Retrieving shallow shear-wave velocity profiles from 2D seismic-reflection data with severely aliased surface waves

The inversion of surface-wave phase-velocity dispersion curves provides a reliable method to derive near-surface shear-wave velocity profiles. In this work, we invert phase-velocity dispersion curves estimated from 2D seismic-reflection data. These data cannot be used to image the first 50 m with seismic-reflection processing techniques due to the presence of indistinct first breaks and significant NMO-stretching of the shallow reflections. A surface-wave analysis was proposed to derive information about the near surface in order to complement the seismic-reflection stacked sections, which are satisfactory for depths between 50 and 700 m. In order to perform the analysis, we had to overcome some problems, such as the short acquisition time and the large receiver spacing, which resulted in severe spatial aliasing. The analysis consists of spatial partitioning of each line in segments, picking of the phase-velocity dispersion curves for each segment in the f-k domain, and inversion of the picked curves using the neighborhood algorithm. The spatial aliasing is successfully circumvented by continuously tracking the surface-wave modal curves in the f-k domain. This enables us to sample the curves up to a frequency of 40 Hz, even though most components beyond 10 Hz are spatially aliased. The inverted 2D VS sections feature smooth horizontal layers, and a sensitivity analysis yields a penetration depth of 20–25 m. The results suggest that long profiles may be more efficiently surveyed by using a large receiver separation and dealing with the spatial aliasing in the described way, rather than ensuring that no spatially aliased surface waves are acquired.Fil: Onnis, Luciano Emanuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; ArgentinaFil: Osella, Ana Maria. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; ArgentinaFil: Carcione, Jose M.. Istituto Nazionale di Oceanografia e di Geofisica Sperimentale; Itali

Characterisation of shallow marine sediments using high-resolution velocity analysis and genetic-algorithm-driven 1D elastic full-waveform inversion

We estimate the elastic properties of marine sediments beneath the seabed by means of high-resolution velocity analysis and one-dimensional elastic full-waveform inversion performed on twodimensional broad-band seismic data of a well-site survey. A high-resolution velocity function is employed to exploit the broad frequency band of the data and to derive the P-wave velocity field with a high degree of accuracy. To derive a complete elastic characterisation in terms of P-wave and S-wave velocities (Vp, Vs) and density of the subsurface, and to increase the resolution of the Vp estimates, we apply a one-dimensional elastic full-waveform inversion in which the outcomes derived from the velocity analysis are used as a priori information to define the Vp search range. The one-dimensional inversion is done using genetic algorithm as the optimisation method. It is performed by considering two misfit functions: the first uses the entire waveform to compute the misfit between modelled and observed seismograms, and the second considers the envelope of the seismograms, thus relaxing the requirement of an exact estimation of the wavelet phase. The full-waveform inversion and the high-resolution velocity analysis yield comparable Vp profiles, but the full-waveform inversion reconstruction is much more detailed. Regarding the full-waveform inversion results, the final depth models of P- and S-wave velocities and density show a fine-layered structure with a significant increase in velocities and density at shallow depth, which may indicate the presence of a consolidated layer. The very similar velocities and density-depth trends obtained by employing the two different misfit functions increase our confidence in the reliability of the predicted subsurface models

Automatic Stack Velocity Picking Using an Unsupervised Ensemble Learning Method

Seismic velocity picking algorithms that are both accurate and efficient can greatly speed up seismic data processing, with the primary approach being the use of velocity spectra. Despite the development of some supervised deep learning-based approaches to automatically pick the velocity, they often come with costly manual labeling expenses or lack interpretability. In comparison, using physical knowledge to drive unsupervised learning techniques has the potential to solve this problem in an efficient manner. We suggest an Unsupervised Ensemble Learning (UEL) approach to achieving a balance between reliance on labeled data and picking accuracy, with the aim of determining the stack velocity. UEL makes use of the data from nearby velocity spectra and other known sources to help pick efficient and reasonable velocity points, which are acquired through a clustering technique. Testing on both the synthetic and field data sets shows that UEL is more reliable and precise in auto-picking than traditional clustering-based techniques and the widely used Convolutional Neural Network (CNN) method

Depth and morphology of reflectors from the 2-D non-linear inversion of arrival-time and waveform semblance data: method and applications to synthetic data

We propose a two-dimensional, non-linear method for the inversion of reflected/converted traveltimes and waveform semblance designed to obtain the location and morphology of seismic reflectors in a lateral heterogeneous medium and in any source-to-receiver acquisition lay-out. This method uses a scheme of non-linear optimisation for the determination of the interface parameters where the calculation of the traveltimes is carried out using a finite- difference solver of the Eikonal equation, assuming an a priori known back- ground velocity model. For the search of the optimal interface model, we have used a multiscale approach and the Genetic Algorithm global optimization technique. During the initial stages of inversion, we used the arrival times of the reflection phase to retrieve the interface model that is defined by a small num- ber of parameters. In the successive steps, the inversion is based on the opti- mization of the semblance value determined along the calculated traveltime curves. Errors in the final model parameters and the criteria for the choice of the bestfit model are also estimated from the shape of the semblance function in the model parameter space. The method is tested and validated on a synthe- tic dataset that simulates the acquisition of reflection data in a complex volca- nic structure

Automatic Velocity Picking Using a Multi-Information Fusion Deep Semantic Segmentation Network

