Abstract: Seismic data from seismic exploration surveys are mapped into a hypercube of bins or voxels in a four-dimensional space (X, Y, Offset, and Azimuth) according to Common Mid-Point (or CMP) between source and receivers. The mapped data from individual voxels or bins is then analyzed by multimodal statistics. Robust estimates of first break picks are obtained from the analysis. The first break picks are then used to as seed inputs for autopicking iteration, which proceeds to convergence. Estimates of confidence levels in the data are provided for re-picking to reduce computer processing time in successive autopicking iterations. Analysis is provided of different seismic attributes such as azimuthal velocity variations indicative of anisotropy, positioning errors of sources/receivers, geometry errors, and three dimensional distribution of inversion residuals. Analysis is also performed of standard deviation of the travel time data useful for estimating data errors in the inversion covariance matrix.

Abstract: Seismic data from seismic exploration surveys are mapped into a hypercube of bins or voxels in a four-dimensional space (X, Y, Offset, and Azimuth) according to Common Mid-Point (or CMP) between source and receivers. The mapped data from individual voxels or bins is then analyzed by multimodal statistics. Robust estimates of first break picks are obtained from the analysis. The first break picks are then used to as seed inputs for autopicking iteration, which proceeds to convergence. Estimates of confidence levels in the data are provided for re-picking to reduce computer processing time in successive autopicking iterations. Analysis is provided of different seismic attributes such as azimuthal velocity variations indicative of anisotropy, positioning errors of sources/receivers, geometry errors, and three dimensional distribution of inversion residuals. Analysis is also performed of standard deviation of the travel time data useful for estimating data errors in the inversion covariance matrix.

Abstract: A surface-consistent refraction analysis automatically derives near surface corrections during seismic data processing. Residual time lags are evaluated in multiple CMP-offset-azimuth bins by similarity analysis with a pilot trace where a correlation window is centered at the refracted arrival. The similarity analysis may take the form of computerized cross-correlation, or other criteria such as semblance. The residuals are then used to build a system of linear equations that is simultaneously inverted for surface-consistent shot and receiver time shift corrections plus a possible subsurface residual term. The refraction analysis steps are completely automated and require a fraction of the time needed for conventional near surface analysis.

Abstract: A surface-consistent refraction analysis automatically derives near surface corrections during seismic data processing. Residual time lags are evaluated in multiple CMP-offset-azimuth bins by similarity analysis with a pilot trace where a correlation window is centered at the refracted arrival. The similarity analysis may take the form of computerized cross-correlation, or other criteria such as semblance. The residuals are then used to build a system of linear equations that is simultaneously inverted for surface-consistent shot and receiver time shift corrections plus a possible subsurface residual term. The refraction analysis steps are completely automated and require a fraction of the time needed for conventional near surface analysis.

Abstract: A surface-consistent refraction analysis automatically derives near surface corrections during seismic data processing. Residual time lags are evaluated in multiple CMP-offset-azimuth bins by similarity analysis with a pilot trace where a correlation window is centered at the refracted arrival. The similarity analysis may take the form of computerized cross-correlation, or other criteria such as semblance. The residuals are then used to build a system of linear equations that is simultaneously inverted for surface-consistent shot and receiver time shift corrections plus a possible subsurface residual term. The refraction analysis steps are completely automated and require a fraction of the time needed for conventional near surface analysis.

Abstract: A surface-consistent refraction analysis method to automatically derive near surface corrections for seismic data processing. The method uses concepts from surface-consistent analysis applied to refracted arrivals. The method includes the use of CMP-offset-azimuth binning, evaluation of mean travel time and standard deviation for each bin, rejection of anomalous first break (FB) picks, derivation of CMP-based travel time-offset functions, conversion to velocity-depth functions, evaluation of long wavelength statics and calculation of surface-consistent residual statics through waveform cross-correlation. Residual time lags are evaluated in multiple CMP-offset-azimuth bins by similarity analysis with a pilot trace for all the other traces in the gather where the correlation window is centered at the refracted arrival. The similarity analysis may take the form of computerized cross-correlation, or other criteria such as semblance.

Abstract: A surface-consistent refraction analysis method to automatically derive near surface corrections for seismic data processing. The method uses concepts from surface-consistent analysis applied to refracted arrivals. The method includes the use of CMP-offset-azimuth binning, evaluation of mean travel time and standard deviation for each bin, rejection of anomalous first break (FB) picks, derivation of CMP-based travel time-offset functions, conversion to velocity-depth functions, evaluation of long wavelength statics and calculation of surface-consistent residual statics through waveform cross-correlation. Residual time lags are evaluated in multiple CMP-offset-azimuth bins by similarity analysis with a pilot trace for all the other traces in the gather where the correlation window is centered at the refracted arrival. The similarity analysis may take the form of computerized cross-correlation, or other criteria such as semblance.

