transformation matrix (S). The noise factor (ε 2 ) is a function of a number of nonzero eigenvalues of the transformation matrix (S).

Full Description:
RELATED CASES 
     This application is a national filing under 35 USC §371 of international application PCT/US2010/029368, filed on 31 Mar. 2010, which claims benefit of U.S. application Ser. No. 61/165,135, filed on 31 Mar. 2009, both of which are incorporated herein by reference. 
    
    
     TECHNICAL FIELD 
     This disclosure relates generally to apparatuses and methods for transforming a seismogram. 
     BACKGROUND 
     Seismograms are commonly transformed into tau-p space to facilitate processing, filtering, deconvolution, and the like. The inversion method used to calculate the transformated data becomes unstable at low frequencies. The artifacts from this instability lead to undesired effects after tau-p domain processing and transformation back to the time-distance domain. Furthermore, current implementations of the transformation lead to a transform domain which is artificially poor in sampling in the low frequencies. 
     A previous solution has been to attempt to stabilize the inversion by the use of a standard, frequency independent noise factor. However, such a noise factor does not attack the problem with sufficient specificity, and the instability remains. The spectral imbalance of the transform domain has simply been tolerated, even though it has deleterious effects on some transform domain processes such as deconvolution. Therefore, a heretofore unaddressed need exists in the industry to address the aforementioned deficiencies and inadequacies. 
     SUMMARY 
     The various embodiments of the present disclosure overcome the shortcomings of the prior art by providing an apparatus and method for transforming a seismogram into tau-p space. According to one aspect, apparatuses and methods for transforming seismic data include a frequency dependent noise factor for stabilizing a transformation matrix. According to an exemplary embodiment, time-offset seismic data D is collected with one or more sensors at a plurality of sensor positions in a range along a dimension of a formation by sensors. As such, the time-offset seismic data D represents the formation. The time-offset data is Fourier transformed into frequency-offset seismic data D. A method of transforming frequency-offset seismic data D into tau-p space includes steps that are performed at each frequency. One step is generating frequency dependent transformation matrices R that are configured to transform frequency-offset seismic data D into tau-p seismic data A according to A=(R + R) −1 R + D. Another step is generating an estimate of the number of nonzero eigenvalues or diagonal elements of the transformation matrix R + R where the number of nonzero eigenvalues is a function of frequency. Subsequently, an invertible transformation matrix X is created as a noise factor is factored into the transformation matrix R + R. The noise factor is a function of the number of nonzero eigenvalues. The frequency-offset seismic data D is then transformed into tau-p seismic data A according to A=(X) −1 R + D. 
     According to an exemplary embodiment, the number of nonzero eigenvalues is further a function of the range of sensor positions and of a p-domain sampling interval. Further, the noise factor is a function of the sum of the eigenvalues of transformation matrix R + R divided by the number of nonzero eigenvalues. Alternatively described, the noise factor is substantially proportional to the average of the non-zero eigenvalues of transformation matrix R + R. 
     According to an exemplary embodiment, for example where the seismic data includes curved events, the method further includes multiplying tau-p seismic data A by the square root of a derivative operator. Seismic data can include curved events, for example when the seismic data is collected at sensor positions located in a range along the surface of the formation. 
     The foregoing has broadly outlined some of the aspects and features of the present disclosure, which should be construed to be merely illustrative of various potential applications. Other beneficial results can be obtained by applying the disclosed information in a different manner or by combining various aspects of the disclosed embodiments. Accordingly, other aspects and a more comprehensive understanding may be obtained by referring to the detailed description of the exemplary embodiments taken in conjunction with the accompanying drawings, in addition to the scope defined by the claims. 
    
    
     
       DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS 
         FIG. 1  is a schematic illustration of an apparatus for vertical seismic profiling exploration and a method for processing seismic data, according to a first exemplary embodiment. 
         FIG. 2  is a schematic illustration of an apparatus for measuring surface seismics, according to a second exemplary embodiment. 
         FIGS. 3-5  are graphical illustrations of an exemplary method of processing a seismogram generated by the apparatus of  FIG. 2 . 
         FIGS. 6-8  are graphical illustrations of exemplary transformations between time-offset space and tau-p space. 
         FIG. 9  is a schematic illustration of an exemplary computing environment of the apparatuses of  FIG. 1  and  FIG. 2 . 
         FIG. 10  is a schematic illustration of an exemplary seismogram in time-offset space. 
         FIG. 11  is a schematic illustration of an exemplary seismogram in frequency-wavenumber space. 
         FIG. 12  is a schematic illustration of a wavelet with respect to a range of data sampling positions and a spatial period. 
     
