Abstract: The invention includes a method for reducing noise in migration of seismic data, particularly advantageous for imaging by simultaneous encoded source reverse-time migration (SS-RTM). One example embodiment includes the steps of obtaining a plurality of initial subsurface images; decomposing each of the initial subsurface images into components; identifying a set of components comprising one of (i) components having at least one substantially similar characteristic across the plurality of initial subsurface images, and (ii) components having substantially dissimilar characteristics across the plurality of initial subsurface images; and generating an enhanced subsurface image using the identified set of components. For SS-RTM, each of the initial subsurface images is generated by migrating several sources simultaneously using a unique random set of encoding functions. Another embodiment of the invention uses SS-RTM for velocity model building.

Abstract: Method for correcting OBC or deep-towed seismic streamer data for surface-related multiple reflections. The measured pressure data, preferably after conditioning (71), are simulated using a forward model that includes a water propagation operator between source locations and receiver locations and a term representing primary impulse responses (72). Other terms include direct arrivals and source wavelets. Iterative optimization of an objective function is used to minimize the difference between measured and simulated data, updating the primary impulse response term and optionally the source wavelets term each iteration cycle (73). The converged primary impulses (74) are used to construct simulated multiples and direct arrivals (75), which can be subtracted from the measured data. Optionally the measured data might be blended during the forward simulation (72), to save computational costs in the forward simulation (72) and in the inversion (73).

Abstract: A method for estimating velocity dispersion in seismic surface waves in massive 3-D data sets (401) that improves upon auto-picking of a curve along the peak or ridge of the magnitude of the beam-formed field (402). The seismic data are transformed to the frequency-slowness domain, where nonlinear constrained optimization is performed on the transformed data. The optimization matches a nonlinear mathematical parametric model (403) of a beam-formed field to that in the transformed data, adjusting the parameters each iteration to reduce mismatch (404). Dispersion curves are determined by the center of the beam in the optimized models (405). A preferred nonlinear parametric mathematical model is a Gaussian-shaped beam or a cosine-tapered boxcar beam.

Abstract: The invention is a method for extrapolating missing near-offset seismic data (101) so that the data may be used, for example, in SRME or another multiple-reflection elimination method. The invention uses the reciprocity principle (102) to relate two seismic states (acoustic or elastic) that can occur in a time-invariant, bounded domain in space. One of these states represents the physical experiment for the acquisition of the actual seismic data where near-offset traces are missing, and the other state represents a synthetic experiment with no missing near offset traces, computer-generated on a much simpler earth model. The reciprocity relationship used to relate these two states is iteratively inverted for the missing near-offset traces (103), preferably using only part of the synthetic data (102) so as to reduce inversion artifacts. The reference model acts as a constraint on the near-offset extrapolation.

Abstract: Method for building a seismic imaging velocity model, particularly at the boundary of a geo-body, and to perform imaging, by taking into account the elastic reflection and scattering information in the seismic data. More illumination of the base and flanks (or in general, the boundary) of the geo-body is provided from (a) inside of the geo-body (502), with elastically converted waves at the geo-body boundary used (via elastic RTM flooding); and (b) from outside the geo-body (503), by utilizing prism waves with elastic RTM to handle the phase correctly in the model building step. The increased illumination and correct elastic phase are used for geo-body boundary determination. Elastic RTM is then applied (505), along with the elastically derived imaging velocity model, to maximize the use of elastic energy in the imaging step, and to obtain the correct image with the correct phase.

Abstract: Method for building a seismic imaging velocity model, particularly at the boundary of a geo-body, and to perform imaging, by taking into account the elastic reflection and scattering information in the seismic data. More illumination of the base and flanks (or in general, the boundary) of the geo-body is provided from (a) inside of the geo-body (502), with elastically converted waves at the geo-body boundary used (via elastic RTM flooding); and (b) from outside the geo-body (503), by utilizing prism waves with elastic RTM to handle the phase correctly in the model building step. The increased illumination and correct elastic phase are used for geo-body boundary determination. Elastic RTM is then applied (505), along with the elastically derived imaging velocity model, to maximize the use of elastic energy in the imaging step, and to obtain the correct image with the correct phase.

Abstract: Method for correcting OBC or deep-towed seismic streamer data for surface-related multiple reflections. The measured pressure data, preferably after conditioning (71), are simulated using a forward model that includes a water propagation operator between source locations and receiver locations and a term representing primary impulse responses (72). Other terms include direct arrivals and source wavelets. Iterative optimization of an objective function is used to minimize the difference between measured and simulated data, updating the primary impulse response term and optionally the source wavelets term each iteration cycle (73). The converged primary impulses (74) are used to construct simulated multiples and direct arrivals (75), which can be subtracted from the measured data. Optionally the measured data might be blended during the forward simulation (72), to save computational costs in the forward simulation (72) and in the inversion (73).

Abstract: Embodiments use seismic processing methods that account for the spatial variability of surface wave velocities. Embodiments analyze surface wave properties by rapidly characterizing spatial variability of the surface waves in the seismic survey data (302). Filtering criteria are formed using the spatial variability of the surface waves (204). The filtering criteria can then be used to remove at least a portion of the surface waves from the seismic data (206, 319). The rapid characterization involves estimating a local group velocity of the surface waves by cross-correlation of the analytic signals (302).

Abstract: Embodiments are directed to systems and methods (200, 300) that enable spatial variability of surface waves to be accounted for in dispersion correction in seismic data processing. This yields superior surface wave noise mitigation, with reduced likelihood of attenuating signal. Embodiments are operative with spatially inhomogeneous media.

