Abstract: Some implementations may include a method for detecting, by a learning machine, a geobody in a seismic volume. The method may include receiving a first seismic input tile representing first seismic data from the seismic volume; receiving a first guide input tile including first labels that indicate presence of the geobody in a respective region in the seismic volume or absence of the geobody in the respective region, and one or more unlabeled regions that make no indication about presence or absence of the geobody; and determining, based on the first seismic input tile and the first guide input tile, a first prediction about geobody presence or absence in the seismic volume.

Abstract: A method for correlating data comprises acquiring a first sequence signal and a second sequence signal, wherein the first sequence signal comprises at least a first data point including a first set of components and the second sequence signal comprises at least a second data point including a second set of components; acquiring a first set of user picks and a second set of user picks, wherein the first and the second sets of user picks each contain a respective first and second correspondence between a component in the first set of components and a component in the second set of components; combining the first and second sets of user picks with the first and second sequence signals to create a first hyper-complex signal and a second hyper-complex signal; and performing signal alignment on the first and second hyper-complex signals.

Abstract: A method for performing wellbore correlation across multiple wellbores includes predicting a depth alignment across the wellbores based on a geological feature of the wellbores. Predicting a depth alignment includes selecting a reference wellbore, defining a control point in a reference signal of a reference well log for the reference wellbore, and generating an input tile from the reference signal, the control points, and a number of non-reference well logs corresponding to non-reference wellbores. The well logs include changes in a geological feature over a depth of a wellbore. The input tile is input into a machine-learning model to output a corresponding control point for each non-reference well log. The corresponding control point corresponds to the control point of the reference log. Based on the corresponding control points output from the machine-learning model, the non-reference well logs are aligned with the reference well log to correlate the multiple wellbores.

Abstract: An apparatus for processing seismic data variables comprising a tracking module and an interpretation module. The tracking module selects groupings of subsurface data variables from the seismic data variables, selects a subsurface data variable for each grouping, and determines an isochron variable for each subsurface data variable for each grouping. Each grouping of subsurface data variables has spatial coordinates values. The interpretation module predicts a horizon variable for each grouping using the isochron variable and an algorithmic model or trained algorithmic. The interpretation module predicts a horizon variable using the isochron variable for each grouping and a trained algorithmic model. The tracking module selects the subsurface data variable for each grouping based on a peak, trough or zero-crossing identified in the grouping.

Abstract: An apparatus for processing seismic data variables comprising a tracking module and an interpretation module. The tracking module selects groupings of subsurface data variables from the seismic data variables, selects a subsurface data variable for each grouping, and determines an isochron variable for each subsurface data variable for each grouping. Each grouping of subsurface data variables has spatial coordinates values. The interpretation module predicts a horizon variable for each grouping using the isochron variable and an algorithmic model or trained algorithmic. The interpretation module predicts a horizon variable using the isochron variable for each grouping and a trained algorithmic model. The tracking module selects the subsurface data variable for each grouping based on a peak, trough or zero-crossing identified in the grouping.

Abstract: A heat flow modeler preprocesses geological and heat flow data for an earth formation for inputting into a plurality of supervised learning models. The heat flow modeler trains the plurality of supervised learning models on the preprocessed geological data to estimate heat flow throughout the earth formation. The heat flow modeler interpolates the estimated heat flow values to a set of desired locations in the earth formation and cosimulates the preprocessed heat flow values with the interpolated heat flow values as auxiliary variables to generate a cosimulated heat flow map. A final heat flow map is generated by rasterizing the cosimulated heat flow map.

Abstract: A method for correlating data includes acquiring a first sequence of data and a second sequence of data, wherein the first sequence of data comprises at least a first data point including a first set of signal components and the second sequence of data comprises at least a second data point including a second set of signal components, wherein the signal components in the first and second sets of signal components contain valid data, nulls, or a combination thereof; identifying one or more nulls in at least one of the first set of signal components or the second set of signal components; defining a difference between the one or more nulls and a component value as a real value; and calculating a distance between the first data point and the second data point based on the difference between the one or more nulls and the component value.

Abstract: A method for correlating data comprises acquiring a first sequence signal and a second sequence signal, wherein the first sequence signal comprises at least a first data point including a first set of components and the second sequence signal comprises at least a second data point including a second set of components; acquiring a first set of user picks and a second set of user picks, wherein the first and the second sets of user picks each contain a respective first and second correspondence between a component in the first set of components and a component in the second set of components; combining the first and second sets of user picks with the first and second sequence signals to create a first hyper-complex signal and a second hyper-complex signal; and performing signal alignment on the first and second hyper-complex signals.

Abstract: A method for correlating data includes acquiring a first sequence of data and a second sequence of data, wherein the first sequence of data comprises at least a first data point including a first set of signal components and the second sequence of data comprises at least a second data point including a second set of signal components, wherein the signal components in the first and second sets of signal components contain valid data, nulls, or a combination thereof; identifying one or more nulls in at least one of the first set of signal components or the second set of signal components; defining a difference between the one or more nulls and a component value as a real value; and calculating a distance between the first data point and the second data point based on the difference between the one or more nulls and the component value.

Abstract: A method of processing image data for display by a display panel of a display device. The method comprises receiving main image pixel data representing a main image and side image pixel data representing a side image, and performing a mapping of the pixel data to signals used to drive the display panel. The mapping is arranged to produce an average on-axis luminance which is dependent mainly on the main image pixel data and an average off-axis luminance which is dependent at least to some extent on the side image pixel data. A compression of the main image pixel data is performed in advance of or at least partly incorporated into the mapping, the compression being performed at least partly in dependence upon the main image pixel data and at least partly in dependence upon how the off-axis luminance varies with pixel data input to the mapping.

Abstract: A method is provided of processing image data for display by a display panel of a display device. The method comprises receiving main image pixel data representing a main image and side image pixel data representing a side image, and performing a mapping of the pixel data to signals used to drive the display panel. The mapping is arranged to produce an average on-axis luminance which is dependent mainly on the main image pixel data and an average off-axis luminance which is dependent at least to some extent on the side image pixel data. A compression of the main image pixel data is performed in advance of or at least partly incorporated into the mapping, the compression being performed at least partly in dependence upon the main image pixel data and at least partly in dependence upon how the off-axis luminance varies with pixel data input to the mapping.

Abstract: A method is provided for performing robust frame rate conversion of video data to a higher frame rate. A metric is formed (16) as a function of motion compensation error normalised by a measure of image content, such as image texture (10, 11). The metric is then compared with thresholds (17, 18) to determine whether conversion will be based on motion compensated interpolation or frame repetition. If the metric falls between thresholds, the previously selected mode may be repeated.

Abstract: A method of performing spatio-temporal up-scaling includes receiving an input video having a sequence of input frames, analyzing the input video to estimate motion vectors associated with the sequence of input frames, and determining corresponding motion compensation errors associated with the motion vectors. The method further includes determining an extent to which computational resources are to be respectively allocated to spatially up-scaling the sequence of input frames and temporally up-scaling the sequence of input frames, based on the estimated motion vectors and corresponding motion compensation errors. In addition, the method includes spatio-temporally up-scaling the sequence of input frames based on the determined extent.