WO2016063125A1

WO2016063125A1 - Imaging the near subsurface with surface consistent deconvolution operators

Info

Publication number: WO2016063125A1
Application number: PCT/IB2015/002121
Authority: WO
Inventors: Matthieu RETAILLEAU
Original assignee: Cgg Services Sa
Priority date: 2014-10-23
Filing date: 2015-10-20
Publication date: 2016-04-28

Abstract

A method for imaging a near subsurface (500) computes for each trace in a plurality of traces an average amplitude and an auto-correlation spectrum within a pre-determined time window (504) and performs surface decomposition of autocorrelation spectra and average amplitudes for all seismic traces in the plurality of traces to decompose the seismic traces into a plurality of components (506). A plurality of predictive deconvolution operators is derived for the auto-correlation spectra of the plurality of components (508), and an image of the near subsurface is generated using the predictive deconvolution operators (512).

Description

IMAGING THE NEAR SUBSURFACE WITH SURFACE CONSISTENT

DECONVOLUTION OPERATORS

CROSS REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority and benefit from U.S. Provisional Patent Application No. 62/067,457, filed October 23, 2014, for "Imaging the Near

Subsurface with Surface Consistent Deconvolution Operators", the entire contents of which is incorporated herein by reference. TECHNICAL FIELD

[0002] Embodiments of the subject matter disclosed herein generally relate to methods and systems for seismic signal processing of multiple reflection signals in the near-surface of the subsurface. BACKGROUND

[0003] For land-based seismic surveys, obtaining a primary image of the shallow subsurface of acceptable quality is a challenge. Primary reflections from standard seismic surveys suffer from well-known limitations including interfering ground roll and refracted arrivals, poor fold, irregular offset distribution and sensitivity to velocity errors. Optimal imaging of the near subsurface would require densification of sources and/or receiver, which prohibitively increases the acquisition cost.

[0004] A shallow velocity model can be inverted from ground roll or first break arrivals present on any seismic records. However, the results need to be calibrated to a compressional velocity model using auxiliary information like up-hole data. Non- seismic geophysical methods such as resistivity or electro-magnetic can produce models that also need careful calibration with seismic data. None of these methods yield direct access to the reflectivity of the very near subsurface that generates most of the distortions of the seismic signal on land-based or in shallow water seismic surveys.

[0005] One of the goals of deconvolution, a standard process in time processing, is to attenuate the predictable part of the seismic signal. Seismic ringing is typically caused by multiple reflections occurring between a shallow geological interface and the surface or between two shallow reflectors. Thus the deconvolution operators contain information about the shallow reflectivity. In shallow water environment, it was shown in Moore, L. and Bisley, R, "Multiple Attenuation In Shallow-Water Situations", 68th meeting, EAGE, Expanded Abstracts, (2006) and in Yang, K., and Hung, B, "Shallow Water Demultiple With Seafloor Reflection

Modeling Using Multichannel Prediction Operator", SEG Technical Program

Extended Abstracts 2012: 1 -5 (2012) that the water bottom reflectivity could be retrieved from multi-channel predictive deconvolution operators and could be used to model water layer related multiples.

[0006] The need still exists, however, for improved imaging of the near surface subsurface that can account for and attenuate seismic ringing caused by multiple reflections in the near surface subsurface.

SUMMARY OF THE INVENTION

[0007] Exemplary embodiments are directed to systems and methods that use surface consistent deconvolution operators for imaging the shallow subsurface. The spatial variability of the shallow reflectivity is captured by the spatial variability of the deconvolution operators, which are computed through a surface consistent decomposition approach. This approach provides direct access to reflectivity up to a few milliseconds below the ground, straightforward quality control and integration with a primary reflectivity cube, integration in standard workflows and zero phase output, because deconvolution operators are computed from auto-correlations. The reflectivity is derived from high fold, good quality reflection data that are free from ground roll and refracted arrival. Imaging the subsurface works well in the presence of high impedance contrasts in the near subsurface that are responsible for multiple contamination and signal distortions.

