Abstract: Method for reducing artifacts in a subsurface physical properties model (120) inferred by iterative inversion (140) of geophysical data (130), wherein the artifacts are associated with some approximation (110) made during the iterative inversion. In the method, some aspect of the approximation is changed (160) as the inversion is iterated such that the artifacts do not increase by coherent addition.

Abstract: Method for improving convergence in gradient-based iterative inversion of seismic data (101), especially advantageous for full wavefield inversion. The method comprises decomposing the gradient into two (or more) components (103), typically the migration component and the tomographic component, then weighting the components to compensate for unequal frequency content in the data (104), then recombining the weighted components (105), and using the recombined gradient to update (106) the physical properties model (102).

Abstract: Method for converting seismic data to obtain a subsurface model of, for example, bulk modulus or density. The gradient of an objective function is computed (103) using the seismic data (101) and a background subsurface medium model (102). The source and receiver illuminations are computed in the background model (104). The seismic resolution volume is computed using the velocities of the background model (105). The gradient is converted into the difference subsurface model parameters (106) using the source and receiver illumination, seismic resolution volume, and the background subsurface model. These same factors may be used to compensate seismic data migrated by reverse time migration, which can then be related to a subsurface bulk modulus model. For iterative inversion, the difference subsurface model parameters (106) are used as preconditioned gradients (107).

Abstract: Method for performing simultaneous encoded-source inversion of geophysical data to estimate parameters of a physical property model (41), especially adapted for surveys without fixed-receiver acquisition geometry, such as marine seismic surveys with moving source and receivers. The encoding functions (32) used on the sources to generate one or more simultaneous encoded-source gathers of data (35), as well as to simulate the same (34), are orthogonal or pseudo-orthogonal with respect to cross-correlation. In addition, receivers are also encoded, with the receiver encoding being designed to make a given receiver less sensitive to sources to which it was not listening during the survey (38). The encoding functions may be temporal bandpass filters differing one from another by central frequency, phase, or both. Efficiency of the method may be further improved by grouping several sources into a super-source, grouping the corresponding gathers into a super-gather, and then applying the above encoding strategy.

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: Method for estimating the Hessian of the objective function, times a vector, in order to compute an update in an iterative optimization solution to a partial differential equation such as the wave equation, used for example in full wave field inversion of seismic data. The Hessian times vector operation is approximated as one forward wave propagation (24) and one gradient computation (25) in a modified subsurface model (23). The modified subsurface model may be a linear combination of the current subsurface model (20) and the vector (21) to be multiplied by the Hessian matrix. The forward-modeled data from the modified model are treated as a field measurement in the data residual of the objective function for the gradient computation in the modified model. In model parameter estimation by iterative inversion of geophysical data, the vector in the first iteration may be the gradient of the objective function.

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: Method for simultaneous full-wavefield inversion of gathers of source (or receiver) encoded geophysical data to determine a physical properties model for a subsurface region, especially suitable for surveys where fixed receiver geometry conditions were not satisfied in the data acquisition. First, a shallow time window of the data (202) where the fixed receiver condition is satisfied is inverted by simultaneous encoded (203) source inversion (205). Then, the deeper time window of the data (208) is inverted by sparse sequential source inversion (209), using the physical properties model from the shallow time window (206) as a starting model (207). Alternatively, the shallow time window model is used to simulate missing far offset data (211) producing a data set satisfying the stationary receiver assumption, after which this data set is source encoded (212) and inverted by simultaneous source inversion (214).

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 for simultaneous full-wavefield inversion of gathers of source (or receiver) encoded geophysical data to determine a physical properties model (118) for a subsurface region, especially suitable for surveys where fixed receiver geometry conditions were not satisfied in the data acquisition. Simultaneous source separation (104) is performed to lessen any effect of the measured geophysical data's not satisfying the fixed-receiver assumption. A data processing step (106) coming after the simultaneous source separation acts to conform model-simulated data (105) to the measured geophysical data (108) for source and receiver combinations that are missing in the measured geophysical data.

Abstract: Method for simultaneous full-wavefield inversion of gathers of source (or receiver) encoded geophysical data to determine a physical properties model for a subsurface region, especially suitable for surveys where fixed receiver geometry conditions were not satisfied in the data acquisition. First, a shallow time window of the data (202) where the fixed receiver condition is satisfied is inverted by simultaneous encoded (203) source inversion (205). Then, the deeper time window of the data (208) is inverted by sparse sequential source inversion (209), using the physical properties model from the shallow time window (206) as a starting model (207). Alternatively, the shallow time window model is used to simulate missing far offset data (211) producing a data set satisfying the stationary receiver assumption, after which this data set is source encoded (212) and inverted by simultaneous source inversion (214).

Abstract: Method for reducing artifacts in a subsurface physical properties model (120) inferred by iterative inversion (140) of geophysical data (130), wherein the artifacts are associated with some approximation (110) made during the iterative inversion. In the method, some aspect of the approximation is changed (160) as the inversion is iterated such that the artifacts do not increase by coherent addition.

Abstract: Method for converting seismic data to obtain a subsurface model of, for example, bulk modulus or density. The gradient of an objective function is computed (103) using the seismic data (101) and a background subsurface medium model (102). The source and receiver illuminations are computed in the background model (104). The seismic resolution volume is computed using the velocities of the background model (105). The gradient is converted into the difference subsurface model parameters (106) using the source and receiver illumination, seismic resolution volume, and the background subsurface model. These same factors may be used to compensate seismic data migrated by reverse time migration, which can then be related to a subsurface bulk modulus model. For iterative inversion, the difference subsurface model parameters (106) are used as preconditioned gradients (107).

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: 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).