    
    
     DETAILED DESCRIPTION 
     As required, detailed embodiments are disclosed herein. It must be understood that the disclosed embodiments are merely exemplary and that the teachings of the disclosure may be embodied in various and alternative forms, and combinations thereof. As used herein, the word “exemplary” is used expansively to refer to embodiments that serve as illustrations, specimens, models, or patterns. The figures are not necessarily to scale and some features may be exaggerated or minimized to show details of particular components. In other instances, well-known components, systems, materials, or methods have not been described in detail in order to avoid obscuring the present disclosure. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a basis for the claims and as a representative basis for teaching one skilled in the art. 
     The disclosure includes exemplary data collection techniques and exemplary methods that can be used to process the data collected by these and other various techniques. Disclosed data collection techniques include vertical seismic profiling (VSP) and surface seismics. 
     Vertical Seismic Profiling 
     Referring to  FIG. 1 , a first apparatus  99  is described. The apparatus  99  includes a tool  100  suitable for VSP exploration that is configured to be lowered on an armored multiconductor cable  108  into a borehole  110  to take VSP measurements of a subspace formation  112 . Tool  100  is configured for movement up and down borehole  110  and includes a pivoted, power-driven clamping arm  102 , receiver pads or geophones  104 ,  106 , and various internal subsystems. Tools of this type are described, for example, in U.S. Pat. No. 4,527,260. The geophone  104  is clamped against the wall of borehole  110  at a borehole depth z by arm  102  and measures seismic energy originating at a seismic source  114 . These measurements are digitized by circuitry (not shown) in tool  100  and the result is sent up via conductors within armored cable  108 . Cable  108  goes to a sheave wheel  116  at the surface, and then to a suitable drum-and-winch mechanism  118  which raises and lowers tool  100  in borehole  110  as desired so that geophone  104  can be clamped at a succession of depths z. Electrical connections between tool  100  and surface equipment are made through a suitable multi-element slip-ring-and-brush contact assembly  120 . A surface unit  122  contains tool control and preprocessing equipment which communicates with tool  100  via cable  108 , and with seismic source  114  via another cable. Cable  108  also runs through a measuring wheel unit  124  which provides signals indicative of the current borehole depth z of geophone  104 . These depth signals are recorded at surface unit  122  such that a given set of outputs of the geophone  104  can be associated with a respective depth z in borehole  110 . 
     The surface unit  122  includes an exemplary environment for implementing the methods described herein in or through use of a personal computer (PC). For example, methods described herein may be implemented through an application program running on an operating system of a PC. Methods described herein also may be practiced with other computer system configurations, including hand-held devices, multiprocessor systems, microprocessor based or programmable consumer electronics, mini-computers, mainframe computers, etc. 
     The application program may include routines, programs, components, data structures, etc. that implement certain abstract data types, perform certain tasks, actions, or tasks. In a distributed computing environment, the application program (in whole or in part) may be located in local memory, or in other storage. In addition, or in the alternative, the application program (in whole or in part) may be located in remote memory or in storage to allow for the practice of the methods where tasks are performed by remote processing devices linked through a communications network. 
     Referring to  FIGS. 1 and 9 , a PC  50  includes a processor  52  (also referred to as a processing means or processing unit) joined by a system bus  54  to a memory  56  (also referred to as system memory). The memory  56  may include read only memory (ROM)  58  and random access memory (RAM)  60 . The ROM  58  stores the basic input/output system  62  (BIOS), which contains basic routines that aid in transferring information between elements within the PC  50  during start-up, and at other times. The RAM  60  may store program modules and drives. In particular, the RAM  60  may include an operating system  64 , one or more application programs  66 , outline fonts  68 , program data  70 , a web browser program (not illustrated), etc. 
