Abstract: A method of analyzing dip in a seismic data volume in which a horizontal gradient is calculated in a first direction in the seismic data volume. A vertical gradient is calculated at data locations in the seismic data volume corresponding to the locations at which the horizontal gradient was calculated. Dip is calculated in the first direction from the horizontal gradient in the first direction and the vertical gradient. Repetition of the process for the entire seismic data volume results in a dip volume.

Abstract: A method of calculating a throw volume corresponding to a seismic data volume. A range of time shifts and a search direction for the seismic data volume are selected. A data location separation and a vertical time window are also selected. A cross-correlation is calculated between data values corresponding to first and second data locations separated by the data location separation and symmetrically located in the search direction on each side of a target data location. The cross-correlation is calculated throughout the vertical time window for each time shift in the range of time shifts. The time shift corresponding to the maximum calculated cross-correlation is stored in the throw volume.

Abstract: Near-offset and far-offset seismic data volumes are time-aligned by first selecting a plurality of time shifts. The near-offset and far-offset seismic data volumes are cross-correlated at the plurality of time shifts. An initial time-shift volume and a maximum correlation volume are created from the maximal cross-correlations at the plurality of time shifts. Areas of high time shift from the initial time-shift volume and areas of low cross-correlation from the maximum correlation volume are determined. The determined areas of high time shift and low cross-correlation are filtered from the initial time-shift volume, generating a filtered time-shift volume. The filtered time-shift volume is applied to the far-offset seismic volume to generate a time-aligned far-offset volume.

Abstract: A method for assessing the suitability of seismic data for quantitative amplitude analysis, where the concern is excessive residual normal moveout (RNMO). The invention uses a near offset stack and a far offset stack, the time difference between the two, a mute pattern, a reflection shape assumption for the RNMO, and a waveform and frequency for the far stack traces to generate a formula that estimates far stack amplitude error caused by RNMO. The formula can be used to compensate the far stack amplitude where the error is not so great as to require reprocessing of the data. The method can also be applied to interpreted amplitude maps.

Abstract: Seismic horizons are transferred between two three-dimensional seismic data volumes which cover the same area. The seismic data volumes are time-aligned to generate a time-shift volume. A seismic horizon is selected and the time-shift volume is applied to the seismic horizon, generating a time-shifted seismic horizon.

Abstract: A method of calculating a throw volume corresponding to a seismic data volume. A range of time shifts and a search direction for the seismic data volume are selected. A data location separation and a vertical time window are also selected. A cross-correlation is calculated between data values corresponding to first and second data locations separated by the data location separation and symmetrically located in the search direction on each side of a target data location. The cross-correlation is calculated throughout the vertical time window for each time shift in the range of time shifts. The time shift corresponding to the maximum calculated cross-correlation is stored in the throw volume.

Abstract: A method of calculating reflection curvature in a seismic data volume wherein an apparent dip value is calculated in a first direction to generate a first apparent dip volume. A horizontal gradient is calculated in the first direction in the first apparent dip volume using a specified length scale to generate a first curvature volume. The process may be repeated one or more times, and the individual curvature volumes combined to generate a combined curvature volume for the seismic data volume.

Abstract: AVO anomalies are classified in near-offset and far-offset seismic data volumes, by first calculating a plurality of initial AVO seismic attributes representative of the offset seismic data volumes. A probabilistic neural network is constructed from the calculated initial AVO seismic attributes. AVO anomaly classifications are calculated in a portion of the offset seismic data volumes. The preceding steps are repeated until the calculated AVO anomaly classifications in the portion of the offset seismic data volumes are satisfactory. AVO anomaly classifications are calculated throughout the offset seismic data volumes using the constructed probabilistic neural network.

Abstract: A method of analyzing dip in a seismic data volume in which a horizontal gradient is calculated in a first direction in the seismic data volume. A vertical gradient is calculated at data locations in the seismic data volume corresponding to the locations at which the horizontal gradient was calculated. Dip is calculated in the first direction from the horizontal gradient in the first direction and the vertical gradient. Repetition of the process for the entire seismic data volume results in a dip volume.

Abstract: Seismic horizons are transferred between two three-dimensional seismic data volumes which cover the same area. The seismic data volumes are time-aligned to generate a time-shift volume. A seismic horizon is selected and the time-shift volume is applied to the seismic horizon, generating a time-shifted seismic horizon.

Abstract: AVO anomalies are classified in near-offset and far-offset seismic data volumes, by first calculating a plurality of initial AVO seismic attributes representative of the offset seismic data volumes. A probabilistic neural network is constructed from the calculated initial AVO seismic attributes. AVO anomaly classifications are calculated in a portion of the offset seismic data volumes. The preceding steps are repeated until the calculated AVO anomaly classifications in the portion of the offset seismic data volumes are satisfactory. AVO anomaly classifications are calculated throughout the offset seismic data volumes using the constructed probabilistic neural network.

Abstract: Near-offset and far-offset seismic data volumes are time-aligned by first selecting a plurality of time shifts. The near-offset and far-offset seismic data volumes are cross-correlated at the plurality of time shifts. An initial time-shift volume and a maximum correlation volume are created from the maximal cross-correlations at the plurality of time shifts. Areas of high time shift from the initial time-shift volume and areas of low cross-correlation from the maximum correlation volume are determined. The determined areas of high time shift and low cross-correlation are filtered from the initial time-shift volume, generating a filtered time-shift volume. The filtered time-shift volume is applied to the far-offset seismic volume to generate a time-aligned far-offset volume.

Abstract: A method of calculating reflection curvature in a seismic data volume wherein an apparent dip value is calculated in a first direction to generate a first apparent dip volume. A horizontal gradient is calculated in the first direction in the first apparent dip volume using a specified length scale to generate a first curvature volume. The process may be repeated one or more times, and the individual curvature volumes combined to generate a combined curvature volume for the seismic data volume.