Identification of lateral discontinuities via multi-offset phase analysis of surface wave data

Surface wave methods are based on the inversion of observed Rayleigh wave phase-velocity dispersion curves. The goal is to estimate mainly the shear-wave velocity profile of the investigated site. The model used for the interpretation is 1D, hence results obtained wherever lateral variations are present cannot be considered reliable.In this paper, we study four synthetic models, all with a lateral heterogeneity. When we process the entire corresponding seismograms with traditional f-. k approach, the resulting 1D profiles are representative of the subsurface properties averaged over the whole length of the receivers lines. These results show that classical analysis disregards evidences of sharp lateral velocity changes even when they show up in the raw seismograms.In our research, we implement and test over the same synthetic models, a novel robust automated method to check the appropriateness of 1D model assumption and locate the discontinuities. This new approach is a development of the recent multi-offset phase analysis with the following further advantages: it does not need previous noise evaluation and more than one shot.Only once the discontinuities are clearly identified, we confidently perform classical f-k dispersion curve extraction and inversion separately on both sides of the discontinuity. Thus the final results, obtained by putting side by side the 1D profiles, are correct 2D reconstructions of the discontinuous S-wave distributions obtained without any additional ad-hoc hypotheses

Focusing inversion techniques applied to electrical resistance tomography in an experimental tank

We present an algorithm for focusing inversion of electrical resistivity tomography (ERT) data. ERT is a typical example of ill-posed problem. Regularization is the most common way to face this kind of problems; it basically consists in using a priori information about targets to reduce the ambiguity and the instability of the solution. By using the minimum gradient support (MGS) stabilizing functional, we introduce the following geometrical prior information in the reconstruction process: anomalies have sharp boundaries. The presented work is embedded in a project (L.A.R.A.) which aims at the estimation of hydrogeological properties from geophysical investigations. L.A.R.A. facilities include a simulation tank (4 m x 8 m x 1.35 m); 160 electrodes are located all around the tank and used for 3-D ERT. Because of the large number of electrodes and their dimensions, it is important to model their effect in order to correctly evaluate the electrical system response. The forward modelling in the presented algorithm is based on the so-called complete electrode model that takes into account the presence of the electrodes and their contact impedances. In this paper, we compare the results obtained with different regularizing functionals applied on a synthetic model.Comment: 4 pages, 7 figures, to appear in the Proceedings of Int. Assoc. for Mathematical Geology XI International Congres

Focusing inversion techniques applied to electrical resistance tomography in an experimental tank

We present an algorithm for focusing inversion of electrical resistivity tomography (ERT) data. ERT is a typical example of ill-posed problem. Regularization is the most common way to face this kind of problems; it basically consists in using a priori information about targets to reduce the ambiguity and the instability of the solution. By using the minimum gradient support (MGS) stabilizing functional, we introduce the following geometrical prior information in the reconstruction process: anomalies have sharp boundaries. The presented work is embedded in a project (L.A.R.A.) which aims at the estimation of hydrogeological properties from geophysical investigations. L.A.R.A. facilities include a simulation tank (4 m x 8 m x 1.35 m); 160 electrodes are located all around the tank and used for 3-D ERT. Because of the large number of electrodes and their dimensions, it is important to model their effect in order to correctly evaluate the electrical system response. The forward modelling in the presented algorithm is based on the so-called complete electrode model that takes into account the presence of the electrodes and their contact impedances. In this paper, we compare the results obtained with different regularizing functionals applied on a synthetic model

Focusing inversion technique applied to radar tomographic data

Traveltime tomography is a very effective tool to reconstruct acoustic, seismic or electromagnetic wave speed distribution. To infer the velocity image of the medium from the measurements of first arrivals is a typical example of ill-posed problem. In the framework of Tikhonov regularization theory, in order to replace an ill-posed problem by a well-posed one and to get a unique and stable solution, a stabilizing functional (stabilizer) has to be introduced. The stabilizer selects the desired solution from a class of solutions with a specific physical and/or geometrical property; e.g., the existence of sharp boundaries separating media with different petrophysical parameters. Usually stabilizers based on maximum smoothness criteria are used during the inversion process; in these cases the solutions provide smooth images which, in many situations, do not describe the examined objects properly. Recently a new algorithm of direct minimization of the Tikhonov parametric functional with minimum support stabilizer has been introduced; it produces clear and focused images of targets with sharp boundaries. In this research we apply this new technique to real radar tomographic data and we compare the obtained result with the solution generated by the more traditional minimum norm stabilizer.Comment: 4 pages, 1 figur

Multiple-point statistical simulation for hydrogeological models: 3D training image development and conditioning strategies

