Abstract: A method for generating a geophysical image of a subsurface region includes defining a computational sub-volume for the geophysical image including a predetermined number of seismic traces of a plurality of seismic traces and a predetermined number of samples per each one of the plurality of seismic traces, generating a data matrix corresponding to a first sub-volume of the subsurface region based on the defined computational sub-volume, the data matrix comprising the predetermined number of samples for the predetermined number of traces of a portion of a seismic dataset corresponding to the first sub-volume. The method also includes estimating a coherence between the predetermined number of traces of the data matrix by performing a sum of a variance of the predetermined number of samples of the data matrix, and assigning the estimated coherence to a location in the geophysical image.

Abstract: Systems and methods are disclosed for hydrocarbon exploration using seismic imaging and, more specifically, measuring randomness in seismic data utilizing a vectorized disorder algorithm. The vectorized disorder algorithm is configured to measure the randomness level (e.g., noise) in seismic data to improve seismic data processing/imaging and the ability to expose subsurface geology. The vectorized disorder algorithm includes performing convolution of seismic data with a vectorized disorder operator having an extra dimension than the seismic data. A nonlinear reduction operation is performed on the vectorized output to generate a randomness distribution dataset having the same dimension as the input data. The randomness distribution dataset comprises data points representing the level of randomness for respective seismic data points. A more accurate seismic image is generated from the seismic data as a function of the measured randomness distribution.

Abstract: Seismic image processing including filtering a three-dimensional (3D) seismic image for random noise attenuation via multiple processors. The filtering includes receiving a 3D image cube of seismic image data, decomposing the 3D image cube into 3D sub-cubes for parallel computation on the multiple processors, designing and applying a two-dimensional (2D) adaptive filter for image points on 2D image slices of the 3D sub-cubes via the multiple processors to give filtered 3D sub-cubes, and summing the filtered 3D sub-cubes to give a filtered 3D image cube.

Abstract: A method for generating a geophysical image of a subsurface region includes defining a computational sub-volume for the geophysical image including a predetermined number of seismic traces of a plurality of seismic traces and a predetermined number of samples per each one of the plurality of seismic traces, generating a data matrix corresponding to a first sub-volume of the subsurface region based on the defined computational sub-volume, the data matrix comprising the predetermined number of samples for the predetermined number of traces of a portion of a seismic dataset corresponding to the first sub-volume. The method also includes estimating a coherence between the predetermined number of traces of the data matrix by performing a sum of a variance of the predetermined number of samples of the data matrix, and assigning the estimated coherence to a location in the geophysical image.

Abstract: Systems and methods are disclosed for hydrocarbon exploration using seismic imaging and, more specifically, measuring randomness in seismic data utilizing a vectorized disorder algorithm. The vectorized disorder algorithm is configured to measure the randomness level (e.g., noise) in seismic data to improve seismic data processing/imaging and the ability to expose subsurface geology. The vectorized disorder algorithm includes performing convolution of seismic data with a vectorized disorder operator having an extra dimension than the seismic data. A nonlinear reduction operation is performed on the vectorized output to generate a randomness distribution dataset having the same dimension as the input data. The randomness distribution dataset comprises data points representing the level of randomness for respective seismic data points. A more accurate seismic image is generated from the seismic data as a function of the measured randomness distribution.

Abstract: The present disclosure describes methods and systems, including computer-implemented methods, computer program products, and computer systems, for smoothing seismic data. One computer-implemented method includes obtaining, by a hardware data processing apparatus, a plurality of seismic data samples; forming, by the hardware data processing apparatus, guiding vectors using the plurality of seismic data samples and a plurality of guiding structure attributes; generating, by the hardware data processing apparatus, a structure guided directional weighted vector filter using the guiding vectors and a plurality of weighting factors; filtering, by the hardware data processing apparatus, the seismic data samples using the structure guided directional weighted vector filter to generate smoothed seismic data; and initiating output of the smoothed seismic data.

Abstract: Seismic image processing including filtering a three-dimensional (3D) seismic image for random noise attenuation via multiple processors. The filtering includes receiving a 3D image cube of seismic image data, decomposing the 3D image cube into 3D sub-cubes for parallel computation on the multiple processors, designing and applying a two-dimensional (2D) adaptive filter for image points on 2D image slices of the 3D sub-cubes via the multiple processors to give filtered 3D sub-cubes, and summing the filtered 3D sub-cubes to give a filtered 3D image cube.

Abstract: The present disclosure describes methods and systems, including computer-implemented methods, computer program products, and computer systems, for smoothing seismic data. One computer-implemented method includes obtaining, by a hardware data processing apparatus, a plurality of seismic data samples; forming, by the hardware data processing apparatus, guiding vectors using the plurality of seismic data samples and a plurality of guiding structure attributes; generating, by the hardware data processing apparatus, a structure guided directional weighted vector filter using the guiding vectors and a plurality of weighting factors; filtering, by the hardware data processing apparatus, the seismic data samples using the structure guided directional weighted vector filter to generate smoothed seismic data; and initiating output of the smoothed seismic data.