     The PC  50  also may include a plurality of drives interconnected to other elements of the PC  50  through the system bus  54  (or otherwise). Exemplary drives include a hard disk drive  72 , a magnetic disk drive  74 , and an optical disk drive  76 . Specifically, each disk drive may be connected to the system bus  54  through an appropriate interface (respectively, a hard disk drive interface  78 , a magnetic disk drive interface  80 , and an optical drive interface  82 ). Further, the PC  50  may include non-volatile storage or memory through the drives and their associated computer-readable media. For example, the magnetic disk drive  74  allows for the use of a magnetic disk  84 ; and the optical disk drive  76  allows for the use of an optical disk  86 . Other types of media that are readable by a computer, e.g., magnetic cassettes, digital video disks, flash memory cards, ZIP cartridges, JAZZ cartridges, etc., also may be used in the exemplary operating environment. 
     In addition, the PC  50  may include a serial port interface  88  connected to the system bus  54 . The serial port interface  88  connects to input devices that allow commands and information to be entered. These input devices may include a keyboard  90 , a mouse  92 , and/or other input device. Pens, touch-operated devices, microphones, joysticks, game pads, satellite dishes, scanners, etc. also may be used to enter commands and/or information. The input devices also may be connected by other interfaces, such as a game port or a universal serial bus (USB). Further, the PC  50  may include a monitor or other display screen  96 . The monitor  96  is connected through an interface such as a video adaptor  98  to the system bus  54 . The PC  50  may include other peripheral and/or output devices, such as speakers or printers (not illustrated). 
     The PC  50  may be connected to one or more remote computers (not shown), and may operate in a network environment. The remote computer may be a PC, a server, a router, a peer device or other common network node, and may include many or all of the elements described in relation to the PC  50 . The connection between the PC  50  and the remote computer may be through a local area network (LAN) and/or a wide area network (WAN). The PC  50  is connected to the LAN through a network interface. With respect to the WAN, the PC  50  may include a modem or other device to channel communications over the WAN, or global data communications network (e.g., the Internet). The modem (internal or external) is connected to the system bus  54  via the serial port interface  88 . The described network connections are exemplary and other ways of establishing a communications link between the PC  50  and a remote computer may be used. 
     According to an exemplary data collection method, tool  100 , with clamping arm  102  retracted, is lowered to the bottom of borehole  110  (or the lowest depth z of interest), arm  102  is extended to clamp geophone  104  in good acoustic contact with a wall of borehole  110 , and a seismic signal is generated at source  114 . The seismic energy measured by the geophone  104  is digitized and sent up to surface unit  122  for preprocessing. For example, preprocessing can include adjusting the data to account for tool  100  orientation and seismic energy attenuation with travel time. Preprocessing can also include subjecting the data to other processing of VSP signals to generate a VSP seismogram in discrete form. 
     The VSP seismogram generated by tool  100  of  FIG. 1  is designated D i (t j ). In this designation, each subscript “i” identifies a respective one of a total of I seismic traces D i (t j ), where i=1, 2, . . . , I and I is an integer. In the same notation, the subscript “j” identifies a time sample t j  of seismic trace D i (t j ) and j=1, 2, . . . , J where J is an integer. VSP seismogram D i (t j ) can be conceptualized as a matrix of time samples of seismic amplitude, where each row is a seismic trace for one spatial position, depth z i , and each column is the set of samples for the same time t j  in all of seismic traces D i (t j ). VSP seismogram D i (t j ) is stored in digital computer storage at a step  126  of an exemplary processing method  125 . 
