A hierarchical conditioning methodology for building and conditioning a geological model is disclosed. In particular, the hierarchical conditioning may include separate levels of conditioning of template instances using larger-scale data (such as conditioning using large-scale data and conditioning using medium-scale data) and using smaller-scale data (such as fine-scale data). Further, one or more templates, to be instantiated to generate the geological bodies in the model, may be selected from currently available templates and/or machine-learned templates. For example, the templates may be generated using unsupervised or supervised learning to re-parameterize the functional form parameters, or may be generated using statistical generative modeling.

A computing program product and method for interpolating wavelets coefficients and estimating spatial varying wavelets using the covariance interpolation method in the data space over a survey region having multiple well locations, are disclosed. The method and computing program product, embodied in a non-transitory computer readable device, that stores instructions for performing by a device are based on interpolating coefficient models in the data space domain using covariance analysis methods to overcome inaccuracy and instability issues commonly observed during wavelet estimation and interpolation.

A computing program product and method for interpolating wavelets coefficients and estimating spatial varying wavelets using the covariance interpolation method in the data space over a survey region having multiple well locations, are disclosed. The method and computing program product, embodied in a non-transitory computer readable device, that stores instructions for performing by a device are based on interpolating coefficient models in the data space domain using covariance analysis methods to overcome inaccuracy and instability issues commonly observed during wavelet estimation and interpolation.

Data objects of a geospatial data set are arranged in a low-discrepancy sequence spanning over a pre-defined interval, and assigned to N computing units based on in which sub-interval within the pre-defined interval the point, to which the data object belongs, falls. A subset of the data objects that have been distributed over the N computing units is subjected to processing operations by computer readable instructions loaded on each of the N computing units.

Data objects of a geospatial data set are arranged in a low-discrepancy sequence spanning over a pre-defined interval, and assigned to N computing units based on in which sub-interval within the pre-defined interval the point, to which the data object belongs, falls. A subset of the data objects that have been distributed over the N computing units is subjected to processing operations by computer readable instructions loaded on each of the N computing units.

A method and apparatus for subsurface data processing includes determining a set of clusters based at least in part on measurement vectors associated with different depths or times in subsurface data, defining clusters in a subsurface data by classes associated with a state mode, reducing a quantity of the subsurface data based at least in part on the classes, and storing the reduced quantity of the subsurface data and classes with the state model in a training database for a machine learning process.

A method and apparatus for subsurface data processing includes determining a set of clusters based at least in part on measurement vectors associated with different depths or times in subsurface data, defining clusters in a subsurface data by classes associated with a state mode, reducing a quantity of the subsurface data based at least in part on the classes, and storing the reduced quantity of the subsurface data and classes with the state model in a training database for a machine learning process.

A method is claimed that includes obtaining a measured present-day value of at least one parameter for each member of a set of unvalidated geological layers arranged in order of increasing depth and iteratively selecting a member of the set as a current layer. For each current layer in turn, the method further determines an estimated archaic value of at least one parameter of the current layer based on its measured present-day value by applying an alternating cycle of decompaction followed by geomechnical modeling to predict a present-day value of the parameter of the current layer based on its estimated archaic value. The method still further determines a validated archaic value of at least one parameter of each current layer based on a difference between the predicted and the measured present-day values. A non-transitory computer readable medium storing instructions for validating the archaic value for each layer is claimed.

A method is claimed that includes obtaining a measured present-day value of at least one parameter for each member of a set of unvalidated geological layers arranged in order of increasing depth and iteratively selecting a member of the set as a current layer. For each current layer in turn, the method further determines an estimated archaic value of at least one parameter of the current layer based on its measured present-day value by applying an alternating cycle of decompaction followed by geomechnical modeling to predict a present-day value of the parameter of the current layer based on its estimated archaic value. The method still further determines a validated archaic value of at least one parameter of each current layer based on a difference between the predicted and the measured present-day values. A non-transitory computer readable medium storing instructions for validating the archaic value for each layer is claimed.

A method for modeling a subterranean volume includes receiving seismic data comprising a signal, generating a semblance in the frequency-wavenumber domain for the seismic data, wherein the semblance represents a coherence of the signal in the frequency-wavenumber domain, extracting one or more wave energy modes in the semblance using a machine learning model trained to identify dispersion curves in the semblance based on a visible characteristic of the dispersion curves, and generating a model representing surface wave propagation based at least in part on the identified one or more wave energy modes.