Abstract: A process for constructing a three-dimensional geologic model of a subsurface earth volume wherein resolution scales of multiple diverse data types, including seismic data, are accounted for by generating multiple frequency passband models and combining them together to form the complete geologic model. Preferably, a model is generated for each of a low-frequency passband, a mid-frequency passband, and a high-frequency passband. When integrating seismic data into the modeling process, the seismic-frequency passband constitutes the mid-frequency passband model. The process further contemplates updating tentative frequency-passband models through optimization of assigned rock property values in each tentative model according to specified geological criteria. Such optimization is carried out by perturbation of the rock property values in a manner wherein the frequency content of each model is maintained.

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: 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: 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 process for constructing a three-dimensional geologic model of a subsurface earth volume wherein resolution scales of multiple diverse data types, including seismic data, are accounted for by generating multiple frequency passband models and combining them together to form the complete geologic model. Preferably, a model is generated for each of a low-frequency passband, a mid-frequency passband, and a high-frequency passband. When integrating seismic data into the modeling process, the seismic-frequency passband constitutes the mid-frequency passband model. The process further contemplates updating tentative frequency-passband models through optimization of assigned rock property values in each tentative model according to specified geological criteria. Such optimization is carried out by perturbation of the rock property values in a manner wherein the frequency content of each model is maintained.