     According to the processing method  125  performed by the surface unit  122 , in order to allow for convenient and effective filtering and/or other processing of time-depth seismogram D i (t j ) in tau-p space, time-depth seismogram D i (t j ) is subjected to a direct forward tau-p transformation at a step  128 . The transformation is discrete but most of the energy, particularly the signal, can be recovered by the reverse transformation. The forward tau-p transformation is achieved with transformation matrices R in  that are derived at a step  130 , as described in further detail below. The subscript “n” identifies a respective one of a total of N columns in each transform matrix R in , where a given column pertains to a given slope p n  in tau-p space, where n=1, 2, . . . , N and N is an integer. 
     The output of the step  128  is a tau-p seismogram A n (τ j ) that is in tau-p (slope-intercept) space. If desired, tau-p seismogram A n (τ j ) can be stored and/or otherwise utilized at a step  132 . At a step  134 , tau-p seismogram A n (τ j ) can be subjected to filtering and/or other processing for example, deconvolution to suppress multiples, up-down separation, other undesirable effects based on large apparent dips, and mode decomposition into compressional and shear waves using multicomponent seismic data. The resulting filtered or processed tau-p seismogram A n (τ j ) can be displayed, recorded, and/or otherwise utilized at step  136 . Processed tau-p seismogram A n (τ j ) can be reverse or inverse transformed at a step  138 , for example, to return to the time-depth space of time-depth seismogram D i (t j ) using transformation matrices R in  from step  130 . The output of step  138  is a processed time-depth seismogram D i (t j ) that can be displayed, stored, or otherwise utilized at a step  142  to help evaluate subsurface formation  112  for underground resources. 
     Surface Seismics 
       FIG. 2  illustrates a second apparatus  200  that is configured to measure surface seismics. Apparatus  200  includes a source  202  of seismic energy and receivers or geophones  204 ,  206 ,  208 . Source  202  is located on the earth&#39;s surface and is illustrated as a truck that uses a vibrator to impart mechanical vibrations to the earth. Source  202  creates a wave of seismic energy that travels downwardly into formations  210 ,  212 ,  214 . For purposes of teaching, several raypaths of a wave of seismic energy are illustrated. Raypath  220  propagates downwardly to the interface between formations  210 ,  212  and returns back to the surface (a primary reflection) where it is received at geophone  204 . Similarly, each of raypaths  222 ,  224  propagates downwardly to the same reflector and is received at respective geophone  206 ,  208  (primary reflections). Each of the raypaths  220 ,  222 ,  224  is received at a different angle of incidence. Non-illustrated raypaths are reflected by the interface between formations  212 ,  214  and received at geophones  204 ,  206 ,  208  as primary reflections. In addition, non-illustrated raypaths are reflected multiple times by one or more interfaces between formations  210 ,  212 ,  214  (multiple reflections) and are received at geophones  204 ,  206 ,  208 . For simplicity, the refractions of the raypaths as they go from one layer into another are not illustrated. 
     A surface unit  236  acquires, processes, and stores the signals or traces that are output by the geophones  204 ,  206 ,  208 . The illustrated surface unit  236  includes an amplifier  230 , a filter  232 , a digitizer  234 , and the PC  50  described with respect to the surface unit  122 . After a suitable number of seismic energy impulses have been imparted to the earth at a particular location and recorded by geophones  204 ,  206 ,  208 , energy source  202  is moved to a new location along a line connecting the source  202  and geophone  204 ,  206 ,  208  positions. Geophones  204 ,  206 ,  208  can also be moved to a respective new location along the same line. The process can be repeated to get multifold coverage of the subsurface formations  210 ,  212 ,  214 . 
     Surface unit  236  can collect and process a time-offset seismogram D i (t j ) that is similar to the seismogram D i (t j ) discussed in connection with method  125  and  FIG. 1 . Here, referring momentarily to  FIGS. 3 ,  5 , and  10 , the time-offset seismogram D i (t j ) can be conceptualized as a matrix of time samples of seismic amplitude, where each row is a seismic trace for one spatial position x i  corresponding to a geophone  204 ,  206 ,  208  and each column is the set of samples for the same time t j  in all of seismic traces D i (t j ). In general, seismograms can be in the more conventional time-offset space, time-distance space, time-lateral space, time-depth space, frequency-offset space, or any other suitable space. 
