Pattern recognition in spatial data: a new method of seismic explorations for oil and gas in crystalline basement rocks

The problem of prospecting oil and gas reserves in the crystalline basement of the Earth mantle by way of a combined interpretation of seismic data registered on the daylight surface and direct information from a sparse net of exploratory wells is considered as pattern recognition problem in which the role of objects whose class membership is to be recovered is played by points of the three-dimensional underground medium. Local properties of reflected seismic signals serve as features of the membership of the respective rock mass zones in the class of collectors, i.e. spatial areas capable of accumulating fluids, whereas direct data obtained from exploratory wells serve as trainer´s information. A new spatial approach to supervised pattern recognition is proposed which makes use of the fact that objects to be recognized are arranged in an array in space. Along with the additional assumption that immediately adjacent points offer a tendency to belong to the same class, this fact allows for drawing reliable decisions from relatively unreliable features