Abstract: A surface-consistent refraction analysis automatically derives near surface corrections during seismic data processing. Residual time lags are evaluated in multiple CMP-offset-azimuth bins by similarity analysis with a pilot trace where a correlation window is centered at the refracted arrival. The similarity analysis may take the form of computerized cross-correlation, or other criteria such as semblance. The residuals are then used to build a system of linear equations that is simultaneously inverted for surface-consistent shot and receiver time shift corrections plus a possible subsurface residual term. The refraction analysis steps are completely automated and require a fraction of the time needed for conventional near surface analysis.

Abstract: Seismic data from seismic exploration surveys are mapped into a hypercube of bins or voxels in a four-dimensional space (X, Y, Offset, and Azimuth) according to Common Mid-Point (or CMP) between source and receivers. The mapped data from individual voxels or bins is then analyzed by multimodal statistics. Robust estimates of first break picks are obtained from the analysis. The first break picks are then used to as seed inputs for autopicking iteration, which proceeds to convergence. Estimates of confidence levels in the data are provided for re-picking to reduce computer processing time in successive autopicking iterations. Analysis is provided of different seismic attributes such as azimuthal velocity variations indicative of anisotropy, positioning errors of sources/receivers, geometry errors, and three dimensional distribution of inversion residuals. Analysis is also performed of standard deviation of the travel time data useful for estimating data errors in the inversion covariance matrix.

Abstract: Seismic data from seismic exploration surveys are mapped into a hypercube of bins or voxels in a four-dimensional space (X, Y, Offset, and Azimuth) according to Common Mid-Point (or CMP) between source and receivers. The mapped data from individual voxels or bins is then analyzed by multimodal statistics. Robust estimates of first break picks are obtained from the analysis. The first break picks are then used to as seed inputs for autopicking iteration, which proceeds to convergence. Estimates of confidence levels in the data are provided for re-picking to reduce computer processing time in successive autopicking iterations. Analysis is provided of different seismic attributes such as azimuthal velocity variations indicative of anisotropy, positioning errors of sources/receivers, geometry errors, and three dimensional distribution of inversion residuals. Analysis is also performed of standard deviation of the travel time data useful for estimating data errors in the inversion covariance matrix.

Abstract: Seismic data from seismic exploration surveys are mapped into a hypercube of bins or voxels in a four-dimensional space (X, Y, Offset, and Azimuth) according to Common Mid-Point (or CMP) between source and receivers. The mapped data from individual voxels or bins is then analyzed by multimodal statistics. Robust estimates of first break picks are obtained from the analysis. The first break picks are then used to as seed inputs for autopicking iteration, which proceeds to convergence. Estimates of confidence levels in the data are provided for re-picking to reduce computer processing time in successive autopicking iterations. Analysis is provided of different seismic attributes such as azimuthal velocity variations indicative of anisotropy, positioning errors of sources/receivers, geometry errors, and three dimensional distribution of inversion residuals. Analysis is also performed of standard deviation of the travel time data useful for estimating data errors in the inversion covariance matrix.

Abstract: Seismic data from seismic exploration surveys are mapped into a hypercube of bins or voxels in a four-dimensional space (X, Y, Offset, and Azimuth) according to Common Mid-Point (or CMP) between source and receivers. The mapped data from individual voxels or bins is then analyzed by multimodal statistics. Robust estimates of first break picks are obtained from the analysis. The first break picks are then used to as seed inputs for autopicking iteration, which proceeds to convergence. Estimates of confidence levels in the data are provided for re-picking to reduce computer processing time in successive autopicking iterations. Analysis is provided of different seismic attributes such as azimuthal velocity variations indicative of anisotropy, positioning errors of sources/receivers, geometry errors, and three dimensional distribution of inversion residuals. Analysis is also performed of standard deviation of the travel time data useful for estimating data errors in the inversion covariance matrix.

Abstract: Methods and computing systems for multiple-domain inversion are disclosed to enhance subsurface region evaluation. In one embodiment, three or more datasets corresponding to a subterranean region are received, wherein at least one of the datasets is a magnetic dataset; and the three or more datasets are jointly inverted to generate at least a velocity model that corresponds to at least a first part of the subterranean region, and a susceptibility model that corresponds to at least the first part of the subterranean region, wherein the velocity model and the susceptibility model are correlated.