     Similar to the manner in which the seismogram is processed by the surface unit  122  according to the method  125 , the seismogram D i (t j ) is processed by surface unit  236  to effect forward transformation, filtering, inverse transformation, and utilization of the result so as to process time-offset seismogram D i (t j ) for use in evaluating underground resources or features of the formations  210 ,  212 ,  214 . For this kind of data, as discussed in further detail below, typical filtering operations would be muting of large p values to attenuate surface waves (such as ground roll), and applying deconvolution filters to attenuate short period multiple reflections. Other known filtering operations include those discussed in  Seismic Data Analysis: Processing, Inversion, and Interpretation of Seismic Data  by Ozdogan Yilmaz (vol. I &amp; II, Society of Exploration Geophysicists, 2001). 
     Processing Method 
       FIGS. 3-5  illustrate an exemplary method of processing time-offset seismogram D i (t j ) corresponding to the surface seismic data collection method illustrated in  FIG. 2 .  FIG. 3  illustrates time-offset seismogram D i (t j ) in time-offset space as would be produced by surface unit  236  of the second apparatus  200  illustrated in  FIG. 2 . For purposes of teaching, time-offset seismogram D i (t j ) of  FIG. 3  is illustrated in a simplified manner to show the relevant features. In practice, seismograms tend to be much more complex and the desired features tend to be much more obscured by undesirable features such as surface waves, multiples, and other noise. In  FIG. 3 , the horizontal axis is the travel time t j  taken by a seismic signal to travel from source  202  to a geophone  204 ,  206 ,  208  at an offset position x i  and the vertical axis is offset distance x i  along the surface of the subsurface formation. 
     For clarity, time-offset seismogram D i (t j ) of  FIG. 3  is represented in continuous form. In practice, each trace is made up of discrete time samples t j , each row of time-offset seismogram D i (t j ) being a trace for a given position x i . The values of elements of time-offset seismogram D i (t j ) are depicted in  FIG. 3  as raised amplitudes. 
     In the simplified plot of  FIG. 3 , there is a surface wave event  300  and a primary reflection event  302 . In the illustrated example, the surface wave event  300  is substantially linear and the primary reflection event  302  is substantially curved. According to an exemplary method of processing, surface wave event  300  is separated from the primary reflection event  302 . It is difficult to effectively achieve this separation in time-offset space because, in a typical real world seismogram, the complexity of the subsurface structures and imperfections of the measuring process can make it impossible or impractical to achieve the separation accurately. However, the separation can be done effectively, conveniently, and accurately in tau-p space.  FIG. 4  illustrates time-offset seismogram D i (t j ) of  FIG. 3  when transformed into tau-p seismogram A n (τ j ) in linear tau-p space. The horizontal axis is the tau-axis (intercept τ) and the vertical axis is the p-axis (slope p). 
     One important characteristic of tau-p seismogram A n (τ j ) represented in tau-p space is that the energy of surface wave event  300  appears at a large p-value of the p-dimension and that the energy of primary reflection event  302  appears at p-values that are generally below the p-value of the surface wave event  300 . Further, the energy of surface wave event  300  is associated with a slope p 1  and an intercept τ 1 . As such, tau-p seismogram A n (τ j ) can be filtered to remove surface wave event  300  by removing all, or a selected part, of the energy that appears at large p-values and/or is associated with slope p 1  and an intercept τ 1 . For example, energy within a window  400  can be removed specifically for the purpose of removing surface wave event  300 . Illustrated window  400  is selected to include the surface wave event  300 . 
       FIG. 5  illustrates the result of an inverse transformation of tau-p seismogram A n (τ j ) of  FIG. 4  after removing energy within window  400 . The processed time-offset seismogram D i (t j ) is in the same space as the original time-offset seismogram D i (t j ) of  FIG. 3 . Surface wave event  300  has been suppressed leaving primary reflection event  302  of the original time-offset seismogram D i (t j ). 