[0008] Exemplary embodiments are directed to a method for imaging a near subsurface. For each trace in a plurality of traces an average amplitude and an autocorrelation spectrum are computed within a pre-determined time window. In one embodiment, the pre-determined time window is selected to correspond to a location below the near subsurface to be imaged. In one embodiment, the pre-determined time window is selected to be from about 400ms to about 2500ms.

[0009] Surface decomposition of autocorrelation spectra and average amplitudes is performed for all seismic traces in the plurality of traces to decompose the seismic traces into a plurality of components. In one embodiment, simultaneous surface decomposition of autocorrelation spectra and average amplitudes is performed for all seismic traces in the plurality of traces. A plurality of predictive deconvolution operators is derived for the auto-correlation spectra of the plurality of components, and an image of the near subsurface is generated using the predictive deconvolution operators. Having generated the image of the near subsurface, the image of the near subsurface is output and stored.

[0010] In one embodiment, the plurality of predictive deconvolution operators includes a source predictive deconvolution operator derived from source autocorrelations of the plurality of components and receiver predictive deconvolution operators derived from receiver auto-correlations from the plurality of components. In one embodiment, generating the image of the near subsurface includes applying signal processing to the deconvolution operators. The signal processing includes de- noising, regularization, time static shift, zero-offset time migration and combinations thereof.

[0011] Exemplary embodiments are also directed to a computer-readable medium containing computer-executable code that when read by a computer causes the computer to perform a method for imaging a near subsurface. According to this method and for each trace in a plurality of traces an average amplitude and an autocorrelation spectrum are computed within a pre-determined time window. In one embodiment, the pre-determined time window is selected to correspond to a location below the near subsurface to be imaged. In one embodiment, the pre-determined time window is selected to be from about 400ms to about 2500ms.

[0012] Surface decomposition of autocorrelation spectra and average amplitudes is performed for all seismic traces in the plurality of traces to decompose the seismic traces into a plurality of components. In one embodiment, simultaneous surface decomposition of autocorrelation spectra and average amplitudes is performed for all seismic traces in the plurality of traces. A plurality of predictive deconvolution operators is derived for the auto-correlation spectra of the plurality of components, and an image of the near subsurface is generated using the predictive deconvolution operators. Having generated the image of the near subsurface, the image of the near subsurface is output and stored.

[0013] In one embodiment, the plurality of predictive deconvolution operators includes a source predictive deconvolution operator derived from source autocorrelations of the plurality of components and receiver predictive deconvolution operators derived from receiver auto-correlations from the plurality of components. In one embodiment, generating the image of the near subsurface includes applying signal processing to the deconvolution operators. The signal processing includes de- noising, regularization, time static shift, zero-offset time migration and combinations thereof.

[0014] Exemplary embodiments are also directed to a computing system for performing a method for imaging a near subsurface. The computing system includes a storage device containing a plurality of traces from a seismic survey dataset and a processer in communication with the storage device. The processor is configured to compute for each trace in a plurality of traces an average amplitude and an autocorrelation spectrum within a pre-determined time window, perform surface decomposition of autocorrelation spectra and average amplitudes for all seismic traces in the plurality of traces to decompose the seismic traces into a plurality of components, derive a plurality of predictive deconvolution operators for the auto- correlation spectra of the plurality of components and generate an image of the near subsurface using the predictive deconvolution operators.

[0015] In one embodiment, the processor is further configured to select the pre-determined time window to correspond to a location below the near subsurface to be imaged. In one embodiment, the processor is further configured to output and store the image of the near subsurface. In one embodiment, the processor in generating the image of the near subsurface is further configured to apply signal processing to the deconvolution operators. In one embodiment, this signal processing includes de-noising, regularization, time static shift, zero-offset time migration and combinations thereof. In one embodiment, the processor is further configured to perform simultaneous surface decomposition of autocorrelation spectra and average amplitudes for all seismic traces in the plurality of traces.

BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate one or more embodiments and, together with the description, explain these embodiments. In the drawings:

[0017] Figure 1 illustrates a vertical section parallel to the receiver lines of a seismic dataset;

[0018] Figure 2 illustrates a time slice a given depth below the surface; [0019] Figure 3 illustrates another vertical section parallel to the receiver lines of a seismic dataset;

[0020] Figure 4 illustrates another vertical section parallel to the receiver lines of a seismic dataset;

[0021] Figure 5 is a flowchart of an embodiment of a method for imaging the near subsurface; and

[0022] Figure 6 is a schematic representation of an embodiment of a computing system for use in executing a method for imaging the near subsurface. DETAILED DESCRIPTION

[0023] The following description of the embodiments refers to the accompanying drawings. The same reference numbers in different drawings identify the same or similar elements. The following detailed description does not limit the invention.

Instead, the scope of the invention is defined by the appended claims. Some of the following embodiments are discussed, for simplicity, with regard to local activity taking place within the area of a seismic survey. However, the embodiments to be discussed next are not limited to this configuration, but may be extended to other arrangements that include regional activity, conventional seismic surveys, etc.

[0024] Reference throughout the specification to "one embodiment" or "an embodiment" means that a particular feature, structure or characteristic described in connection with an embodiment is included in at least one embodiment of the subject matter disclosed. Thus, the appearance of the phrases "in one embodiment" or "in an embodiment" in various places throughout the specification is not necessarily referring to the same embodiment. Further, the particular features, structures or characteristics may be combined in any suitable manner in one or more embodiments.

[0025] Exemplary embodiments of systems and methods obtain a shallow velocity model and image the near or shallow subsurface for land-based and shallow water seismic surveys. Initially, a seismic survey dataset is obtained for a given subsurface. Any suitable method of conducting land-based and shallow water seismic surveys can be used. The obtained seismic survey dataset includes a plurality of seismic traces associated with sources in the subsurface and at least one receiver. Having obtained the seismic survey dataset, the average amplitude (gain), i.e., the arithmetic average, which is also referred to as the amplitude scalar, and auto- correlation spectrum of each trace in the plurality of traces is computed in a predetermined time window selected to lie below the shallow sub-surface section to be imaged or to include traces having an acceptable signal to noise ratio. In one embodiment, the pre-determined time window is selected to be from about 400 ms to about 2500 ms at zero offset and following primary reflections up to about 2500 m offset. In general, the time window depends on the data and is chosen by the processor to have good signal to noise ratio, below the multiple reflection generators. Any suitable method for determining the autocorrelation of a seismic trace known and available in the art can be used. In general, autocorrelation compares a seismic signal to itself across a plurality of time shifts or time lags in order to identify repeating periods within the seismic trace in the presence of noise.

[0026] Having determined the average amplitude or amplitude scalar of each seismic trace and an auto-correlation spectrum of each seismic trace within the desired pre-determined time window, a surface decomposition of auto-correlation spectra and amplitude scalars or average gains is performed iteratively for all seismic traces to yield or decompose the plurality of seismic traces into a plurality of components or terms. In one embodiment, surface decomposition is conducted separately, i.e., non- simultaneously, for the auto-correlation spectra and amplitude scalars. Alternatively, a joint simultaneous surface decomposition of auto-correlation spectra and amplitude scalars or average gains is performed. Preferably, the auto-correlation spectra and the amplitude scalars are decomposed into four components, a global average component or term, a source component or term, a receiver component or term and an offset component or term. The amplitude scalars and auto-correlation spectra are

decomposed into these four components because in a surface-consistent model the average amplitude and autocorrelation spectra values are described as a combination of a global average term that expresses an average waveform, a source term that includes the shallow or near-surface effects on the downgoing or source wavefront, a receiver term the includes the shallow or near-surface effects on the upward reflected wavefront and an offset term that includes any offset-related effects.

[0027] Following decomposition, a plurality of predictive deconvolution operators is derived for the auto-correlation spectra of the plurality of components. A seismic record can be seen as the result of the convolution of a wavelet with the earth reflectivity. As used herein, a deconvolution operator is a filter that removes a wavelet from the recorded seismic by reversing the process of convolution. The deconvolution operator can be computed as a Wiener filter that minimizes in a least square sense the difference between the convolution result and the desired output. In one embodiment, the deconvolution operator is computed from the auto-correlation of the wavelet or the auto-correlation of the seismic trace, assuming the earth reflectivity has a white spectrum. The predictive deconvolution operators remove only the predictable part of the wavelet after a certain time gap. In one embodiment, shot or source predictive deconvolution operators are derived from shot or source autocorrelations, and receiver predictive deconvolution operators are derived from receiver autocorrelations.

