We discuss a data‐processing sequence adopted to reprocess a seismic line that crosses the Italian southern Apennines from the Tyrrhenian Sea to the Adriatic margin and investigate both the overthrust and foreland areas. We first determine the main causes of the very low S/N ratio in the field data and then propose a processing sequence aimed at exploiting the signal content, also making use of a priori geological knowledge of this area. Our work indicates a combination of causes for the very low quality of the seismic data. These include length of the spread (about 20 km) that is unfavorable because of the rapid variation in the near‐surface geology, tectonic complexity, crooked‐line acquisition, and the rough topography associated with outcropping rocks characterized by highly variable velocities. Based on the outcome of this data analysis, we present a processing sequence driven by knowledge of the regional tectonic setting and by knowledge of the shallow subsurface geology. The main effort is in removing the large, near‐surface related noise components. The low S/N ratio makes it impossible or nearly impossible to successfully apply highly sophisticated techniques such as depth migration or wave equation datuming. Thus, we used robust techniques specifically designed to solve each problem that degraded data quality. The most relevant of these techniques were the removal of bad traces where unacceptably low quality was detected by energy and frequency decay criteria; estimation and correction for static time shifts attributable to near‐surface conditions; optimization of common midpoint (CMP) sorting to attenuate the deleterious effects of the crooked‐line acquisition; application of a weighted stacking technique to maximize stack power and application of prestack f-x deconvolution to attenuate uncorrelated noise. The outcome of this processing sequence is compared with the result of a more standard sequence that was previously applied to the same data and is also discussed in terms of the possible geological model it might evidence. The realization of a seismic section showing rather continuous and structured events down to 8 s which, depending on the interpretation, may be related to Moho discontinuity or to very deep sedimentary layers supports the efficacy of the processing approach we propose.
We present a stochastic full-waveform inversion that uses genetic algorithms (GA FWI) to estimate acoustic macro-models of the P-wave velocity field. Stochastic methods such as GA severely suffer the curse of dimensionality, meaning that they require unaffordable computer resources for inverse problems with many unknowns and expensive forward modeling. To mitigate this issue, we propose a two-grid technique, that is, a coarse grid to represent the subsurface for the GA inversion and a finer grid for the forward modeling. We applied this procedure to invert synthetic acoustic data of the Marmousi model. We show three different tests. The first two tests use as prior information a velocity model derived from standard stacking velocity analysis and differ only for the parameterization of the coarse grid. Their comparison shows that a smart parameterization of the coarse grid may significantly improve the final result. The third test uses a linearly increasing 1D velocity model as prior information, a layer-stripping procedure, and a large number of model evaluations. All the three tests return velocity models that fairly reproduce the long-wavelength structures of the Marmousi. First-break cycle skipping related to the seismograms of the final GA-FWI models is significantly reduced compared to the one computed on the models used as prior information. Descent-based FWIs starting from final GA-FWI models yield velocity models with low and comparable model misfits and with an improved reconstruction of the structural details. The quality of the models obtained by GA FWI + descent-based FWI is benchmarked against the models obtained by descent-based FWI started from a smoothed version of the Marmousi and started directly from the prior information models. The results are promising and demonstrate the ability of the two-grid GA FWI to yield velocity models suitable as input to descent-based FWI
A B S T R A C TWe investigate the interactions between the elastic parameters, V P , V S and density, estimated by non-linear inversion of AVA data, and the petrophysical parameters, depth (pressure), porosity, clay content and fluid saturation, of an actual gas-bearing reservoir. In particular, we study how the ambiguous solutions derived from the non-uniqueness of the seismic inversion affect the estimates of relevant rock properties. It results that the physically admissible values of the rock properties greatly reduce the range of possible seismic solutions and this range contains the actual values given by the well. By means of a statistical inversion, we analyse how approximate a priori knowledge of the petrophysical properties and of their relationships with the seismic parameters can be of help