2D characterization of near-surface V P/V S: surface-wave dispersion inversion versus refraction tomography

International audienceThe joint study of pressure (P-) and shear (S-) wave velocities (Vp and Vs ), as well as their ratio (Vp /Vs), has been used for many years at large scales but remains marginal in near-surface applications. For these applications, and are generally retrieved with seismic refraction tomography combining P and SH (shear-horizontal) waves, thus requiring two separate acquisitions. Surface-wave prospecting methods are proposed here as an alternative to SH-wave tomography in order to retrieve pseudo-2D Vs sections from typical P-wave shot gathers and assess the applicability of combined P-wave refraction tomography and surface-wave dispersion analysis to estimate Vp/Vs ratio. We carried out a simultaneous P- and surface-wave survey on a well-characterized granite-micaschists contact at Ploemeur hydrological observatory (France), supplemented with an SH-wave acquisition along the same line in order to compare Vs results obtained from SH-wave refraction tomography and surface-wave profiling. Travel-time tomography was performed with P- and SH- wave first arrivals observed along the line to retrieve Vtomo p and Vtomo s models. Windowing and stacking techniques were then used to extract evenly spaced dispersion data from P-wave shot gathers along the line. Successive 1D Monte Carlo inversions of these dispersion data were performed using fixed Vp values extracted from Vtomo p the model and no lateral constraints between two adjacent 1D inversions. The resulting 1D Vsw s models were then assembled to create a pseudo-2D Vsw s section, which appears to be correctly matching the general features observed on the section. If the pseudo-section is characterized by strong velocity incertainties in the deepest layers, it provides a more detailed description of the lateral variations in the shallow layers. Theoretical dispersion curves were also computed along the line with both and models. While the dispersion curves computed from models provide results consistent with the coherent maxima observed on dispersion images, dispersion curves computed from models are generally not fitting the observed propagation modes at low frequency. Surface-wave analysis could therefore improve models both in terms of reliability and ability to describe lateral variations. Finally, we were able to compute / sections from both and models. The two sections present similar features, but the section obtained from shows a higher lateral resolution and is consistent with the features observed on electrical resistivity tomography, thus validating our approach for retrieving Vp/Vs ratio from combined P-wave tomography and surface-wave profiling

Bacteria-inducing legume nodules involved in the improvement of plant growth, health and nutrition

Bacteria-inducing legume nodules are known as rhizobia and belong to the class Alphaproteobacteria and Betaproteobacteria. They promote the growth and nutrition of their respective legume hosts through atmospheric nitrogen fixation which takes place in the nodules induced in their roots or stems. In addition, rhizobia have other plant growth-promoting mechanisms, mainly solubilization of phosphate and production of indoleacetic acid, ACC deaminase and siderophores. Some of these mechanisms have been reported for strains of rhizobia which are also able to promote the growth of several nonlegumes, such as cereals, oilseeds and vegetables. Less studied are the mechanisms that have the rhizobia to promote the plant health; however, these bacteria are able to exert biocontrol of some phytopathogens and to induce the plant resistance. In this chapter, we revised the available data about the ability of the legume nodule-inducing bacteria for improving the plant growth, health and nutrition of both legumes and nonlegumes. These data showed that rhizobia meet all the requirements of sustainable agriculture to be used as bio-inoculants allowing the total or partial replacement of chemicals used for fertilization or protection of crops

Retrieving Surface Wave Dispersion Curves from Cross-Spread Seismic Acquisition.

Three-dimensional (3D) seismic surveys are always more often used both in oil prospection and in near surface problems to handle complex geological structures. At the same time surface wave methods are often coupled to other seismic techniques (2D seismic reflection or refraction) where we can use seismic data to retrieve dispersion curve along the seismic line. In this work we prove the possibility of retrieving dispersion curves from cross-spread 3D seismic acquisition scheme. Several tests based on synthetic and real data show that the dispersion curves estimated from cross-spread seismic acquisition scheme are in agreement with the dispersion curves estimated through the traditional methods. Furthermore, through a 3D synthetic model, we show that the proposed processing approach can be used to retrieve 3D subsoil models, also for inaccessible areas

Scale property Monte Carlo driven inversion of surface wave data

Analysis of surface wave data can be made using probabilistic approaches, e.g. Monte Carlo methods that employ a random or pseudorandom generator. A method like this is required to efficiently avoid local minima, evaluate nonuniqueness in the solution and estimating the values and uncertainties of the model parameters. The pure Monte Carlo method applied to surface wave inversion becomes efficient with the introduction of a “smart sampling” rule which exploits the scale property (scaling of the modal solution with the wavelength) of the solution. Introducing this property in the Monte Carlo inversion focuses the scan of model space on high probability density zones. Each model is scaled before evaluating the misfit in order to bring the theoretical dispersion curve obtained by forward algorithm closer to the experimental. An applicative example is presented to support our hypothesis. The main advantage of the proposed approach, based on scale property of dispersion curves, is the possibility of using a pure Monte Carlo method with a limited number of simulations. This leads to a solution which accounts for data uncertainties evidencing equivalent final models. The possible bias of the result from a wrong choice of the initial model is significantly reduced