Most studies about the application of geostatistical simulations based on multiple-point statistics (MPS) to hydrogeological modelling focus on relatively fine-scale models and concentrate on the estimation of facies-level, structural uncertainty. Much less attention is paid to the use of input data and optimal construction of training images. For instance, even though the training image should capture a set of spatial geological characteristics to guide the simulations, the majority of the research still relies on 2D or quasi-3D training images. In the present study, we demonstrate a novel strategy for 3D MPS modelling characterized by: (i) realistic 3D training images, and (ii) an effective workflow for incorporating a diverse group of geological and geophysical data sets. The study covers an area of 2810 km2 in the southern part of Denmark. MPS simulations are performed on a subset of the geological succession (the lower to middle Miocene sediments) which is characterized by relatively uniform structures and dominated by sand and clay. The simulated domain is large and each of the geostatistical realizations contains approximately 45 million voxels with size 100 m × 100 m × 5 m. Data used for the modelling include water well logs, high-resolution seismic data, and a previously published 3D geological model. We apply a series of different strategies for the simulations based on data quality, and develop a novel method to effectively create observed sand/clay spatial trends. The training image is constructed as a small 3D voxel model covering an area of 90 km2. We use an iterative training image development strategy and find that even slight modifications in the training image create significant changes in simulations. Thus, the study underlines that it is important to consider both the geological environment, and the type and quality of input information in order to achieve optimal results from MPS modelling. In this study we present a possible workflow to build the training image and effectively handle different types of input information to perform large-scale geostatistical modellin

The Use of Tail as a Minimal-Invasive Method to Detect a Large Set of Biochemical Responses in the Italian Wall Lizard Podarcis siculus (Rafinesque, 1810)

Conventional methods to analyze biochemical processes related to contaminant toxicity usually require the sacrifice of animals to collect tissues and organs. However, for ethical reasons and especially for endangered species, non- or minimal-invasive methods should be preferred. Among vertebrates, reptiles show a general decline worldwide and therefore the use of non- or minimal-invasive methods to measure some biochemical processes in these animals are encouraged. It is well known that most lizards use a common safety behavior implying the natural loss of tail in the case of predation events. Therefore, if common analyses testing contaminant toxicity could be performed in tail tissue, this method, not implying the sacrifice of the animals, could be considered as a good minimal-invasive method. The aim of this study is to test on wild Italian wall lizard Podarcis siculus the use of tail to detect a large set of biomarkers including oxidative stress (TOSCAROO, TOSCAOH, CAT, tGSH, MDA), biotransformation processes (EROD, GSTs) and neurotoxicity (AChE, BChE). All the biochemical responses, excluding EROD and MDA, resulted to be analytically detectable in tail tissues of P. siculus, although the mean values obtained with this minimal-invasive method were significantly lower than those obtained with invasive one

Measuring and modeling water-related soil-vegetation feedbacks in a fallow plot

Abstract. Land fallowing is one possible response to shortage of water for irrigation. Leaving the soil unseeded implies a change of the soil functioning that has an impact on the water cycle. The development of a soil crust in the open spaces between the patterns of grass weed affects the soil properties and the field-scale water balance. The objectives of this study are to test the potential of integrated non-invasive geophysical methods and ground-image analysis and to quantify the effect of the soil–vegetation interaction on the water balance of fallow land at the local- and plot scale. We measured repeatedly in space and time local soil saturation and vegetation cover over two small plots located in southern Sardinia, Italy, during a controlled irrigation experiment. One plot was left unseeded and the other was cultivated. The comparative analysis of ERT maps of soil moisture evidenced a considerably different hydrologic response to irrigation of the two plots. Local measurements of soil saturation and vegetation cover were repeated in space to evidence a positive feedback between weed growth and infiltration at the fallow plot. A simple bucket model captured the different soil moisture dynamics at the two plots during the infiltration experiment and was used to estimate the impact of the soil vegetation feedback on the yearly water balance at the fallow site

Functional traits predict species co-occurrence patterns in a North American Odonata metacommunity

The probability of occurrence of a given species in a target locality and assemblage is conditioned not only by environmental/climatic variables but also by the presence of other species (i.e., species co-occurrence). This framework, already complex in nature, becomes even more complicated if one considers the functional traits of species that, in turn, might influence the structure of metacommunities in various ways. Depending on the ecological and environmental setting, functional similarity (i.e., convergence in morphological and ecological traits) between species might either reduce their co-occurrence due to high niche overlap driving negative interactions or promote it if the similar traits are associated with local habitat suitability. Similarly, functional divergence might either promote species co-occurrence by limiting negative interactions through niche separation or reduce it through trait mediated environmental filtering. Therefore, discriminating between these alternative scenarios—predicting whether two species will tend to co-occur or not based on their traits—is extremely challenging. Here, we develop a novel protocol to tackle the challenge, and we demonstrate its effectiveness by showing that ecological species traits can predict species co-occurrence in a large dataset of North American Odonata. To this end, we first used the Hierarchical Modeling of Species Communities framework to quantify the pairwise species co-occurrence after controlling for environmental and climatic factors. Then, we used machine learning to generate models which proved capable of predict accurately the observed co-occurrence patterns from species functional traits. Our approach offers a generalizable analytical framework with the potential to clarify long-standing ecological questions