GNGTS 2019 - Atti del 38° Convegno Nazionale

GNGTS 2019 S essione 3.3 761 References Bozdag, E., Trampert, J., Tromp, J., 2011, Misfit functions for full waveform inversion based on instantaneous phase and envelope measurements, Geophys. J. Int. , 185 , 845-870 Engquist, B. & Froese, B. D., 2014, Application of the Wasserstein metric to seismic signals, Communications in Mathematical Sciences , 12(5) , 979–988 Fichtner, A., 2011, Full seismic waveform modelling and inversion, Springer Galuzzi B. G., Zampieri E. & Stucchi E. M. 2017. A local adaptive method for the numerical approximation in seismic wave modelling, Communications in Applied and Industrial Mathematics , 8(1) , 265-281 Jiménez Tejero, C. E. , Dagnino, D., Sallarès, V. & Ranero, C. R., 2015, Comparative study of objective functions to overcome noise and bandwidth limitations in full waveform inversion, Geophys. J. Int. , 203 , 632-645 Pladys, A., Brossier, R.B., Métivier, L., 2017, FWI Alternative Misfit Functions – What Properties Should They Satisfy?, 79th EAGE Conference and Exhibition Sajeva, A., Bienati, N., Aleardi, M., Stucchi, E., Mazzotti, A., 2016, Estimation of acoustic macro-models using a genetic full-waveform inversion: applications to the Marmousi model, Geophysics , 81 , no. 4, R173-R184 Tarantola, A., 1984, Inversion of seismic reflection data in the acoustic approximation, Geophysics , 49 , 1259-1266 Virieux, J. & S. Operto, 2009, An overview of full-waveform inversion in exploration geophysics, Geophysics , 74 , no. 6, WCC1–WCC26 Warner, M. & Guash,L., 2016, Adaptive waveform inversion: Theory, Geophysics , 81 , R429-R445 Yang, Y., Engquist, B., Sun, J. & Froese, B. D., 2018, Application of optimal transport and the quadratic Wasserstein metric to full-waveform inversion, Geophysics , 83 , R43-R62 WAVELET-BASED COHERENCY FUNCTIONAL FOR VELOCITY ANALYSIS OF SEISMIC REFLECTION DATA A.Tognarelli Earth Sciences Department, University of Pisa, Italy Introduction. Velocity analysis is the step in the seismic reflection data processing workflow aimed at estimating a velocity field of the P-waves. The knowledge of a velocity model, even if approximate, is necessary to perform subsequent operations such as conventional geometrical spreading and normal move-out corrections, as well as advanced procedures such as migration and inversions. Velocity analysis is accomplished by measuring the coherency of the data samples along trial hyperbolic trajectories. The commonly used functional for the evaluation of coherency is the Semblance estimator (Neidell and Taner, 1971), which is generally robust against random noise and not time-consuming. However, it produces low-resolution coherencies and it is not adequate if coherent noise occurs in the data or in case of events that are close in time and/or velocity. Nowadays, different algorithms have been developed and able to provide coherency measures with a high-resolution both in time and velocity (Key and Smithson, 1990, Tognarelli et al. , 2013, Abbad and Ursin, 2012, Spagnolini et al. , 1993). All these methods assume a stationary nature of the recorded signals and, for this reason, they adopt a constant set of parameters while computing the velocity spectra. In general, the panels estimated in this way can be considered adequate for the location, in the time-velocity plane, of the most important reflected events. However, if the analysis is performed on data recorded in complex geological contexts characterized by low signal-to-noise ratio and strong lateral and vertical attenuation phenomena, a fixed parameter setting is no longer suitable for the computation of velocity spectra. This work presents a new coherency functional to the velocity analysis of seismic reflection data. The functional performs the coherency measure in the time-scale domain computed by the multi-resolution technique of Continuous-Wavelet Transform (CWT) (Daubechies, 1990, 1992) and provides a three-component analysis: time, velocity and scale (or frequency). By