[0028] The predictive deconvolution operators are used to generate an image of the near subsurface because they embed the shallow subsurface reflectivity which generated surface or internal multiple reverberations, causing ringing in the seismic record. The image can then be outputted, for example, displayed to an operator, and saved in one of more databases.

[0029] In one embodiment, one of more signal processing techniques are applied to the deconvolution operators in order to generate the image of the near subsurface. The deconvolution operators, after signal processing, are then looked at or evaluated to generate the desired image of the near subsurface. The operators have a standard seismic record format regularly sampled in time. They are exported into a 2D/3D visualization and interpretation station in which horizontal or vertical sections can be visualized, horizons picked, attributes overlaid, extracted, etc. The image can be blended with the standard volume obtained after migrating the seismic traces. It replaces the noisy near surface image obtained from standard processing. This signal processing includes de-noising, regularization to a surface grid, time state shift, zero offset time migration and combinations thereof.

[0030] In one embodiment of signal processing and for purposes of quality control on the source and receiver predictive deconvolution operators, these predictive deconvolution operators are gridded and sorted on the sub-surface grid. In one embodiment, the source and receiver deconvolution operators are extracted and regularized to their nominal acquisition grids, i.e. 250 m x 25 m for the receivers and 50 m x 50 m for the shots. The shot operators are also interpolated onto the 25 m x 25 m bin grid via an irregular Fourier transform. After basic processing, e.g., 10 Hz to 60 Hz band pass filter and a single gate scaling), the predictive deconvolution operators are compared to a migrated stack at surface datum. The resulting images from the deconvolution operators should match closely with the primary reflections down to about 300 ms in depth. Generally, the boundaries of the main layers and faults appear clearly and often sharper than primary reflections.

[0031] This method can be applied for the processing of land, ocean bottom node (OBN) and ocean bottom seismic data. The resulting high-resolution images of the near sub-surface allow more precise shallow geological model building for depth imaging, better static anomaly detection/correction, more accurate surface or internal multiple predictions through a model-based approach, estimation and correction of seismic signal phase distortions on the receiver and source sides.

[0032] In two exemplary applications of the method, herein referred to as A and B, seismic datasets were obtained from broadband, dense, wide-azimuth surveys. Both are characterized by large maximum cross-line offsets, i.e., greater than 6000 m, and huge folds, around 8500 in a 25 m x 25 m bin. On both surveys, a broadband vibroseis source was used with emitted frequencies ranging from 1 .5 Hz to 86 Hz.

[0033] A simultaneous joint inversion of surface-consistent scalars and deconvolution operators was conducted. First the average amplitude or amplitude scalar and auto-correlation spectrum of each individual trace in a time and offset window showing good signal to noise ratio were determined. The auto-correlation spectra and amplitude scalars were simultaneously and iteratively decomposed into four components: global mean or average term, source term, receiver term, and offset term. Finally, predictive deconvolution operators were derived from the re-combined autocorrelation spectra using the classical Wiener algorithm and applied to the data in the seismic survey dataset. Looking at stacked data, the impact of the surface- consistent deconvolution step seemed fair. In both cases, the wavelet ringing was attenuated, but it was impossible to spot the attenuation of obvious short or long wavelength multiples.

[0034] The source and receiver prediction operators were extracted and regularized to their nominal acquisition grids, i.e., 250 m x 25 m for the receivers on both surveys, 50 m x 50 m for the shots of survey A, and 25 m x 100 m for the shots of survey B. On survey A, the shot operators were also interpolated onto the 25 m x 25 m bin grid by means of an irregular Fourier transform as described in Poole, G, "5D Data Reconstruction Using The Anti-Leakage Fourier Transform", 72nd EAGE Conference & Exhibition, Extended Abstracts (2010). After a basic processing sequence, 10 Hz to 60 Hz band-pass filter and a single gate scaling, the operators were shifted to the same datum as the current migrated stack by applying twice the full shot or receiver static corrections. Finally, they were migrated using the current migration velocity field.

