Similarity

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  Automated analysis of the back-scattering electromagnetic field BSEF. Carries out the transition from the presentation of data on the subsurface in the form of a set of amplitudes of the reflected signals in the form of a radarogram to the characteristics of this subsurface in the form of an attribute section. Such a presentation makes the result of processing GPR data more understandable both to geophysicists and specialists in related fields. For example, geologists or design-and-planning engineers. BSEF analysis works well in areas of strong noise located at the bottom of the GPR profile, which allows to increase the depth of GPR research. Along with this, the BSEF analysis provides information on the structure of the investigated subsurface environment even under conditions of a smooth change in its electrophysical characteristics, i.e. in the absence of layer boundaries on the radarogram.
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  B-Detector Method (Boundaries Detector). This method is designed to detect reflections from the boundaries of layers in the wave pattern of a GPR profile, suppress interference that overlaps these reflections, and increase the vertical resolution of GPR data. Using the B-Detector, the user manages the frequency and width of the spectrum of the signals, thereby changing their duration and degree of detail of the radarogram. As a result of processing by the B-Detector GPR method, the profile obtained by the low-frequency antenna looks as if the frequency of the GPR antenna during recording of this profile was several times higher. The resolution of signals on this GPR profile is also several times higher. If necessary, it is possible, and vice versa, to lower the central frequency of the radarogram signals
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  Method for decomposing a radarogram into components. This is not a wavelet decomposition of signals, although it is also implemented in the GEORADAR-EXPERT. This method converts a signals matrix of GPR profile into a set of matrices. Each matrix from this set contains only one unique type of reflected waves. The user discards the matrices with those types of waves, which he defined as interference, and then summarizes the remaining matrices. Thus, interference are excluded from the GPR profile signals. Using the same method, it is possible to remove diffracted reflections, including air ones, leaving behind only the boundaries of the layers on the radarogram. This is useful, for example, for road research. Or vice versa, you can remove all the boundaries of the layers, leaving only diffracted reflections. For example, to better detect reinforcement in a concrete structure.
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  Statistical analysis module. This module is intended to summarize the results of GPR profiling and to better presentation the variability of the electrophysical characteristics of the GPR profile. Using the results of statistical analysis, presented in the form of tables and graphs, it is possible to establish a relationship between the physicomechanical characteristics of the investigated subsurface environment (for example, soil or building structures) and various statistical indicators. Thus, a feature set is created that can be used in the future to assess the state of similar objects. Statistical data sources are GPR profiles, attribute sections, which are calculated based on the results of BSEF automated analysis, and slices of the 3D assembly. If the user has drawn the boundaries of the layers on the GPR profile, on the attribute section or on the 3D assembly slice, the statistical indicators are calculated inside each layer, along the boundary of each layer and for the whole object, without regard to separation into layers. Using the statistical analysis module implemented by GEORADAR-EXPERT raises the quality of GPR research results to a new, higher level.
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  Cluster analysis. Cluster analysis of attributes is used to rapid assessment the structure of the subsurface environment in a given approximation. The degree of approximation is determined by the number of clusters. The result of cluster analysis is a section of the attribute in the form of a combination of blocks of various shapes (clusters). Within a cluster, attribute values are the same. Through clusters, the section is divided into characteristic areas, each of which has its own features.
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  Flaw detection module of building structures. Flaw detection of concrete and reinforced concrete building structures is based on the identification of zones on the section with abnormal deviations of the values of the Q-factor attribute. Flaw detection results are saved in the form of a section of the attribute on which the defective zones are highlighted, and a defective list, in which for each GPR profile a quantitative and qualitative assessment of the state of the object is given, during the study of which one or another profile is obtained.