GNGTS 2015 - Atti del 34° Convegno Nazionale

GNGTS 2015 S essione 3.3 141 Prodehl C., Kennett B.,Artemieva I.M. andThyboH.; 2013: 100 years of seismic research on theMoho . Tectonophysics, 609 , 9–44. Ryden N., Park C.B., Ulriksen P. and Miller R.D.; 2004: Multimodal Approach to Seismic Pavement Testing . Journal of Geotechnical and Geoenvironmental Engineering, 130 , 636-645. Scales J.A., Smith M.L. and Treitel S.; 2001: Introductory Geophysical Inverse Theory . Open file, Samizdat Press, 193 pp. http://samizdat.mines.edu Tada T., Cho I. and Shinozaki Y.; 2009: New Circular-Array Microtremor Techniques to Infer Love- ave Phase Velocities . Bull. Seism. Soc. Am., 99 , 2912–2926. TokimatsuK., Tamura S. andKojima H.; 1992: Effects of MultipleModes on Rayleigh ave Dispersion Characteristics . Journal of Geotechnical Engineering, 118 , 1529-1543. Application of attribute-based automated picking to GPR and seismic surveys M. Dossi, E. Forte, M. Pipan Department of Mathematics and Geosciences (DMG), University of Trieste, Italy Introduction . An accurate reflection picking, as independent as possible from the subjectivity of the interpreter, is of paramount importancewhen performing both qualitative (e.g. stratigraphic interpretation) and quantitative (e.g. amplitude inversion) analyses of several wave-field geophysical surveys. Automated picking processes can be used to facilitate interpretation and to recover several parameters and attributes (Chopra and Marfurt, 2005) from the recorded profile, most importantly the reflected amplitudes and the two-way traveltimes, which can then be used to estimate the main impedance contrasts in the subsurface. In ground penetrating radar (GPR) surveys, examples of application include the identification of contaminants in near-surface hydrogeological settings (Backer, 1998; Babcock and Bradford, 2013); the inspection and maintenance of roads through the identification of damaged sections (Saarenketo and Scullion, 2000); and the monitoring of glaciers in terms of their temporal variations in stratigraphy and water content (Forte et al. , 2014a, 2014b; Colucci et al. , 2015). In seismic surveys, an accurate picking can be used for first-breaks detection and data processing (Sabbione and Velis, 2010); amplitude-versus-offset analysis (AVO; Castagna and Backus, 2007); and the identification of faults in a profile, which are characterized by discontinuities in the picked events (Hoyes and Cheret, 2011). Several picking techniques exist (Dorn, 1998), and they differ in terms of 1) their adopted picking criteria, like for example manual picking, amplitude cross-correlation, or interpolation between control points (seeds); 2) the assumptions made with regards to the analyzed data set, for example that the recorded events are locally smooth; 3) the accuracy of the picked results and their dependence on the interpreter’s subjectivity and experience. We developed an automated process designed to accurately detect, and mark as a horizon, any event that shows lateral phase continuity, and to select specific reflection phases for subsequent analysis and interpretation. In this paper, we give a short description of the main features of the implemented algorithm (more details in Dossi et al. , 2015a, b; Forte et al. , 2015), and show a few examples of its application to both GPR and seismic data sets. Automated picking . The algorithm starts by performing attribute analysis on the recorded data set, also known as complex trace analysis (Taner et al. , 1979; Barnes, 1996, 2007), separating the signal into its reflection strength (also referred to as instantaneous amplitude, or trace envelope) and the cosine of its instantaneous phase (also referred to as cosine phase). The cosine phase profile allows to follow reflections more accurately with respect to the