Ground Penetrating Radar (GPR) systems with multi-concurrent sampling receivers can rapidly acquire dense multi-offset GPR data, which is not feasible using typical common offset (CO) GPR systems with a single, fixed offset transmitter-receiver pair. Multi-offset GPR data from these new multi-concurrent receiver systems have the potential to be used to create detailed subsurface velocity models and enhanced reflection sections. These are important features that can improve qualitative and quantitative interpretation of GPR data. In order to realize these benefits and to deal with the large amount of multi-offset data generated by these new systems, we have developed an automated and customized data processing workflow. There are three key algorithms that we have developed as part of our workflow, which are crucial for processing large volume, multi-offset GPR data so as: firstly, to efficiently correct and manage time misalignments from multi-concurrent receivers; secondly, to carry out trace balancing of common mid-point (CMP) data for semblance analysis; and thirdly, to automate the velocity analysis step. We showcase our processing workflow using two field datasets acquired using a multi-concurrent sampling receiver GPR system consisting of one transmitter and seven receivers. The field data were collected at two different locations: a site using a system with a 500 MHz center frequency and another site using a system with a 1000 MHz center frequency. We demonstrated, with both datasets, that our processing workflow could produce automated stacking velocity fields and enhanced zero-offset reflection cross-sections. These benefits increase the information that can be used for interpretation (compared to conventional CO data) and can form the basis of further processing steps such as migration. As the cost of these multi-concurrent sampling receiver systems decreases over time, we anticipate their use, and the acquisition of dense multi-offset GPR data, to become much more commonplace.
Symbolic regression (SR) is a machine learning-based regression method based on genetic programming principles that integrates techniques and processes from heterogeneous scientific fields and is capable of providing analytical equations purely from data. This remarkable characteristic diminishes the need to incorporate prior knowledge about the investigated system. SR can spot profound and elucidate ambiguous relations that can be generalizable, applicable, explainable and span over most scientific, technological, economical, and social principles. In this review, current state of the art is documented, technical and physical characteristics of SR are presented, the available programming techniques are investigated, fields of application are explored, and future perspectives are discussed.
Processing Ground Penetrating Radar (GPR) data is essential to assist with interpretation, the reduction of unwanted noise, and to highlight information relating to the purpose of a survey. Currently, the majority of GPR data processing software is coupled together with GPR hardware systems from the manufacturers, and often at an additional cost. These software packages often incorporate simplified or limited processing methods, and they do not allow the processing of data generated by GPR systems of different manufacturers. We present a MATLAB-based software with a user-friendly graphical user interface (GUI) for the visualization and processing of GPR data. The software supports file formats from a wide variety of GPR manufacturers, and can handle both single-fold (SF) and multi-fold (MF) GPR data. The software is capable of applying standard workflows, but one of its key features is novel processing algorithms for MF GPR data, especially from the new multi-concurrent sampling receiver GPR systems. These algorithms are more commonly used in seismic data processing and have not previously been applied to GPR data from these systems.
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