in reducing the ambiguity of the inversion solutions and eventually in estimating the petrophysical properties of the specific target reservoir. This statistical inversion allows the determination of the most likely values of the sought rock properties along with their uncertainty ranges. The results show that the porosity is the best-resolved rock property, with its most likely value closely approaching the actual value found by the well, even when we insert somewhat erroneous a priori information. The hydrocarbon saturation is the second best-resolved parameter, but its most likely value does not match the well data. The depth of the target interface is the least-resolved parameter and its most likely value is strongly dependent on a priori information. Although no general conclusions can be drawn from the results of this exercise, we envisage that the proposed AVA-petrophysical inversion and its possible extensions may be of use in reservoir characterization. I N T R O D U C T I O NSeismic inversion techniques applied to real, noisy data with limited apertures are rarely, if ever, capable of yielding unique results. Linearized inversion of observed-wave reflection amplitudes, even when searching for contrast information rather than for absolute values, is unable to recover fully the set of seismic parameters that characterize the target reflections (Stolt and Weglein 1985; Mace et al.The results of a full waveform and multicomponent inversion are still ambiguous: without the use of a priori information, density contrasts are almost unresolved (Simmons and Backus 1996;Lebrun et al. 2001). Non-linear AVA inversion of single interfaces does not overcome this ambiguity: depending on the noise level and on the range of available angles of incidence, the loci of the inversion solutions may be described by second-order curves in the parameter space, which can be associated with a 'null space' concept (Drufuca and Mazzotti 1995). This indicates that a set of elastic parameters (e.g. density, shear-and compressional-wave velocities) equally well fits the actual AVA data within the required error tolerance. However, it has to be ascertained if and how much the ambiguous solutions affect the estimation of petrophysical qua...
[1] The Alps are considered as a classical example for an orogen created by continental plate collision. In this study we present new images obtained from deep seismic reflection profiling in the Eastern Alps between Munich and Venice which give rise to examine and revise existing concepts. The seismic sections exhibit a prominent bi-verging reflection pattern at crustal scale. A major ramp-like structure, outcropping at the Inn-Valley fault, can be traced southward over 80 km into the mountain root where relics of the subducted Penninic ocean are expected. New models of the evolution of the Eastern Alps show an upper/lower crustal decoupling along transcrustal thrust faults with opposite thrust directions of both the European and the Adriatic-African continents.
Stochastic optimization methods, such as genetic algorithms, search for the global minimum of the misfit function within a given parameter range and do not require any calculation of the gradients of the misfit surfaces. More importantly, these methods collect a series of models and associated likelihoods that can be used to estimate the posterior probability distribution. However, because genetic algorithms are not a Markov chain Monte Carlo method, the direct use of the genetic-algorithm-sampled models and their associated likelihoods produce a biased estimation of the posterior probability distribution. In contrast, Markov chain Monte Carlo methods, such as the Metropolis-Hastings and Gibbs sampler, provide accurate posterior probability distributions but at considerable computational cost. In this paper, we use a hybrid method that combines the speed of a genetic algorithm to find an optimal solution and the accuracy of a Gibbs sampler to obtain a reliable estimation of the posterior probability distributions. First, we test this method on an analytical function and show that the genetic algorithm method cannot recover the true probability distributions and that it tends to underestimate the true uncertainties. Conversely, combining the genetic algorithm optimization with a Gibbs sampler step enables us to recover the true posterior probability distributions. Then, we demonstrate the applicability of this hybrid method by performing one-dimensional elastic full-waveform inversions on synthetic and field data. We also discuss how an appropriate genetic algorithm implementation is essential to attenuate the "genetic drift" effect and to maximize the exploration of the model space. In fact, a wide and efficient exploration of the model space is important not only to avoid entrapment in local minima during the genetic algorithm optimization but also to ensure a reliable estimation of the posterior probability distributions in the subsequent Gibbs sampler step
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