[0035] Referring to Figures 1 -4, results for surveys A and B are illustrated and are discussed below. In general for both survey A and survey B, a good match is observed between the events seen on the operators and the events seen on the primary reflections down to 300 ms, which means that surface multiples were captured by the prediction operators. The image from the operators looks much cleaner, with continuous, structurally consistent events appearing almost up to the surface. The near surface image from primary reflections suffered from the very low and irregular near- offset coverage. This limited not only the accuracy of migration velocity picking, but also the accuracy of noise filtering. The primary reflections are still contaminated by residual ground roll and refracted energy. On the contrary, the prediction operators were derived in a deeper time window, from high fold, good quality reflectivity data.

[0036] Referring to Figure 1 , for survey A, vertical section parallel to the receiver lines is illustrated for a migrated stack 102, migrated receiver deconvolution operators 104, migrated source deconvolution operators 106 and migrated source deconvolution operators after interpolation 108. As illustrated in Figure 1 for survey A, in a corridor where a major regional SE-NW fault system affects the entire section up to the ground surface, the migrated deconvolution operators are able to image very shallow anticline and syncline structures which are almost invisible on the migrated stack of primary reflections 102. Both source and receiver operators display interfering, ringing, flat events that correspond to the correction of coupling or distortion effects. They usually appear stronger on the source side 106, 108, below the syncline or anticline structure.

[0037] Referring to Figure 2, for survey A, time slice approximately 48 ms below the average ground surface is illustrated for a migrated stack 202, migrated source deconvolution operators after interpolation 204, migrated source deconvolution operators 206 and migrated receiver deconvolution operators 208. The dense sampling of shot operators in both directions allows the construction of a more precise three- dimensional (3D) image of the shallow subsurface, as illustrated by the time slices in Figure 2. However, there are a few areas, as large as 10 km², which the vibrators could not access, but where the geophones could be laid out. In these cases, despite the absence of near traces, the reflectivity of the shallow subsurface can be retrieved from the receiver operators as illustrated in Figure 3, which illustrates for survey A, a vertical section parallel to the receiver lines for a migrated stack 302, migrated receiver deconvolution operators 304, migrated source deconvolution operators 306 and migrated source deconvolution operators after interpolation 308.

[0038] Referring to Figure 4 for survey B, a vertical section parallel to the receiver line is illustrated for a migrated stack 402, migrated receiver deconvolution operators 404 and migrated source deconvolution operators 406. The well log 41 1 shows the P velocity. For survey B, the imaging uplift brought by the operators can be seen in Figure 4. In particular the top and base of a high-velocity carbonate layer, which is known regionally as a major generator of surface and internal multiples, appears clearer and more continuous on the operators compared to the migrated section 408. The improvement is more pronounced when this layer is approaching the near surface on the left-hand side of the picture. A very good correlation with a shallow well velocity log 41 1 is also observed. Indeed, a sharp velocity increase observed on the well log corresponds to a strong reflector on the seismic section 410.

[0039] This example demonstrates that a continuous and accurate image of the shallow subsurface of land surveys can be retrieved from one-dimensional (1 D) shot and receiver prediction operators. The high source and receiver density in the example allowed the construction of 3D finely sampled near-surface volumes. The modern broadband sweep, without side lobes, was favorable to provide clean operators in which reflections could be extracted up to very shallow times.

[0040] These high-resolution images of the subsurface are helpful in various ways including detection/correction of static or phase anomalies, creation of shallow velocity models for depth imaging, and creation of shallow reflectivity models for multiple prediction and attenuation

[0041] Referring now to Figure 5, exemplary embodiments are directed to a method for imaging a near subsurface 500. Seismic datasets are obtained 502. Each seismic dataset includes a plurality of seismic traces. Any suitable method known and available in the art for obtaining or generating seismic data can be used.

[0042] An average amplitude and an auto-correlation spectrum are computed within a pre-determined time window of a plurality of traces from a seismic dataset 504. In one embodiment, the pre-determined time window is selected to correspond to a location below the near subsurface to be imaged. Alternatively, the pre-determined time window is selected to be from about 400ms to about 2500ms. Surface decomposition of autocorrelation spectra and average amplitudes is performed for all seismic traces in the plurality of traces 506 to decompose the seismic traces into a plurality of components. Surface decomposition can be conducted simultaneously for the autocorrelation spectra and average amplitudes. Alternatively, the decompositions are performed separately or sequentially. For example, decomposition is performed first for the average amplitudes followed by the autocorrelations.