       FIG. 3  illustrates a simplistic example in which it is clear that event  300  is a surface wave event. However, the method of filtering is applicable to a more complex environment. The technique of forward transforming from a first space into a second space in a manner that allows exact inverse transformation back to the first space is applicable to other kinds of spaces and to other kinds of filtering in the second space. Also, the method can be used with data other than seismograms. 
     Tau-p Transform 
       FIGS. 6-8  illustrate the nature of tau-p transformations in their simplest form.  FIG. 6  illustrates time-offset seismogram D i (t j ) represented in time-offset space and  FIG. 7  shows the corresponding tau-p seismogram A n (τ j ) represented in tau-p space.  FIG. 6  corresponds to  FIG. 3  and  FIG. 7  corresponds to  FIG. 4 . In the continuous form of the tau-p transformation, the integral along a line in time-depth space becomes a point in tau-p space. The integral along line  600   a  in  FIG. 6  becomes a point  600   b  in  FIG. 7 . The integral along a line  602   a  in  FIG. 6 , which has the same slope p as line  600   a , becomes a point  602   b  in  FIG. 7  and has the same slope p as point  600   b . Integrals along parallel lines  604   a ,  606   a  become points  604   b ,  606   b  that have the same slope p value. The term integral in this context is used to mean the integral of the energy that appears in the seismogram represented in  FIG. 6  along the respective line. 
     The inverse transformation from tau-p space to time-offset space is illustrated in  FIG. 8  and includes choosing a point  608   c  in the time-offset space that happens to have the same location in time-offset space as point  608   a  in FIG.  6  and accumulating the contribution to this point from each of the points in  FIG. 7  that pass through point  608   a . For simplicity, point  608   c  was chosen at the intersection of lines  600   c ,  604   c  and the reconstructed point  608   c  receives and accumulates contributions from points  600   b ,  604   b  in  FIG. 7 . Point  604   b  also makes contributions to all points in  FIG. 8  that are along line  604   c . If  FIGS. 6 and 8  are superimposed, line  600   a  would coincide with line  600   c  and line  604   a  would coincide with line  604   c . The inverse transformation from a representation such as  FIG. 7  to a representation such as  FIG. 8  is sometimes called back projection and the transformation from a representation such as  FIG. 6  to a representation such as  FIG. 7  is sometimes called a forward projection. 
     A specific and nonlimiting method for transforming to tau-p space from time-offset space is now described in detail. The exemplary method includes obtaining a two dimensional array of values of a seismic parameter such as time-offset seismogram D i (t j ), transforming time-offset seismogram D i (t j ) into frequency space to get frequency-offset seismogram D i (ω j ), transforming frequency-offset seismogram D i (ω j ) into ω-p space to get frequency-domain tau-p seismogram A n (ω j ), and then transforming the frequency ω back to intercept time τ to get tau-p seismogram A n (τ j ). At least part of the transformation involves transformation matrices R in  that can be derived as discussed in further detail below. 
     The tau-p seismogram A n (τ j ) is in discrete form with the subscript n identifying respective rows of the array and the indices j identifying respective columns of the array. In this form of tau-p seismogram A n (τ j ), the subscript n represents slope p n  and the index j represents intercept tau τ. Here, each intercept tau τ j  is time t j  although the intercept can be in frequency domain or another space as illustrated by frequency-domain tau-p seismogram A n (ω j ). 
     Tau-p deconvolution or a tau-p transform can be achieved with a least squares inversion method including fitting frequency domain data such as frequency-offset seismogram D i (ω j ) to a tau-p model such as frequency-domain tau-p seismogram A n (ω j ). The inversion method is advantageous with respect to discretization of the continuous transform because it can handle irregularly spaced data, and it better handles the edges of the data (where the continuous version tries to fit a function which sharply transitions to zero at the edges). The least squares problem is 
     
