Algorithm for suppression of regular interference in ground-pressure radar

The processing and interpretation of ground penetrating radar (GPR) data faces substantial technical challenges primarily stemming from the presence of correlated noise components that significantly degrade data quality and obscure meaningful subsurface information. Among the most problematic forms of interference are persistent horizontal phase-coherent noise patterns that dominate the wavefield and effectively mask genuine reflections from target structures and geological interfaces. Conventional signal processing approaches, including widely-used techniques like mean value subtraction and median filtering operations, frequently prove inadequate for addressing these challenges due to several fundamental limitations inherent in GPR signal characteristics. The spectral properties and correlation parameters of both the desired signals and noise components exhibit pronounced non-stationary behavior across both temporal and spatial domains, while simultaneously demonstrating substantial spectral overlap across critical frequency bands essential for proper resolution of subsurface features. This complex interplay of signal and noise characteristics results in unavoidable information loss during standard processing workflows and consequently reduces the reliability and precision of geological interpretation and target identification. To address these critical limitations, this research focuses on developing and rigorously evaluating an advanced adaptive filtering algorithm specifically designed to suppress correlated noise while preserving essential signal components. The proposed methodology centers around an optimized reproduction filter framework that systematically minimizes the root-mean-square deviation between processed output signals and their ideal reference waveforms through a sophisticated multi-stage computational approach. The implemented algorithm integrates several key processing components including comprehensive autocorrelation analysis of individual traces, precise synthesis of frequency-dependent filter responses tailored to specific noise characteristics, and iterative interference subtraction procedures. Extensive validation testing conducted using carefully constructed synthetic datasets incorporating Berlage pulse signals contaminated with controlled noise patterns demonstrated the algorithm's ability to produce output spectra that closely approximate ideal reference signals across all relevant frequency bands. When applied to actual field-collected radargram data under various geological conditions, the method achieved consistent noise reduction performance ranging between 30-50% amplitude suppression while successfully revealing previously obscured details in the wavefield patterns. Detailed spectral analysis confirmed the technique's particular effectiveness in mitigating both low-frequency (5 MHz) and high-frequency (165 MHz) noise components, including stubborn system-generated artifacts and equipment-specific interference that often resist conventional processing methods. The practical implementation of this advanced filtering approach offers substantial improvements in data interpretation accuracy for numerous critical applications including mineral resource exploration, detailed engineering site characterization, and sensitive archaeological investigations. The algorithm demonstrates special utility when processing datasets acquired in challenging environments featuring highly heterogeneous near-surface layers or complex interference patterns, where traditional methods typically fail to provide satisfactory results. Field deployment cases have shown that the technique enables more confident identification of subtle geological features, more precise delineation of structural boundaries, and enhanced detection of small-scale anthropogenic objects that frequently escape notice when using standard processing workflows. The robust performance across diverse operating conditions and geological settings makes this approach particularly valuable for both academic research and commercial applications where data quality and interpretation reliability are paramount concerns. Ongoing development efforts continue to refine the algorithm's computational efficiency and adaptiveness while exploring potential integration with emerging machine learning techniques to further enhance its noise discrimination capabilities and operational flexibility in increasingly complex survey environments. The methodology has already demonstrated measurable benefits in multiple practical scenarios including improved detection rates for buried utilities, enhanced resolution of stratigraphic sequences in sedimentary environments, and superior performance in archaeological prospection compared to conventional processing chains. These demonstrated advantages, combined with the algorithm's methodological rigor and consistent performance across varied test cases, position it as a valuable advancement in GPR signal processing with broad applicability across multiple scientific and engineering disciplines dealing with subsurface investigation challenges.