Multiresolution analysis and learning for computational seismic interpretation

A Multiresolution Time-frequency Analysis and Interpretation of Musical Rhythm

This thesis describes an approach to representing musical rhythm in computational terms. The purpose of such an approach is to provide better models of musical time for machine accompaniment of human musicians and in that attempt, to better understand the processes behind human perception and performance. The intersections between musicology and artificial intelligence (AI) are reviewed, descri...

Computational Geodynamics for Seismic Hazard Analysis and Earthquake Prediction

SeisVision Seismic Interpretation Software Powerful 2D and 3D seismic interpretation system for rapid prospect generation

Data loading and management 2D or 3D data in standard SEG-Y format is easily loaded with the wizard-based data loading tools Batch loading of 2D data is supported, and the EBCDIC character header is stored as the data is loaded, so this important piece of information is not lost Project can be 2D, 3D or a combination 2D seismic surveys are easily managed with program groups and multiple version...

3 D seismic image processing for interpretation

Extracting fault, unconformity, and horizon surfaces from a seismic image is useful for interpretation of geologic structures and stratigraphic features. Although interpretation of these surfaces has been automated to some extent by others, significant manual effort is still required for extracting each type of these geologic surfaces. I propose methods to automatically extract all the fault, u...

A Directional Coherence Attribute for Seismic Interpretation

The coherence attribute is one of the most commonly used attributes in seismic interpretation. In this paper, we propose building on the recently introduced Generalized Tensor-based Coherence (GTC) attribute to make it directionally selective. This directional selectivity is achieved by selecting a directional Gaussian preprocessing kernel and applying a 3D rotational matrix to its covariance m...