The present disclosure describes methods and systems for estimating dispersion spectra for full waveform sonic (FWS) logging. One computer-implemented method includes receiving FWS data, performing frequency-spatial (FX) transform on the FWS data, using a nonstationary predictive error filtering (PEF) inversion on the transformed FWS data to estimate local matrix L and matrix P, calculating an inverse covariance matrix based on the estimated local matrix L and matrix P, and obtaining a nonstationary maximum likelihood method (MLM) spectra based on the inverse covariance matrix.

A multi-stage inversion method for deblending seismic data includes: a) acquiring blended seismic data from a plurality of seismic sources; b) constructing an optimization model that includes the acquired blended seismic data and unblended seismic data; c) performing sparse inversion, via a computer processor, on the optimization model; d) estimating high-amplitude coherent energy from result of the performing sparse inversion in c); e) re-blending the estimated high-amplitude coherent energy; and f) computing blended data with an attenuated direct arrival energy.

The present invention pertains to the fields of geology and geophysics, is designed for use for onshore seismic acquisition. The method involves distributing and arranging the elements used in the acquisition of two-dimensional seismic data from dynamite sources, enabling imaging quality to be improved. The use of sources of dynamite with single charges and variable weight at each shot point results in the generation of seismic waves with variable energy that provide reflections with complementary frequency and amplitudes content for use in the geophysical imaging of geological features. The stacking of this incremental content generated by charges of variable weights results in a significant improvement in the resolution of the processed seismic data on both the continuity of stratigraphic reflectors and existing geological framework.

A non-blended dataset related to a same surveyed area as a blended dataset is used to deblend the blended dataset. The non-blended dataset may be used to calculate a model dataset emulating the blended dataset, or may be transformed in a model domain and used to derive sparseness weights, model domain masking, scaling or shaping functions used to deblend the blended dataset.

A method for deblending seismic signals includes entering as input to a computer recorded signals comprising seismic energy from a plurality of actuations of one or more seismic energy sources. A model of deblended seismic data and a blending matrix are initialized. A blending matrix inversion is performed using the initialized model. The inversion includes using a scaled objective function. The inversion is constrained by a thresholding operator. The thresholding operator is arranged to recover coefficients of the model of the deblended seismic data that are substantially nonzero, against a Gaussian white noise background. The thresholded model is projected into data space. Performing the blending matrix inversion is repeated if a data residual exceeds a selected threshold and the inversion is terminated if the data residual is below the selected threshold. At least one of storing and displaying an output of the blending matrix inversion is performed when the blending matrix inversion is terminated.

Methods and apparatuses for processing seismic data acquired with multicomponent sensors build an accurate S-wave velocity model of a surveyed underground formation using a full waveform inversion (FWI) approach. PS synthetic data is generated using approximative acoustic equations in anisotropic media with a P-wave model, a current S-wave velocity model and a reflectivity model as inputs. The current S-wave velocity model is updated using FWI to minimize an amplitude-discrepancy-mitigating cost function that alleviates the amplitude mismatch between the PS observed data and the PS synthetic data due to the use of the approximative acoustic equations.

A method and system for removing intrinsic transducer noises. The method may comprise disposing a measurement assembly into a wellbore, performing a measurement operation at a depth in the wellbore with the measurement assembly to record two or more raw reflected waveforms, identifying one or more intrinsic transducer noises in the two or more raw reflected waveforms, dividing the two or more raw reflected waveforms into one or more subsections, and identifying one or more incoherent measurements in the one or more subsections. The method may further comprise deriving a noise model for each of the one or more incoherent measurements, performing an inversion for each noise model, and applying an adaptive subtraction to remove the one or more intrinsic transducer noises from the two or more raw reflected waveforms.

This specification describes workflows for, but is not limited to, performing full waveform inversion (FWI) to build high resolution velocity models to improve the accuracy of seismic imaging of a subterranean formation. This specification describes processes to automatically edit and enhance S/N quality of seismic data (such as land seismic data) to prepare the datasets for FWI. The methods for automatic corrections and pre-processing include: automatic iterative surface-consistent residual statics calculation, automatic rejection of anomalous traces (such as dead traces), and the automatic correction of surface-consistent amplitude anomalies (such as by scalar or deconvolution approaches). The operations include automatic “muting” of noise before first arrivals.

Method for acquiring seismic data is described. The method includes determining a non-uniform optimal sampling design that includes a compressive sensing sampling grid. Placing a plurality of source lines or receiver lines at a non-uniform optimal line interval. Placing a plurality of receivers or nodes at a non-uniform optimal receiver interval. Towing a plurality of streamers attached to a vessel, wherein the plurality of streamers is spaced apart at non-uniform optimal intervals based on the compressive sensing sampling grid. Firing a plurality of shots from one or more seismic sources at non-uniform optimal shot intervals. Acquiring seismic data via the plurality of receivers or nodes.

A multi-stage inversion method for deblending seismic data includes: a) acquiring blended seismic data from a plurality of seismic sources; b) constructing an optimization model that includes the acquired blended seismic data and unblended seismic data; c) performing sparse inversion, via a computer processor, on the optimization model; d) estimating high-amplitude coherent energy from result of the performing sparse inversion in c); e) re-blending the estimated high-amplitude coherent energy; and f) computing blended data with an attenuated direct arrival energy.