ALBERT

Beschreibung: The non-uniqueness of the underdetermined inverse problem requires that any available geological information be incorporated to constrain the results. Such information commonly comes in the form of a geological model comprising unstructured wireframe surfaces. Hence, we perform geophysical modelling on unstructured meshes, which provide the flexibility required to efficiently incorporate complicated geological information. Designing spatial matrix operators for unstructured meshes is a non-trivial task. Gradient operators are required for powerful inversion regularization schemes that allow for the incorporation of geological information. Other authors have developed simple regularization schemes for unstructured meshes but those approaches do not use true gradient operators and do not allow for the incorporation of structural information. In this paper we develop new methods for generating spatial gradient operators on unstructured meshes. Our approach is essentially to fit a linear trend in a small neighbourhood around each cell. This results in a small linear system of equations to solve for each cell. Solving for the linear trend parameters yields the required information to construct the stationary gradient operators. Care must be taken when setting up the linear systems to avoid potential numerical issues. We test and compare our methods against the rectilinear mesh equivalents using some simple illustrative 2-D synthetic examples. Our methods are then applied to more complicated 2-D and 3-D examples, including real earth scenarios. This work provides a new method for regularizing inversions on unstructured meshes while allowing for the incorporation of structural orientation information.

Beschreibung: Joint inversion, the inversion of multiple geophysical data sets containing complementary information about the subsurface, has the potential to significantly improve inversion results by reducing the nonuniqueness of the inverse problem. One of the challenges of joint inversion is deciding how to couple the multiple physical property models. If a coupling approach is used that is inconsistent with the physical truth, then inversion artifacts can occur and may lead to incorrect interpretations. In this paper, we investigated the fuzzy c -means (FCM) clustering approach to provide a lithological coupling of the seismic velocity and density models in joint 2D inversions of first-arrival traveltimes and gravity data. Even though this coupling approach has been used in previous works, recommendations for its effective use have not yet been developed. We conducted a suite of joint inversion tests on synthetic data generated from a geologically realistic model based on magmatic massive sulfide deposits. There is a known relationship between seismic velocity and density for the silicate rocks and sulfide minerals involved; this lithological relationship was used to design a clustered coupling strategy in the joint inversions. The tests we conducted clearly exhibited the benefits of joint inversion using FCM coupling. Our work revealed the effects of including inaccurate a priori physical property information. We also evaluated approaches to assess whether such inaccurate information may have been used.

Beschreibung: Turnover of soil bacterial diversity driven by wide-scale environmental heterogeneity Nature Communications 4, 1434 (2013). doi:10.1038/ncomms2431 Authors: L. Ranjard, S. Dequiedt, N. Chemidlin Prévost-Bouré, J. Thioulouse, N.P.A. Saby, M. Lelievre, P. A. Maron, F.E.R Morin, A. Bispo, C. Jolivet, D. Arrouays & P. Lemanceau

Beschreibung: 〈span〉〈div〉SUMMARY〈/div〉Muography is a relatively new geophysical imaging method that uses muons to provide estimates of average densities along particular lines of sight. Muography can only see above the horizontal elevation of the detector and it is therefore attractive to attempt a joint inversion of muography data with gravity data, which is also responsive to density but generally requires combination with another geophysical data set to overcome issues related to non-uniqueness and poor depth resolution. Some previous work has investigated this joint inverse problem and demonstrated the potential improvements to be gained by jointly inverting muography and gravity data. However, there has yet to be a thorough investigation of different numerical approaches for formulating the joint inverse problem. Particularly important is how to account for the fact that even though the two data types are sensitive to the same physical quantity, density, they respond through different response functions. Moreover, the two measurements are affected by different systematic uncertainties that are difficult to model. In this work, we considered an approximation where the two density quantities, inferred from the two data types, can be related by an unknown scalar offset. We considered various existing and new joint inversion methods that might solve this problem and we applied them to a synthetic volcano imaging scenario based on the Puy de Dôme volcano in the Central Massif region of France. We used unstructured meshes in our modelling to adequately honour the significant topography in that scenario. Our experiments indicated that the most successful joint inversion method for this type of geological scenario was one in which the data misfit function is reformulated to automatically determine the best-fitting offset following a least-squares minimization argument. However, other approaches showed merit and we suggest several of the investigated methods be applied and compared for any specific joint inversion scenario.〈/span〉

Beschreibung: Due to the remote, seafloor environment in which seafloor massive sulphide deposits form, the assessment of the deposit volume and geometry through methods such as drilling are difficult and expensive. To aid in the evaluation of the resource potential of seafloor massive sulphide deposits, the threedimensional surface geometry inversion of magnetic data collected near the seafloor using autonomous underwater vehicles can further enhance geologic models produced from sparse drilling. This study applies the surface geometry inversion method to magnetic data collected above the active mound at the Trans-Atlantic Geotraverse hydrothermal vent field, creating a 3D wireframe model of the deposit and the hydrothermally altered basalt below it.