GNGTS 2015 - Atti del 34° Convegno Nazionale

and change parameters or even the used approach (e.g. by switching between the energy-based and the phase-based grouping methods). Indeed, the entire procedure (i.e. picking, grouping, and even the phase assessment) is implemented as an interactive process rather than a “ black box ”. We showed the versatility of the algorithm, which can be successfully applied to both reflection seismic and GPR data sets, as well as to both CO and CMP gathers. Furthermore, the process can equally perform at different levels of complexity in terms of structural domains and subsurface material variations. We demonstrated that data processing is not a critical issue for the procedure, although both picking and grouping performances are improved with higher signal-to-noise ratios. Nevertheless, the algorithm can be applied to data sets with just basic processing and without (or with limited) amplitude recovery, which allows it to pick amplitudes free from possible subjective distortions caused by the interpreter’s assumptions regarding the propagation velocity and signal dissipation in the subsurface materials. This is very important in case the picking results are to be used for further analysis (e.g. AVO) or inversion processes (e.g. amplitude inversion). Conclusion . We presented a few examples of application of an automated process designed to detect and track, in an accurate and objective way, reflections inside a recorded data set by exploiting their lateral phase continuity. The results, obtained in different profiles from both reflection seismic and GPR surveys, are quite accurate, since they are able to mark most of the recorded reflections and their different phases, with only a few exceptions in more complex areas characterized by noise or interference. Although the presented examples were limited to just 2D sections, the procedure can be easily extended to 3D data sets. A few input parameters must be selected and carefully evaluated by the interpreter, nevertheless the degree of subjectivity is greatly reduced with respect to other commonly used picking algorithms, leading to a faster and more objective process. Further improvements can be achieved by using integrated attributes as additional thresholds, or by evaluating the behavior of other physical parameters, such as changes in the spectral distribution. Acknowledgments. This research is partially funded by the “Finanziamento di Ateneo per progetti di ricerca scientifica – FRA 2014” of the University of Trieste. We thank Dr. Renato R. Colucci for his contribution in the acquisition and interpretation of the glaciological survey. The airport runway GPR section is property of Esplora srl, which is gratefully acknowledged. We also thank the “Istituto Nazionale di Oceanografia e Geofisica Sperimentale - OGS” for the permission to use and publish their proprietary seismic data, which can be purchased from them for scientific or industrial purposes. References Babcock E., and Bradford J., 2013, “Detecting subsurface contamination using Ground Penetrating Radar and Amplitude Variation with Offset analysis”, Proceedings of the 7th International Workshop on Advanced Ground Penetrating Radar, Nantes, France, pp. 1-5, ISBN 978-1-4799-0937-7 Baker G.S., 1998, “Applying AVO analysis to GPR data”, Geophysical Research Letters, vol. 25, no. 3, pp. 397-400 Barnes A.E., 1996, “Theory of 2-D complex seismic trace analysis”, Geophysics, vol. 61, no. 1, pp. 264-272 Barnes A.E., 2007, “A tutorial on complex seismic trace analysis”, Geophysics, vol. 72, no. 6, pp. W33-W43 Castagna J.P., and Backus M.M. (Eds.), 2007, “Offset-dependent reflectivity: Theory and practice of AVO analysis”, SEG, Investigations in Geophysics, vol. 8, 7th edition, 348 pp., ISBN 978-1-5608-0059-0 Chopra, S., and Marfurt K.J., 2005, “Seismic attributes - A historical perspective”, Geophysics, vol. 70, no. 5, pp. 3SO-28SO Colucci R.R., Forte E., Boccali C., Dossi M., Lanza L., Pipan M. and Guglielmin M., 2015, “Evaluation of internal structure, volume and mass of glacial bodies by integrated LiDAR and Ground Penetrating Radar surveys: The case study of Canin Eastern Glacieret (Julian Alps, Italy)”, Surveys in Geophysics, vol. 36, pp. 231–252 Dossi M., Forte E., and Pipan M., 2015a, “Automated reflection picking and polarity assessment through attribute analysis: Theory and application to synthetic and real ground-penetrating radar data”, Geophysics, vol. 80, no. 5, pp. H23-H35 Dossi M., Forte E., and Pipan M., 2015b, “Auto-picking and phase assessment by means of attribute analysis applied to GPR pavement inspection”, Proceedings of the 8th International Workshop on Advanced Ground Penetrating Radar, Florence, Italy, in press Dorn, G.A., 1998, “Modern 3-D seismic interpretation”, The Leading Edge, vol. 17, pp. 1262-1269 146 GNGTS 2015 S essione 3.3