[0043] A plurality of predictive deconvolution operators are derived for the autocorrelation spectra of the plurality of components 508. This plurality of predictive deconvolution operators includes, but is not limited to, a source predictive

deconvolution operator derived from source auto-correlations of the plurality of components and receiver predictive deconvolution operators derived from receiver auto-correlations from the plurality of components.

[0044] In one embodiment, signal processing is applied to the plurality of derived deconvolution operators 510. Signal processing includes, but is not limited to, de- noising, signal to noise enhancement, spatial regularization, time static shift, re- datuming through static shift, zero offset time migration and any combination of these signal processing techniques. De-noising includes filtering unwanted frequencies, applying an amplitude scalar and applying spatial dip filtering. Regularization builds a three-dimensional (3D) image where the operators are positioned on the same spatial regular grid as imaging grid. This can be done through simple spatial shift or more sophisticated techniques such as anti-leakage Fourier transform. At this stage, receiver and source operators can be merged into one single volume. Time static shift uses or applies the same static time shift applied on main seismic traces. This facilitates straight comparison between the primaries (main flow) and the operators. Static shift is applied to the deconvolution operators because the assumption is the deconvolution operators captured surface multiples, whose time origin is the surface of the Earth as in field seismic records. Therefore, the same static time shift, i.e., to a desired datum or time origin, is applied on acquired seismic data and deconvolution operators. Zero offset time migration time migrates the operators using a time migration velocity field.

[0045] An image is then generated of the near subsurface using the predictive deconvolution operators 512. The resulting image of the near subsurface is then output, i.e., displayed to an operator, and stored for future use and reference 516. Any suitable method for displaying and storing images and image data can be used.

Therefore, an improved image of the subsurface is generated, in particular in the near subsurface. This improves the field of seismic exploration and imaging of the subsurface and the use of seismic surveys to locate oil, gas and petroleum reservoirs for drilling and production.

[0046] In one embodiment, the deconvolution operators can also be used to control the quality of phase and static corrections already applied to seismic traces. For example, if applying a static correction distorts the image of the operators, then these static likely corrections did not correct for heterogeneities in the near subsurface, as should have happened, but rather compensated deeper seismic ray path complexities. The deconvolution operators can be used to compute phase and static corrections, to pick shallow events to build a velocity model of the near subsurface and to generate pre-stack models of multiple reflections to be subtracted from seismic data. The operators' volume provides an accurate image of the reflectivity of the near surface. Field seismic records can be propagated in this clean 3D reflectivity model (e.g. via a wave field extrapolation technique) to produce pre-stack models of internal or surface seismic multiples generated by the near surface. These multiple models can be subtracted from the raw field records, e.g. via an adaptive subtraction approach.

[0047] Referring to Figure 6, exemplary embodiments are directed to a computing system 600 for performing a method for imaging a near subsurface. In one embodiment, a computing device for performing the calculations as set forth in the above-described embodiments may be any type of computing device capable of obtaining, processing and communicating seismic traces from one or more seismic datasets associated with seismic surveys. The computing system 600 includes a computer or server 602 having one or more central processing units 604 in

communication with a communication module 606, one or more input/output devices 610 and at least one storage device 608.

[0048] The communication module is used to obtain seismic datasets of a subsurface structure. Each seismic dataset includes a plurality of seismic traces. The plurality of seismic datasets can be obtained, for example, through the input/output devices. The obtained plurality of seismic datasets and seismic traces are stored in the storage device. In addition, the storage device is used to store updated seismic traces, intermediate data generated during the updating of the seismic traces, images generated of the near subsurface and the computer executable code that is used to execute the method for imaging a near subsurface. The input/output device can also be used to communicate or display outputs and updated seismic traces and imaged near subsurface, for example, to a user of the computing system. [0049] The processer is in communication with the communication module and configured to compute for each trace in a plurality of traces an average amplitude and an auto-correlation spectrum within a pre-determined time window, to perform simultaneous surface decomposition of autocorrelation spectra and average amplitudes for all seismic traces in the plurality of traces to decompose the seismic traces into a plurality of components, to derive a plurality of predictive deconvolution operators for the auto-correlation spectra of the plurality of components and to generate an image of the near subsurface using the predictive deconvolution operators.

