A method for seismic imaging of a subsurface using a full waveform inversion, FWI, includes the steps of obtaining an initial P-velocity model that describes how a seismic wave propagates through the subsurface, generating an S-velocity model based on the initial P-velocity model, generating an elastic synthetic dataset p based on the initial P-velocity model and the S-velocity model, receiving a recorded seismic dataset d of the subsurface, updating the P-velocity model based on a comparison between the synthetic dataset p and the recorded seismic dataset d, updating a reflectivity at various locations in the subsurface based on the updated P-velocity model, and generating an image of the subsurface based on the reflectivity.

A permutation that optimizes correspondence between the seismic data and the simulated data is computed using a graph space optimal transport formulation-based misfit. The seismic data or simulated data are transformed into auxiliary data by applying a portion of time shifts computed from the optimal permutation before updating the structural model of the explored underground formation. The full-waveform inversion minimization of the distance between auxiliary data and the seismic data or simulated data to which partial time shifts have not been applied, may be embedded in a Kantorovich-Rubinstein norm.

A method is described for seismic data processing including receiving a seismic dataset D(s, r; t) representative of a subsurface volume of interest; calculating a pre-migration attenuated travel time t*(s, r; t); performing a first migration on D(s, r; t) to generate common image point (CIP) gathers G(x, h); performing a second migration on D(s, r; t)*t*(s, r; t) to generate weighted common image point (CIP) gathers G.sub.t*(x, h); and calculating a conditioned ratio of the weighted CIP gathers G.sub.t*(x, h) over the CIP gathers G(x, h) to get CIP gathers of attenuated traveltime t*(x, h). The CIP gathers of attenuated traveltime t*(x, h) may be used to perform seismic tomography to generate an attenuation (Q) model.

A method for correcting physical positions of seismic sensors and/or seismic sources for a seismic data acquisition system. The method includes estimating a respective energy generated by each source element, which belongs to a source array; calculating a respective energy recorded by each individual seismic sensor, which belongs to a composite receiver; summing, for each individual seismic sensor, all the generated energies from the all the source elements; estimating a model of direct arrival waves that propagate from the source elements to the individual seismic sensors; calculating positions of the individual seismic sensors based on the model of direct arrival waves; comparing calculated positions of the individual seismic sensors with observed positions of the individual seismic sensors; selecting a best calculated position for each of the individual seismic sensors based on an objective function; and correcting the observed positions of the individual seismic sensors with corresponding best calculated positions.

Systems and methods of detecting marine seismic survey parameters are provided. A data processing system can obtain seismic data from seismic data acquisition units disposed on a seabed responsive to an acoustic signal propagated from an acoustic source through a water column. The data processing system can determine from the seismic data, a direct arrival time for the acoustic signal at each of the plurality of seismic data acquisition units, and can obtain an estimated depth value of each of the plurality of seismic data acquisition units and an estimated water column transit velocity of the acoustic signal. The data processing system can apply a depth model and a water column transit velocity model to the estimated depth value and to the estimated water column transit velocity determine an updated depth value and an updated water column transit velocity for each of the plurality of seismic data acquisition units.

A permutation that optimizes correspondence between the seismic data and the simulated data is computed using a graph space optimal transport formulation-based misfit. The seismic data or simulated data are transformed into auxiliary data by applying a portion of time shifts computed from the optimal permutation before updating the structural model of the explored underground formation. The full-waveform inversion minimization of the distance between auxiliary data and the seismic data or simulated data to which partial time shifts have not been applied, may be embedded in a Kantorovich-Rubinstein norm.

A method and apparatus for generating attenuation-compensated images of subsurface region, including: computing an image of the region utilizing elastic wave propagation, based on field data and subsurface model; generating forward-modeled data utilizing forward viscoelastic wave propagation, based on the image; computing secondary image by migration; computing NMF based on the images; and applying the NMF to the image to generate the attenuation-compensated image. A method and apparatus includes: iteratively computing attenuation-compensated gradient of the region utilizing an elastic wave propagation operator in the back-propagation and a viscoelastic wave propagation operator in the forward modelling, based on field data and subsurface model; computing search direction based on the attenuation-compensated gradient, searching for an improved model, and checking the improved model for convergence.

Methods for updating a physical properties model of a subsurface region that combine advantages of FWI and tomography into a joint inversion scheme are provided. One method comprises obtaining measured seismic data; generating simulated seismic data using an initial model; computing at least one of an FWI gradient and a tomography gradient; minimizing a joint objective function E, wherein the objective function E is based on a combination of the FWI gradient and the tomography gradient or preconditioning of the FWI gradient or the tomography gradient; generating a final model based on the minimized joint objective function E; and using the final model to generate a subsurface image. The joint objective function E may be a function of one or both a FWI objective function C.sub.FWI and a tomography objective function C.sub.Tomo. The joint objective function E may be defined as a weighted sum of C.sub.FWI and C.sub.Tomo.

A method is described for seismic inversion including receiving a processed seismic image and an enhanced seismic image representative of a subsurface volume of interest; forward modeling the processed seismic image and the enhanced seismic image to generate a first modeled dataset and a second modeled dataset; differencing the first modeled dataset and the second modeled dataset to create a residual dataset; filtering the first modeled dataset to generate an approximation of illumination; preconditioning the residual dataset with the approximation of illumination to generate an adjoint source; back projecting the adjoint source to determine a model update; and applying the model update to an earth model of the subsurface volume of interest. The method may be executed by a computer system.

Methods and systems for identifying near-surface heterogeneities in a subterranean formation using surface seismic arrays can include: recording raw seismic data using sensors at ground surface; applying a band bass filter to the raw seismic data using a central frequency; picking a phase arrival time for the filtered data; generating an initial starting phase velocity model for tomographic inversion from the raw seismic data; applying tomographic inversion to the filtered data to generate a dispersion map associated at the central frequency; repeating the applying a band bass filter, picking a phase arrival time, generating an initial starting velocity model, and applying tomographic inversion steps for each of a set of central frequencies; and generating a three-dimensional dispersion volume representing near-surface conditions in the subterranean formation by combining the dispersion maps.