E. Stucchi

Seismic exploration in complex terrains : A processing experience in the Southern Apennines

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 remov...

2D seismic exploration of the Ancona landslide (Adriatic Coast, Italy)

We have used on- and offshore seismic reflection profiles to determine the extension of a historic landslide at depth and toward the sea. The subsurface landslide structure was delineated by using four separate data sets produced by the combined use of geophone and hydrophone spreads, and of explosive and air-gun sources which also illuminated, through an undershooting configuration, the subsurface below the coastal road and railway. Many noise problems related to the source and environment were overcome and alleviated with several signal-processing routines. The resulting stack and depth-migrated sections reveal the deep geometry of the main landslide body and indicate the emergence location, at the landslide foot, of a deep, potential detachment surface, which previous investigations failed to evidence.

Comparison of Stochastic Optimization Methods on Two Analytic Objective Functions and on a 1D Elastic FWI

We compare the performance of three different stochastic optimization methods on two analytic objective functions varying the number of parameters, and on a 1D elastic full waveform inversion (FWI) problem. The three methods that we consider are the Adaptive Simulated Annealing (ASA), the Genetic Algorithm (GA), and the Neighbourhood Algorithm (NA) which are frequently used in seismic inversion. The application of these algorithms on the two analytic functions is aimed at evaluating the rate of convergence for different model space dimensions. The first function consists in a convex surface, and the second one is a multi-minima objective function which also permits to verify the ability of each method to escape from entrapment in local minima. Our study shows that among the three optimization methods GA displays the better scaling with the number of parameters. The ASA method is often the most efficient in case of low dimensional model spaces, whereas NA seems to perform less efficiently than the other two and to be more prone to get trapped in local minima. Tests of 1D elastic FWI on synthetic data, inverting for density, P and S-wave velocity for a total of 21 unknowns confirm the conclusions drawn from the previous examples.

Comparison between reprocessed seismic profiles: Seismologic and geologic data — A case study of the Colfiorito earthquake area

Seismic reflection data are used to reconstruct the subsurface geologic structures below the Umbria-Marche region in Italy, a highly seismogenic area with a recent history of seismic activity (the 1997–1998 Colfiorito sequence). We reprocess three vibroseis seismic profiles (acquired in the early 1980s for hydrocarbon exploration) whose stacked sections were optimized for relatively deep oil targets. On the reprocessed seismic profile closest to the epicentral area, we construct the main reflectors to a depth of about 4 s (two-way time) and compare this interpretation with the available hypocenters of the 1997 earthquakes. The improvements in visualizing the shallow and deep reflections provide a better correlation between the reflectors and the observed surface structures as well as a better delineation of the basement-rock geometry. We find that part of the Colfiorito sequence is localized around some of the reflectors in the reflection profile, which we interpret as related to the active normal faults ...

Comparing the performances of four stochastic optimisation methods using analytic objective functions, 1D elastic full-waveform inversion, and residual static computation

We compare the performances of four different stochastic optimisation methods using four analytic objective functions and two highly non-linear geophysical optimisation problems: 1D elastic full-waveform inversion (FWI) and residual static computation. The four methods we consider, namely, adaptive simulated annealing (ASA), genetic algorithm (GA), neighbourhood algorithm (NA), and particle swarm optimisation (PSO), are frequently employed for solving geophysical inverse problems. Because geophysical optimisations typically involve many unknown model parameters, we are particularly interested in comparing the performances of these stochastic methods as the number of unknown parameters increases. The four analytic functions we choose simulate common types of objective functions encountered in solving geophysical optimisations: a convex function, two multi-minima functions that differ in the distribution of minima, and a nearly flat function. Similar to the analytic tests, the two seismic optimisation problems we analyse are characterized by very different objective functions. The first problem is a 1D elastic FWI, which is strongly ill-conditioned and exhibits a nearly flat objective function, with a valley of minima extended along the density direction. The second problem is the residual static computation, which is characterized by a multi-minima objective function produced by the so-called cycle-skipping phenomenon. According to the tests on the analytic functions and on the seismic data, GA generally displays the best scaling with the number of parameters. It encounters problems only in the case of irregular distribution of minima, that is, when the global minimum is at the border of the search space and a number of important local minima are distant from the global minimum. The ASA method is often the best-performing method for low-dimensional model spaces, but its performance worsens as the number of unknowns increases. The PSO is effective in finding the global minimum in the case of low-dimensional model spaces with few local minima or in the case of a narrow flat valley. Finally, the NA method is competitive with the other methods only for low-dimensional model spaces; its performance stability sensibly worsens in the case of multi-minima objective functions. This article is protected by copyright. All rights reserved

Multicomponent Velocity Analysis by means of Covariance Measures and Complex Matched filters.

