Basement lithostratigraphy of the Adula nappe: implications for Palaeozoic evolution and Alpine kinematics

The Adula nappe belongs to the Lower Penninic domain of the Central Swiss Alps. It consists mostly of pre-Triassic basement lithologies occurring as strongly folded and sheared gneisses of various types with mafic boudins. We propose a new lithostratigraphy for the northern Adula nappe basement that is supported by detailed field investigations, U–Pb zircon geochronology, and whole-rock geochemistry. The following units have been identified: Cambrian clastic metasediments with abundant carbonate lenses and minor bimodal magmatism (Salahorn Formation); Ordovician metapelites associated with amphibolite boudins with abundant eclogite relicts representing oceanic metabasalts (Trescolmen Formation); Ordovician peraluminous metagranites of calc-alkaline affinity ascribed to subduction-related magmatism (Garenstock Augengneiss); Ordovician metamorphic volcanic–sedimentary deposits (Heinisch Stafel Formation); Early Permian post-collisional granites recording only Alpine orogenic events (Zervreila orthogneiss). All basement lithologies except the Permian granites record a Variscan + Alpine polyorogenic metamorphic history. They document a complex Paleozoic geotectonic evolution consistent with the broader picture given by the pre-Mesozoic basement framework in the Alps. The internal consistency of the Adula basement lithologies and the stratigraphic coherence of the overlying Triassic sediments suggest that most tectonic contacts within the Adula nappe are pre-Alpine in age. Consequently, mélange models for the Tertiary emplacement of the Adula nappe are not consistent and must be rejected. The present-day structural complexity of the Adula nappe is the result of the intense Alpine ductile deformation of a pre-structured entity.     

Final analysis of ELAIS 15-μm observations: method, reduction and catalogue

We present the final analysis of the European Large Area Infrared Space Observatory ( ISO ) Survey (ELAIS) 15-μm observations, carried out with the ISO Camera (ISOCAM) instrument on board the ISO .
The data-reduction method, known as the Lari Method, is based on a mathematical model of the behaviour of the detector and was specifically designed for the detection of faint sources in ISOCAM/ISO Photopolarimeter (ISOPHOT) data. The method is fully interactive and leads to very reliable and complete source lists.
The resulting catalogue includes 1923 sources detected with signal-to-noise ratio of  > 5  in the 0.5–100 mJy flux range and over an area of 10.85 deg² split into four fields, making it the largest non-serendipitous extragalactic source catalogue obtained to date from the ISO data.
This paper presents the concepts underlying the data-reduction method together with its latest enhancements. The data-reduction process, the production and basic properties of the resulting catalogue are discussed. The catalogue quality is assessed by means of detailed simulations, optical identifications and comparison with previous analyses.     

1D elastic full‐waveform inversion and uncertainty estimation by means of a hybrid genetic algorithm–Gibbs sampler approach

Stochastic optimization methods, such as genetic algorithms, search for the global minimum of the misfit function within a given parameter range and do not require any calculation of the gradients of the misfit surfaces. More importantly, these methods collect a series of models and associated likelihoods that can be used to estimate the posterior probability distribution. However, because genetic algorithms are not a Markov chain Monte Carlo method, the direct use of the genetic‐algorithm‐sampled models and their associated likelihoods produce a biased estimation of the posterior probability distribution. In contrast, Markov chain Monte Carlo methods, such as the Metropolis–Hastings and Gibbs sampler, provide accurate posterior probability distributions but at considerable computational cost. In this paper, we use a hybrid method that combines the speed of a genetic algorithm to find an optimal solution and the accuracy of a Gibbs sampler to obtain a reliable estimation of the posterior probability distributions. First, we test this method on an analytical function and show that the genetic algorithm method cannot recover the true probability distributions and that it tends to underestimate the true uncertainties. Conversely, combining the genetic algorithm optimization with a Gibbs sampler step enables us to recover the true posterior probability distributions. Then, we demonstrate the applicability of this hybrid method by performing one‐dimensional elastic full‐waveform inversions on synthetic and field data. We also discuss how an appropriate genetic algorithm implementation is essential to attenuate the “genetic drift” effect and to maximize the exploration of the model space. In fact, a wide and efficient exploration of the model space is important not only to avoid entrapment in local minima during the genetic algorithm optimization but also to ensure a reliable estimation of the posterior probability distributions in the subsequent Gibbs sampler step.     

Combining discrete cosine transform and convolutional neural networks to speed up the Hamiltonian Monte Carlo inversion of pre-stack seismic data

Markov chain Monte Carlo algorithms are commonly employed for accurate uncertainty appraisals in non-linear inverse problems. The downside of these algorithms is the considerable number of samples needed to achieve reliable posterior estimations, especially in high-dimensional model spaces. To overcome this issue, the Hamiltonian Monte Carlo algorithm has recently been introduced to solve geophysical inversions. Different from classical Markov chain Monte Carlo algorithms, this approach exploits the derivative information of the target posterior probability density to guide the sampling of the model space. However, its main downside is the computational cost for the derivative computation (i.e. the computation of the Jacobian matrix around each sampled model). Possible strategies to mitigate this issue are the reduction of the dimensionality of the model space and/or the use of efficient methods to compute the gradient of the target density. Here we focus the attention to the estimation of elastic properties (P-, S-wave velocities and density) from pre-stack data through a non-linear amplitude versus angle inversion in which the Hamiltonian Monte Carlo algorithm is used to sample the posterior probability. To decrease the computational cost of the inversion procedure, we employ the discrete cosine transform to reparametrize the model space, and we train a convolutional neural network to predict the Jacobian matrix around each sampled model. The training data set for the network is also parametrized in the discrete cosine transform space, thus allowing for a reduction of the number of parameters to be optimized during the learning phase. Once trained the network can be used to compute the Jacobian matrix associated with each sampled model in real time. The outcomes of the proposed approach are compared and validated with the predictions of Hamiltonian Monte Carlo inversions in which a quite computationally expensive, but accurate finite-difference scheme is used to compute the Jacobian matrix and with those obtained by replacing the Jacobian with a matrix operator derived from a linear approximation of the Zoeppritz equations. Synthetic and field inversion experiments demonstrate that the proposed approach dramatically reduces the cost of the Hamiltonian Monte Carlo inversion while preserving an accurate and efficient sampling of the posterior probability.     

Neo-deterministic seismic hazard assessment in North Africa

North Africa is one of the most earthquake-prone areas of the Mediterranean. Many devastating earthquakes, some of them tsunami-triggering, inflicted heavy loss of life and considerable economic damage to the region. In order to mitigate the destructive impact of the earthquakes, the regional seismic hazard in North Africa is assessed using the neo-deterministic, multi-scenario methodology (NDSHA) based on the computation of synthetic seismograms, using the modal summation technique, at a regular grid of 0.2?×?0.2°. This is the first study aimed at producing NDSHA maps of North Africa including five countries: Morocco, Algeria, Tunisia, Libya, and Egypt. The key input data for the NDSHA algorithm are earthquake sources, seismotectonic zonation, and structural models. In the preparation of the input data, it has been really important to go beyond the national borders and to adopt a coherent strategy all over the area. Thanks to the collaborative efforts of the teams involved, it has been possible to properly merge the earthquake catalogues available for each country to define with homogeneous criteria the seismogenic zones, the characteristic focal mechanism associated with each of them, and the structural models used to model wave propagation from the sources to the sites. As a result, reliable seismic hazard maps are produced in terms of maximum displacement (D _max), maximum velocity (V _max), and design ground acceleration.