       
         
           
             
               
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     where m=1, 2, . . . , M and M is an integer. Subscript “m” is the same as subscript “n”, but is referenced differently to distinguish transformation matrix R in  from adjoint transformation matrix R in   + that is given as adjoint transformation matrix R mi . The normal equations can be written in matrix form as (R + R)A=R + D 
     where the elements of transformation matrix R in  are given by R in =e iω     j     p     n     x     i      
     and the elements of adjoint transformation matrix R mi  are given by R mi =e −iω     j     p     m     x     i   . 
     The algebraic solution of the normal equations gives the frequency-p seismogram A n (ω j ) for a frequency ω j  and can be written as
 
 A =( R   +   R ) −1   R   +   D.  
 
     The frequency-p seismograms A n (ω j ) are then inverse Fourier transformed to give the tau-p seismogram for each frequency ω j  that are combined in tau-p seismogram A n (τ j ) for a range of times τ j . The matrix R + R is hereinafter referred to as combined transformation matrix S mn . 
     Stability and Noise Factor 
     To stabilize or constrain the solution for frequency-domain tau-p seismogram A n (ω j ) or otherwise provide that combined transformation matrix S mn  can be inverted, a frequency dependent noise factor ε 2  is added to each of the elements of or otherwise combined with combined transformation matrix S mn  to create an invertible combined transformation matrix X mn . In general, the frequency dependent noise factor ε 2  stabilizes the solution for frequency-domain tau-p seismogram A n (ω j ) by limiting the contributions of the small eigenvalues of the combined transformation matrix S mn . 
     For purposes of teaching, a frequency independent noise factor ε 1  and the frequency dependent noise factor ε 2  are described. The frequency independent noise factor ε 1  can be developed as a function of the average diagonal element of combined transformation matrix S mn  according to 
               ɛ   1     =       F     Y   p       ⁢       ∑   α     ⁢           ⁢     S   αα               
where Y p  is the number of eigenvalues (Y p =M) and F is the fraction of the average diagonal element to add as noise. For example, F can be one percent of the average diagonal element or 0.01. Since for a Hermitian matrix the average diagonal element is equal to the average eigenvalue, frequency independent noise factor ε 1  can create a floor such that all the eigenvalues are greater than zero. However, where frequency independent noise factor ε 1  is developed in this manner, a large number of zero-value eigenvalues can significantly reduce the value of frequency independent noise factor ε 1  to the point where frequency independent noise factor ε 1  fails to stabilize the solution for frequency-domain tau-p seismogram A n (ω j ) even though the eigenvalues are non-zero. It has been observed that the number of zero-value eigenvalues increases at lower frequencies ω j  and, accordingly, the solutions for frequency-domain tau-p seismogram A n (ω j ) at lower frequencies ω j  can be difficult to stabilize with frequency independent noise factor ε 1  as it is simply a function of the average diagonal element of combined transformation matrix S mn .
 
     To stabilize combined transformation matrix S mn  even at low frequencies ω j , the frequency dependent noise factor ε 2  can be used and is given by 
               ɛ   2     =       F     fdp   ⁢           ⁢   Δ   ⁢           ⁢     xY   p         ⁢       ∑   α     ⁢           ⁢       S   αα     .               
Here, a number of non-zero eigenvalues Y p,nonzero  is given by the denominator Y p,nonzero (f j )=f j  dp Δx Y p  and a value F is the frequency-independent fraction of the average non-zero diagonal element to add to combined transformation matrix S mn  as noise. Adding frequency dependent noise factor ε 2  to combined transformation matrix S mn  creates invertible combined transformation matrix X mn . Here, frequency f is given in units of Hertz instead of radians per second. Frequency f, ω can be given in either form as f=ω/(2*pi) and ω=2*pi*f. As above, the value F can be 0.01 or one percent.
 
     As frequency dependent noise factor ε 2  is a function of the average of the non-zero diagonal elements or eigenvalues of combined transformation matrix S mn , it is not substantially affected by an increase in the number of non-zero eigenvalues Y p,nonzero  at lower frequencies ω j . Frequency dependent noise factor ε 2  is a function of frequency f j  as the number of non-zero eigenvalues Y p,nonzero  is a function of frequency f j . As such, frequency dependent noise factor ε 2  stabilizes the solutions for frequency-domain tau-p seismogram A n (ω j ) even at lower frequencies f j . The solutions for frequency-domain tau-p seismogram A n (ω j ) can be given by
 