Velocity analysis for multicomponent data is revised in order to improve the accuracy of the velocity estimate and to combine information from horizontal and vertical components into a single panel. Multicomponent OBC data are strongly contaminated by coherent noise such as torsional modes, mud rolls, multiples and ghosts. The reflections cannot be properly resolved by the standard semblance operator that is insensitive to coherent noises and is unable to distinguish between interfering events. Velocity estimation is improved by the use of a coherence measurement based on the decomposition into eigenstructures of the spatial covariance matrix as well as by the approximate a-priori knowledge of the wavelet amplitude spectrum. The multicomponent panel, in which the velocity analysis is performed, is obtained by adding in quadrature the horizontal and the vertical responses. One of the main advantages is that the velocity analysis carried out on a single gather allows to speed up the velocity picking that, otherwise, has to be repeated for orthogonal panels. Moreover, lithologic bounds on the Vp/Vs ratio can be checked because the trends of pure and converted waves are mapped together. Tests performed on synthetic and real data show that the multicomponent velocity analysis provides accurate velocity estimations even for data at the early steps of processing.

Two-grid stochastic full waveform inversion of 2D marine seismic data

We apply stochastic Full Waveform Inversion (FWI) to 2D marine seismic data to estimate the macro-model velocity field which can be a suitable input for subsequent local (gradient based) FWI. Genetic Algorithms are used as the global optimization method. Our two-grid representation of the subsurface, made of a coarse grid for the inversion and of a fine grid for the modeling, allows us to reduce the number of unknowns to an acceptable number for the given computer resources and to perform a stable and reliable finite difference modeling. Thus, notwithstanding the known high computational costs that characterize global inversion methods, we are able to reconstruct a smooth, low wavenumber, acoustic velocity model of the subsurface. The reliability of the estimated velocity macro-model is checked through the inspection of prestack depth migrated gathers and through the superposition of observed and modeled seismograms. The method we propose is less affected by the risk of being trapped in local minima of the misfit functional than gradient based FWI methods, and can be a viable alternative to estimate proper starting models for gradient based full waveform inversions.

S/N enhancement by means of array simulation for near surface seismic investigations

We present a procedure for enhancing the signal-to-noise ratio (S/N) of shallow seismic reflection data based on two different steps: 1) an acquisition step that requires the recording of closely spaced common source records with standard source and receiver equipment and 2) a processing step where weighted or un-weighted source and receiver arrays are simulated on the basis of required needs for source related noise attenuation and depth penetration. The data acquisition can be carried out employing single source-single geophone recordings, with a standard 24 or 48-channel equipment. Simple energy sources such as weight drop or sledgehammer are considered. The design and application of the spatial filters in the processing phase is very flexible and can be tailored to the specific needs. In fact, the simulated source and/or receiver arrays can be time and/or space variant and can be weighted to provide the desired responses. Optimal weights can be determined by means of Chebyshev polynomials. Real data examples show the increase in the data quality in terms of better coherent noise attenuation and of enhanced depth penetration.

Stochastic FWI on Wide-angle Land Data with Different Order of Approximation of the 2D Acoustic Wave Equation

We experience the application of a genetic algorithm driven full-waveform inversion (GA FWI) on two expanding spread records acquired in a forward-reverse configuration along a 2D seismic land profile. Maximum source to receiver offset reach up to 40 km for the forward record and up to 30 km for the reverse record. The FWI is performed in time domain and with the acoustic 2D approximation. The data area is characterized by rough topography and complicate near surface and the seismograms show a low S/N ratio. We test whether, given the poor data quality and using a simple data misfit computation based on waveform envelopes and L1norm, the order of approximation of the spatial derivatives can be reduced without losing anything significant with respect to higher order approximations. The GA FWI experiments consider only the direct and diving waves of the shot records and constitute part of a wider project in which GA FWI is applied to both the expanding spread and the standard data to estimate a low-resolution velocity model apt to be used as a starting model for gradient based FWI. It turns out that reducing the order of approximation in the spatial derivatives computation from the 4th to the 2nd order does not appreciably change the matching between observed and predicted data as well as the estimated velocity models, while the computing time is drastically reduced. Also, in such data conditions, the adoption of more sophisticated misfit functions does not seem to produce significant improvements in the results.

SH-wave Seismic Reflection at the Patigno Landslide and Integration with P-wave Reflection Data

In this work we describe the acquisition and processing, up to the depth migrated image, of an SH-wave reflection seismic survey carried out on a complex deep seated landslide located in the Northern Apennines in Italy. We also show a comparison with a recently acquired P-wave seismic reflection profile that investigates the same landslide body. The P-wave survey was able to delineate the deep sliding discontinuity, but was unable to give a detailed description of the small reactivation slip surfaces delineating minor landslides at shallow depths, that are responsible of the major observed damages. Our experience shows that the combined use of both P-waves and SH-waves offers the possibility to obtain detailed insights of the whole landslide body from the deepest discontinuity up to the very shallow portion of the landslide, overcoming the limitations due to the low resolution of P-wave method for imaging shallow horizons and the low investigation depth of SH-wave method. The deeper knowledge of the landslide internal setting that can be gained by a joint application of both methodologies is of primary importance to plan adequate and effective defence strategies.