[0050] The processor is further configured to select the pre-determined time window to correspond to a location below the near subsurface to be imaged. In one embodiment, the plurality of components includes a global average component, a source component, a receiver component and an offset component. The plurality of predictive deconvolution operators includes a source predictive deconvolution operator derived from source auto-correlations of the plurality of components and receiver predictive deconvolution operators derived from receiver auto-correlations from the plurality of components.

[0051] In one embodiment, the processor in generating the image of the near subsurface is further configured to apply the deconvolution operators to the seismic traces and to generate updated seismic traces having attenuated seismic ringing.

[0052] Suitable embodiments for the various components of the computing system are known to those of ordinary skill in the art, and this description includes all known and future variants of these types of devices. The communication module provides for communication with other computing systems, databases and data acquisition systems across one or more local or wide area networks 612. This includes both wired and wireless communication. Suitable input/output devices include keyboards, point and click type devices, audio devices, optical media devices and visual displays.

[0053] Suitable storage devices include magnetic media such as a hard disk drive (HDD), solid state memory devices including flash drives, ROM and RAM and optical media. The storage device can contain data as well as software code for executing the functions of the computing system and the functions in accordance with the methods described herein. Therefore, the computing system 600 can be used to implement the methods described above associated with performing a method for imaging a near subsurface. Hardware, firmware, software or a combination thereof may be used to perform the various steps and operations described herein.

[0054] Methods and systems in accordance with exemplary embodiments can be hardware embodiments, software embodiments or a combination of hardware and software embodiments. In one embodiment, the methods described herein are implemented as software. Suitable software embodiments include, but are not limited to, firmware, resident software and microcode. In addition, exemplary methods and systems can take the form of a computer program product accessible from a computer-usable or computer-readable medium providing program code for use by or in connection with a computer, logical processing unit or any instruction execution system. In one embodiment, a machine-readable or computer-readable medium contains a machine-executable or computer-executable code that when read by a machine or computer causes the machine or computer to perform a method for imaging a near subsurface in accordance with exemplary embodiments and to the computer-executable code itself. The machine-readable or computer-readable code can be any type of code or language capable of being read and executed by the machine or computer and can be expressed in any suitable language or syntax known and available in the art including machine languages, assembler languages, higher level languages, object oriented languages and scripting languages.

[0055] As used herein, a computer-usable or computer-readable medium can be any apparatus that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. Suitable computer-usable or computer readable mediums include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or

semiconductor systems (or apparatuses or devices) or propagation mediums and include non-transitory computer-readable mediums. Suitable computer-readable mediums include, but are not limited to, a semiconductor or solid state memory, magnetic tape, a removable computer diskette, a random access memory (RAM), a read-only memory (ROM), a rigid magnetic disk and an optical disk. Suitable optical disks include, but are not limited to, a compact disk - read only memory (CD-ROM), a compact disk - read/write (CD-R/W) and DVD.

[0056] The disclosed exemplary embodiments provide a computing device, software and method for method for inversion of multi-vintage seismic data. It should be understood that this description is not intended to limit the invention. On the contrary, the exemplary embodiments are intended to cover alternatives, modifications and equivalents, which are included in the spirit and scope of the invention. Further, in the detailed description of the exemplary embodiments, numerous specific details are set forth in order to provide a comprehensive understanding of the invention. However, one skilled in the art would understand that various embodiments may be practiced without such specific details.

[0057] Although the features and elements of the present exemplary embodiments are described in the embodiments in particular combinations, each feature or element can be used alone without the other features and elements of the embodiments or in various combinations with or without other features and elements disclosed herein. The methods or flowcharts provided in the present application may be implemented in a computer program, software, or firmware tangibly embodied in a computer-readable storage medium for execution by a geophysics dedicated computer or a processor.