Velocity picking, a critical step in seismic data processing, has been studied for decades. Although manual picking can produce accurate normal moveout (NMO) velocities from the velocity spectra of prestack gathers, it is time-consuming and becomes infeasible with the emergence of large amount of seismic data. Numerous automatic velocity picking methods have thus been developed. In recent years, deep learning (DL) methods have produced good results on the seismic data with medium and high signal-to-noise ratios (SNR). Unfortunately, it still lacks a picking method to automatically generate accurate velocities in the situations of low SNR. In this paper, we propose a multi-information fusion network (MIFN) to estimate stacking velocity from the fusion information of velocity spectra and stack gather segments (SGS). In particular, we transform the velocity picking problem into a semantic segmentation problem based on the velocity spectrum images. Meanwhile, the information provided by SGS is used as a prior in the network to assist segmentation. The experimental results on two field datasets show that the picking results of MIFN are stable and accurate for the scenarios with medium and high SNR, and it also performs well in low SNR scenarios

Direct And Evolutionary Approaches For Optimal Receiver Function Inversion

Receiver functions are time series obtained by deconvolving vertical component seismograms from radial component seismograms. Receiver functions represent the impulse response of the earth structure beneath a seismic station. Generally, receiver functions consist of a number of seismic phases related to discontinuities in the crust and upper mantle. The relative arrival times of these phases are correlated with the locations of discontinuities as well as the media of seismic wave propagation. The Moho (Mohorovicic discontinuity) is a major interface or discontinuity that separates the crust and the mantle. In this research, automatic techniques to determine the depth of the Moho from the earth’s surface (the crustal thickness H) and the ratio of crustal seismic P-wave velocity (Vp) to S-wave velocity (Vs) (ï«= Vp/Vs) were developed. In this dissertation, an optimization problem of inverting receiver functions has been developed to determine crustal parameters and the three associated weights using evolutionary and direct optimization techniques

Inversion of Seismic Anisotropic Parameters Using Very Fast Simulated Annealing with Application to Microseismic Event Location

The study and interpretation of hydraulically stimulated regions, such as certain unconventional hydrocarbon reservoirs (e.g. Vaca Muerta Formation, Neuquén, Argentina), requires the accurate location of the induced microseismic events. The localization is carried out by means of the analysis of the travel times of the generated compressional and shear seismic waves from the unknown event position to a set of geophones, usually located in a nearby monitoring well. The accuracy of the localization, and thus the characterization of the fracturing process, can be strongly affected by the available seismic velocity model, from which only estimates are known. Also, the underlying medium usually shows an anisotropic behavior, meaning that the velocities of the seismic waves depend on the propagation direction. Therefore, knowledge of the parameters that characterize the anisotropy and an appropriate calibration of the velocities can reduce the errors in the localization of the microseismic events. In this paper we propose a strategy to simultaneously calibrate the velocity model and invert the anisotropy parameters from three-component microseismic data. The strategy relies on the hypothesis that the subsurface is composed of a finite number of horizontal layers with weak anisotropy, a widely used approximation that requires only three anisotropy parameters per layer. The differences between the observed and the calculated travel times, for a known seismic source, are quantified by means of an appropriate objective function that turns out to be non-linear and multimodal. For this reason, we minimize it using very fast simulated annealing (VFSA), a stochastic global optimization algorithm devised to find near-optimal solutions to hard optimization problems. Tests on synthetic data show that the proposed strategy can be used to effectively calibrate the seismic velocities and to provide appropriate estimates of the anisotropy parameters in spite of the severe non-uniqueness of the inverse problem at hand. Also, the stochastic nature of VFSA allows us to obtain the uncertainties of the solutions by repeating the inversion several times. Finally, by means of a simulated microseismic location example, we show the importance of having a well calibrated model to successfully estimate the locations of the hydraulically induced events.Facultad de Ciencias Astronómicas y GeofísicasConsejo Nacional de Investigaciones Científicas y Técnica

Inversion of Seismic Anisotropic Parameters Using Very Fast Simulated Annealing with Application to Microseismic Event Location

The study and interpretation of hydraulically stimulated regions, such as certain unconventional hydrocarbon reservoirs (e.g. Vaca Muerta Formation, Neuquén, Argentina), requires the accurate location of the induced microseismic events. The localization is carried out by means of the analysis of the travel times of the generated compressional and shear seismic waves from the unknown event position to a set of geophones, usually located in a nearby monitoring well. The accuracy of the localization, and thus the characterization of the fracturing process, can be strongly affected by the available seismic velocity model, from which only estimates are known. Also, the underlying medium usually shows an anisotropic behavior, meaning that the velocities of the seismic waves depend on the propagation direction. Therefore, knowledge of the parameters that characterize the anisotropy and an appropriate calibration of the velocities can reduce the errors in the localization of the microseismic events. In this paper we propose a strategy to simultaneously calibrate the velocity model and invert the anisotropy parameters from three-component microseismic data. The strategy relies on the hypothesis that the subsurface is composed of a finite number of horizontal layers with weak anisotropy, a widely used approximation that requires only three anisotropy parameters per layer. The differences between the observed and the calculated travel times, for a known seismic source, are quantified by means of an appropriate objective function that turns out to be non-linear and multimodal. For this reason, we minimize it using very fast simulated annealing (VFSA), a stochastic global optimization algorithm devised to find near-optimal solutions to hard optimization problems. Tests on synthetic data show that the proposed strategy can be used to effectively calibrate the seismic velocities and to provide appropriate estimates of the anisotropy parameters in spite of the severe non-uniqueness of the inverse problem at hand. Also, the stochastic nature of VFSA allows us to obtain the uncertainties of the solutions by repeating the inversion several times. Finally, by means of a simulated microseismic location example, we show the importance of having a well calibrated model to successfully estimate the locations of the hydraulically induced events.Facultad de Ciencias Astronómicas y GeofísicasConsejo Nacional de Investigaciones Científicas y Técnica