'A habitual disposition to the good': on reason, virtue and realism

Amidst the crisis of instrumental reason, a number of contemporary political philosophers including Jürgen Habermas have sought to rescue the project of a reasonable humanism from the twin threats of religious fundamentalism and secular naturalism. In his recent work, Habermas defends a post-metaphysical politics that aims to protect rationality against encroachment while also accommodating religious faith within the public sphere. This paper contends that Habermas’ post-metaphysical project fails to provide a robust alternative either to the double challenge of secular naturalism and religious fundamentalism or to the ruthless instrumentalism that underpins capitalism. By contrast with Habermas and also with the ‘new realism’ of contemporary political philosophers such as Raymond Geuss or Bernard Williams, realism in the tradition of Plato and Aristotle can defend reason against instrumental rationality and blind belief by integrating it with habit, feeling and even faith. Such metaphysical–political realism can help develop a politics of virtue that goes beyond communitarian thinking by emphasising plural modes of association (not merely ‘community’), substantive ties of sympathy and the importance of pursuing goodness and mutual flourishing

Microseismic Full Waveform Modeling in Anisotropic Media with Moment Tensor Implementation

Seismic anisotropy which is common in shale and fractured rocks will cause travel-time and amplitude discrepancy in different propagation directions. For microseismic monitoring which is often implemented in shale or fractured rocks, seismic anisotropy needs to be carefully accounted for in source location and mechanism determination. We have developed an efficient finite-difference full waveform modeling tool with an arbitrary moment tensor source. The modeling tool is suitable for simulating wave propagation in anisotropic media for microseismic monitoring. As both dislocation and non-double-couple source are often observed in microseismic monitoring, an arbitrary moment tensor source is implemented in our forward modeling tool. The increments of shear stress are equally distributed on the staggered grid to implement an accurate and symmetric moment tensor source. Our modeling tool provides an efficient way to obtain the Green’s function in anisotropic media, which is the key of anisotropic moment tensor inversion and source mechanism characterization in microseismic monitoring. In our research, wavefields in anisotropic media have been carefully simulated and analyzed in both surface array and downhole array. The variation characteristics of travel-time and amplitude of direct P- and S-wave in vertical transverse isotropic media and horizontal transverse isotropic media are distinct, thus providing a feasible way to distinguish and identify the anisotropic type of the subsurface. Analyzing the travel-times and amplitudes of the microseismic data is a feasible way to estimate the orientation and density of the induced cracks in hydraulic fracturing. Our anisotropic modeling tool can be used to generate and analyze microseismic full wavefield with full moment tensor source in anisotropic media, which can help promote the anisotropic interpretation and inversion of field data

Modeling of seismic wave propagation using high-order finite elements with MPI on a cluster of 192 GPUs

International audienc

High-order finite-element seismic wave propagation modeling on a cluster of 192 GPUs

International audienc

High-order Finite-Element Seismic Wave Propagation Modeling with MPI on a large GPU Cluster

International audienc

Imclass - a User-Tailored Machine Learning Image Classification Chain for Change Detection or Landcover Mapping

International audienceWith the increasing availability of satellite imagery at several spatial, spectral and temporal resolutions, the choice of the best image and the most appropriate method for object detection and classification of a broad range of land surface classes or processes is still a difficult task for many users. In order to guide the users, we proposed a user-tailored machine learning method (IMage CLASSification - ImCLASS) to detect and classifiy specific landcover classes. The method assumes a mono-class approach taking several ill-posed problems (e.g. class imbalance, high diversity inside the studied class, similarities with the adjacent samples…) as use cases (landslides, construction works in urban areas, burnt areas, vegetation classes…). It is a generalization of the ALADIM processor already validated in the context of landslide mapping and available as a service on the ESA GeoHazards Exploitation Platform (GEP). The proposed chain is able to combine optical and radar images, uses open source libraries, and is optimized for rapid calculation on HPC environments. The ImCLASS processor is presented and its performance is evaluated on three use cases: landslide detection and mapping after disasters in different regions of the World, urban classes change detection with a focus on construction works in Strasbourg, and crop mapping (vineyard) in the Grand-Est region. First results using either bi-dates or mono-date imagery are presented

A spectral-element seismic wave propagation algorithm on a cluster of 192 GPUs

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