[0058] This written description uses examples of the subject matter disclosed to enable any person skilled in the art to practice the same, including making and using any devices or systems and performing any incorporated methods. The patentable scope of the subject matter is defined by the claims, and may include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims.

Claims

WHAT IS CLAIMED IS:

1 . A method for imaging a near subsurface (500), the method comprising: computing for each trace in a plurality of traces an average amplitude and an auto-correlation spectrum within a pre-determined time window (504);

performing surface decomposition of autocorrelation spectra and average amplitudes for all seismic traces in the plurality of traces to decompose the seismic traces into a plurality of components (506);

deriving a plurality of predictive deconvolution operators for the autocorrelation spectra of the plurality of components (508); and

generating an image of the near subsurface using the predictive

deconvolution operators (512).

2. The method of claim 1 , further comprising selecting the pre-determined time window to correspond to a location below the near subsurface to be imaged.

3. The method of claim 1 , further comprising selecting the pre-determined time window to be from about 400ms to about 2500ms.

4. The method of claim 1 , wherein performing the surface decomposition comprises performing simultaneous surface decomposition of autocorrelation spectra and average amplitudes for all seismic traces in the plurality of traces.

5. The method of claim 4, wherein the plurality of predictive deconvolution operators comprises a source predictive deconvolution operator derived from source auto-correlations of the plurality of components and receiver predictive deconvolution operators derived from receiver auto-correlations from the plurality of components.

6. The method of claim 1 , wherein generating the image of the near subsurface further comprises applying signal processing to the deconvolution operators.

7. The method of claim 6, wherein applying signal processing to the deconvolution operators comprises de-noising, regularization, time static shift, zero- offset time migration or combinations thereof

8. The method of claim 1 , wherein the method further comprises outputting and storing the image of the near subsurface.

9. A computer-readable medium containing computer-executable code that when read by a computer causes the computer to perform a method for imaging a near subsurface (500), the method comprising:

computing for each trace in a plurality of traces an average amplitude and an auto-correlation spectrum within a pre-determined time window (504);

generating an image of the near subsurface using the predictive

deconvolution operators (512).

10. The computer-readable medium of claim 9, further comprising selecting the pre-determined time window to correspond to a location below the near subsurface to be imaged.

1 1 . The computer-readable medium of claim 9, wherein performing the surface decomposition comprises performing simultaneous surface decomposition of autocorrelation spectra and average amplitudes for all seismic traces in the plurality of traces.

12. The computer-readable medium of claim 9, wherein the method further comprises outputting and storing the image of the near subsurface.

13. The computer-readable medium of claim 8, wherein generating the image of the near subsurface further comprises applying signal processing to the deconvolution operators.

14. The computer-readable medium of claim 13, wherein applying signal processing to the deconvolution operators comprises de-noising, regularization, time static shift, zero-offset time migration or combinations thereof.

15. A computing system (600) for performing a method for imaging a near subsurface (500), the computing system comprising:

a storage device comprising a plurality of traces from a seismic survey dataset (608); and

a processer (604) in communication with the storage device and configured to:

compute for each trace in a plurality of traces an average amplitude and an auto-correlation spectrum within a pre-determined time window (504);

perform surface decomposition of autocorrelation spectra and average amplitudes for all seismic traces in the plurality of traces to decompose the seismic traces into a plurality of components (506);

derive a plurality of predictive deconvolution operators for the autocorrelation spectra of the plurality of components (508); and

generate an image of the near subsurface using the predictive deconvolution operators (512).

16. The computing system of claim 15, wherein the processor is further configured to select the pre-determined time window to correspond to a location below the near subsurface to be imaged.

17. The computing system of claim 15, wherein the processor is further configured to output and store the image of the near subsurface.

18. The computing system of claim 15, wherein the processor in generating the image of the near subsurface is further configured to apply signal processing to the deconvolution operators.

19. The computing system of claim 17, wherein the signal processing comprises de-noising, regularization, time static shift, zero-offset time migration or combinations thereof.

20. The computing system of claim 15, wherein the processor is further configured to perform simultaneous surface decomposition of autocorrelation spectra and average amplitudes for all seismic traces in the plurality of traces.