Design Methodology for Construction of Mapping Applications

Abstract

Geoscientists and engineers use anomalies which are parts of a profile that is above or below the surrounding average to infer subsurface targets (groundwater, ore and petroleum). Customarily, they are detected by processing field measurements including geological and geophysical data using methods such as stacking (averaging), Fourier analysis and filtering. The issue is these methods often result in partial detection because they perform partial separation of wanted from unwanted anomalies and the error in separation gets propagated into data layers and subsequent analyses, thereby resulting in less accurate spatial predictions. In order to understand and address this issue, we investigate whether the design methodology for construction of mapping applications for characterizing geospatial variables achieves logical consistency of data layers and improve mapping accuracy of groundwater flow. We present a design methodology as an artifact and evaluated it by applying it to hydrogeological and geodetic data acquired from the Santa Clara Valley, CA, USA. The result shows that data parts offer distinctive patterns of geometric features that are signatures of groundwater flow for sustainable groundwater management. The practical implications of the result can be applied by software developers and data modelers, information systems and operations managers to construct logically consistent and well-composed environmental information systems.