Geoenvironmental zoning evaluation and optimization

The problem of fast pattern classification by automatic analytical and sorting techniques is relevant across a wide range of scientific and technical disciplines. Since the availability of mass transactional and experimental data sets to address the challenges faced by the Earth and environmental scientists, validation of zoning has become an important topic. In this paper, we propose a new approach for producing Automatic Integrated Self-Organized Optimum Zoning (AISOOZ) maps using comprehensive (multivariate) geological and geophysical data. Unlike conventional zoning, the new approach includes techniques for finding the optimal structure that best fits natural pattern of a given area without the benefit of any a priori class information. While there are obvious similarities between the conventional and new optimal zoning maps, the automatic optimal approach reveals new insights into the geological evolution of the study area that could not be observed on the conventional maps. The success of the AISOOZ case study encourages the enlargement of its scope and application for rapid online as well as offline interactive multivariate pattern discovery in the Earth and environmental sciences studies. Finally, a comparative study between two widely used stopping criteria for optimal zoning and pattern recognition has been performed.