 A =( X ) −1   R   +   D  
 
     The inverse transform is a straight sum of plane waves given by D=RA. 
     Frequency dependent noise factor ε 2  stabilizes combined transformation matrix S mn , for example, by establishing a lowest value of an eigenvalue for combined transformation matrix S mn . This constrains the least squares fit where combined transformation matrix S mn  has a null space or zero-value eigenvalues. 
     Estimate of Number of Nonzero Eigenvalues 
     The development of the function for determining the number of non-zero eigenvalues Y p,nonzero  is now described. Referring to  FIGS. 10-11 , in the wavenumber-frequency (kf) domain, the slope (p) sampling interval dp can be determined according to largest frequency f max  and range of positions Δx between the minimum and maximum positions of the geophones  204 ,  208 . Such a p-sampling interval dp is adequate at largest frequency f max  and oversamples the kf domain at lower frequencies f j . 
     Referring to  FIG. 12 , for purposes of teaching, in contrast to the period in the spatial domain provided by range of positions Δx, a period L is introduced that can be derived as follows. A wavenumber (k) domain sampling interval dk produces the period L in the spatial domain as L=1/dk. The relationship between the p-domain and the k-domain is given as k=p*f such that, a p-domain sampling interval dp produces period L in the spatial domain as L=1/(f j *dp). Here, period L is a function of frequency f j . 
     When frequency f j  is largest frequency f max , spatial period L is substantially equal to range of positions Δx as L=Δx=1/(f max *dp(f max )). As mentioned above, dp is fixed according to largest frequency f max . For smaller frequencies f j , period L becomes large with respect to range of positions Δx and includes a null space outside of the model space of range of positions Δx. As such, the solution for frequency-domain tau-p seismogram A n (ω j ) may not be constrained to the data for range of positions Δx but may correspond to the null space. As an example, a wavelet v is illustrated that is zero at the data locations for range of positions Δx but is within spatial period L. Wavelet v has energy in the p-domain, but doesn&#39;t affect the fit to the data of time-depth seismogram D i (t j ). 
     For a given frequency f j , the ratio of the number of non-zero diagonal elements or eigenvalues Y p,nonzero  to the number of eigenvalues Y p  is substantially the same as the ratio of the size of range of positions Δx to the size of period L. This can be given as 
                 Y     p   ,   nonzero         Y   p       =         Δ   ⁢           ⁢   x     L     .           
Since L=1/(f*dp), the number of non-zero eigenvalues Y p,nonzero  is then
 
               Y     p   ,   nonzero       =         Y   p     ⁢       Δ   ⁢           ⁢   x     L       =       Y   p     ⁢   fdp   ⁢           ⁢   Δ   ⁢           ⁢   x             
as seen above in the equation for frequency dependent noise factor ε 2 . Alternatively, the ratio of the number of non-zero diagonal elements or eigenvalues Y p,nonzero  relative to the number of eigenvalues Y p  is substantially the same as the ratio of frequency f j  to largest frequency f max .
 
Curved Event
 
     In an example described above, the linear version of the tau-p transform is useful for removing the surface wave event  300  since it is a substantially linear event. However, since the primary reflection event  302  and other multiples are curved events, a use of curved lines (curves) rather than straight lines (slopes) is appropriate for additionally filtering and processing seismogram D i (t j ). In general, curved lines are appropriate if the features of the greatest interest in the original seismogram happen to be along curved rather than straight lines. There is a need to vary the noise factors with frequency in this case just as in the linear case. 
     When plane waves are used to build a curved event such as primary reflection event  302 , the input wavelets are related to the output wavelets by the square root of a derivative operator sqrt(iω), which comes in from the stationary phase integration. Using the formulation above, there is a difference between the tau-p domain and the time-offset domain of substantially 3 db/octave. This is undesirable for deconvolution in the tau-p domain. Specifically deconvolution is a least-squares operation. The derivation of the deconvolution filter on the unbalanced spectrum described above gives relatively too little weight to the low frequencies, leading to some artifacts. When these low frequency artifacts are boosted by the inverse transformation, they become more objectionable. To account for this difference, the output of the forward transform, frequency-domain tau-p seismogram A n (ω j ), is multiplied by the square root of a derivative operator sqrt(iω) to get a curved frequency-domain tau-p seismogram C n (ω j ) given by C n (ω j )=A n (ω j )√{square root over (iω)}. The sqrt(iω) factor in the transform definition preserves the spectrum and phase of the wavelet. Curved frequency-domain tau-p seismogram C n (ω j ) can then be subjected to filtering, processing, or deconvolution. Before an inverse transform is applied to transform a seismogram from tau-p space to k-f space, curved frequency-domain tau-p seismogram C n (ω j ) is divided by the square root of the derivative operator sqrt(iω) to get linear frequency-domain tau-p seismogram A n (ω j ). The inverse transform is then applied to linear frequency-domain tau-p seismogram A n (ω j ) as described above. 
     The processing methods described herein with respect to the data collected by the apparatus  200  of  FIG. 2  can similarly be applied to the data collected by the apparatus  99  of  FIG. 1  where data is collected at depths z instead of surface positions x. 
     The above-described embodiments are merely exemplary illustrations of implementations set forth for a clear understanding of the principles of the disclosure. Variations, modifications, and combinations may be made to the above-described embodiments without departing from the scope of the claims. All such variations, modifications, and combinations are included herein by the scope of this disclosure and the following claims.

Technology Classification (CPC): 6