Abstract: The invention is a method for extrapolating missing near-offset seismic data (101) so that the data may be used, for example, in SRME or another multiple-reflection elimination method. The invention uses the reciprocity principle (102) to relate two seismic states (acoustic or elastic) that can occur in a time-invariant, bounded domain in space. One of these states represents the physical experiment for the acquisition of the actual seismic data where near-offset traces are missing, and the other state represents a synthetic experiment with no missing near offset traces, computer-generated on a much simpler earth model. The reciprocity relationship used to relate these two states is iteratively inverted for the missing near-offset traces (103), preferably using only part of the synthetic data (102) so as to reduce inversion artifacts. The reference model acts as a constraint on the near-offset extrapolation.

Abstract: An exemplary method for filtering multi-component seismic data is provided. One or more characteristics of the seismic data corresponding to a relative manifestation of surface wave noise on the different components, such as polarization attributes, are identified. A time-frequency boundary in the seismic data is also identified in the time-frequency transform domain, the time-frequency boundary delineating portions of the seismic data estimated to contain surface wave noise (302). Finally, filtered seismic data are created (306) by removing portions of the seismic data that correspond to the identified characteristics of surface waves and that are within the time-frequency boundary (303).

Abstract: The invention includes a method for reducing noise in migration of seismic data, particularly advantageous for imaging by simultaneous encoded source reverse-time migration (SS-RTM). One example embodiment includes the steps of obtaining a plurality of initial subsurface images; decomposing each of the initial subsurface images into components; identifying a set of components comprising one of (i) components having at least one substantially similar characteristic across the plurality of initial subsurface images, and (ii) components having substantially dissimilar characteristics across the plurality of initial subsurface images; and generating an enhanced subsurface image using the identified set of components. For SS-RTM, each of the initial subsurface images is generated by migrating several sources simultaneously using a unique random set of encoding functions. Another embodiment of the invention uses SS-RTM for velocity model building.

Abstract: A method for estimating velocity dispersion in seismic surface waves in massive 3-D data sets (401) that improves upon auto-picking of a curve along the peak or ridge of the magnitude of the beam-formed field (402). The seismic data are transformed to the frequency-slowness domain, where nonlinear constrained optimization is performed on the transformed data. The optimization matches a nonlinear mathematical parametric model (403) of a beam-formed field to that in the transformed data, adjusting the parameters each iteration to reduce mismatch (404). Dispersion curves are determined by the center of the beam in the optimized models (405). A preferred nonlinear parametric mathematical model is a Gaussian-shaped beam or a cosine-tapered boxcar beam.

Abstract: Method and apparatus for producing a bubble curtain with a diversity of bubble diameters for purposes such as modifying the characteristics of a seismic source used in marine seismic surveys. Bubble generating elements are used that combine porous wall material with discrete holes (91) to create a curtain of diverse-sized bubbles (92).

Abstract: Method for adapting a template to a target data set. The template may be used to remove noise from, or interpret noise in, the target data set. The target data set is transformed (550) using a selected complex-valued, directional, multi-resolution transform (‘CDMT’) satisfying the Hubert transform property at least approximately. An initial template is selected, and it is transformed (551) using the same CDMT. Then the transformed template is adapted (560) to the transformed target data by adjusting the template's expansion coefficients within allowed ranges of adjustment so as to better match the expansion coefficients of the target data set. Multiple templates may be simultaneously adapted to better fit the noise or other component of the data that it may be desired to represent by template.

Abstract: Method and apparatus for producing a bubble curtain with a diversity of bubble diameters for purposes such as modifying the characteristics of a seismic source used in marine seismic surveys. Bubble generating elements are used that combine porous wall material with discrete holes (91) to create a curtain of diverse-sized bubbles (92).

Abstract: Method and apparatus for producing a bubble curtain with a diversity of bubble diameters for purposes such as suppressing surface-related multiple reflections in marine seismic surveys. Bubble generating elements are used that combine porous wall material with discrete holes.

Abstract: Method for reducing air wave and/or magnetotelluric noise in controlled source electromagnetic surveying by either shielding the source (61) from the air interface, shielding the receivers from downward traveling electromagnetic energy, or by employing a second source (62) to preferentially cancel the air wave (and MT) part of the signal, or a combination of the preceding.

Abstract: Method for adapting a template to a target data set. The template may be used to remove noise from, or interpret noise in, the target data set. The target data set is transformed (550) using a selected complex-valued, directional, multi-resolution transform (‘CDMT’) satisfying the Hubert transform property at least approximately. An initial template is selected, and it is transformed (551) using the same CDMT. Then the transformed template is adapted (560) to the transformed target data by adjusting the template's expansion coefficients within allowed ranges of adjustment so as to better match the expansion coefficients of the target data set. Multiple templates may be simultaneously adapted to better fit the noise or other component of the data that it may be desired to represent by template.

Abstract: An exemplary method for filtering multi-component seismic data is provided. The exemplary method comprises identifying a plurality of characteristics of the seismic data, the plurality of characteristics corresponding to a relative manifestation of surface wave noise on the different components and identifying a time-frequency boundary in the seismic data, the time-frequency boundary delineating portions of the seismic data estimated to contain surface wave noise (302). In addition, the exemplary method comprises creating filtered seismic data (306) by removing portions of the seismic data that correspond to the plurality of characteristics of surface waves and that are within the time-frequency boundary (303).