GPRAlpha
GPRAlpha is a hypothetical signal processing algorithm designed for enhanced detection and characterization of underground structures using Ground Penetrating Radar (GPR) data. Its primary objective is to improve the signal-to-noise ratio and to automatically identify and classify anomalies that may represent buried objects, geological layers, or other subsurface features. The algorithm employs a multi-stage approach, often involving advanced filtering techniques and machine learning components.
The initial stage of GPRAlpha typically focuses on noise reduction and artifact suppression. This can involve
The subsequent stage often involves classification or interpretation. Here, machine learning models, such as support vector