A review of open software resources in python for electrical resistivity modelling

Geophysical modelling performs to obtain subsurface structures in agreement with measured data. Freeware algorithms for geoelectrical data inversion have not been widely used in geophysical communities; however, different open-source modelling/inversion algorithms were developed in recent years. In this study, we review the structures and applications of openly Python-based inversion packages, such as pyGIMLi (Python Library for Inversion and Modelling in Geophysics), BERT (Boundless Electrical Resistivity Tomography), ResIPy (Resistivity and Induced Polarization with Python), pyres (Python wrapper for electrical resistivity modelling), and SimPEG (Simulation and Parameter Estimation in Geophysics). In addition, we examine the recovering ability of pyGIMLi, BERT, ResIPy, and SimPEG freeware through inversion of the same synthetic model forward responses. A versatile pyGIMLi freeware is highly suitable for various geophysical data inversion. The SimPEG framework is developed to allow the user to explore, experiment with, and iterate over multiple approaches to the inverse problem. In contrast, BERT, pyres, and ResIPy are exclusively designed for geoelectric data inversion. BERT and pyGIMLi codes can be easily modified for the intended applications. Both pyres and ResIPy use the same mesh designs and inversion algorithms, but pyres uses scripting language, while ResIPy uses a graphical user interface (GUI) that removes the need for text inputs. Our numerical modelling shows that all the tested inversion freeware could be effective for relatively larger targets. pyGIMLi and BERT could also obtain reasonable model resolutions and anomaly accuracies for small-sized subsurface structures. Based on the heterogeneous layered model and experimental target scenario results, the geoelectrical data inversion could be more effective in pyGIMLi, BERT, and SimPEG freeware packages. Moreover, this study can provide insight into implementing suitable inversion freeware for reproducible geophysical research, mainly for geoelectrical modelling.

Reviews

Exploiting Seismic Waveforms: Correlation, Heterogeneity and Inversion, by Brian L. N. Kennett and Andreas Fichtner, ISBN 978-1-108-82878-9, 2021, Cambridge University Press, 502 p. Hyperspectral Remote Sensing: Theory and Applications, by Prem Chandra Pandey et al., ISBN 978-0-081-02894-0, 2020, Elsevier, 506 p.

The Ethical Value of the Inhumanity in Art A Levinasian Reading

Reality and its Shadow, a brief yet powerful essay written in 1948, is the only text where Emmanuel Levinas (1906-1995) deals solely with the ontology of art. Already in this early text, we can see how his understanding that ethics is the ground of philosophy drives his discussion. The nature of art is therefore treated in relation to what it does, ethically, to the subject, the maker, and the viewer. Art is the “inhumanity” and “inversion” of ethics. Only philosophical criticism reintegrates its “inhumanity” in the ethical relation. The strength of Levinas’s philosophy issues from a pre-cognitive commitment to the “other”, epitomised in the “face to face” relation. Any philosophy emphasising the primacy of the subject over and above the “other” crumbles under his reading. Yet this same strength implies that those domains where the “face to face” relation is obscured lead to irresponsibility. One such domain is art. In this essay I argue that by applying his mature work to the criticism he advances in Reality and its Shadow we can find ethical value in art in virtue of its “inhumanity” and “inversion”. That is, we can agree with Levinas that art leads to irresponsibility, and yet ascribe to it positive ethical value in Levinas's own terms. This can help concretise the tension between the ethical and unethical aspects of art within a Levinasian framework.

Regularization, Bayesian Inference, and Machine Learning Methods for Inverse Problems

Classical methods for inverse problems are mainly based on regularization theory, in particular those, that are based on optimization of a criterion with two parts: a data-model matching and a regularization term. Different choices for these two terms and a great number of optimization algorithms have been proposed. When these two terms are distance or divergence measures, they can have a Bayesian Maximum A Posteriori (MAP) interpretation where these two terms correspond to the likelihood and prior-probability models, respectively. The Bayesian approach gives more flexibility in choosing these terms and, in particular, the prior term via hierarchical models and hidden variables. However, the Bayesian computations can become very heavy computationally. The machine learning (ML) methods such as classification, clustering, segmentation, and regression, based on neural networks (NN) and particularly convolutional NN, deep NN, physics-informed neural networks, etc. can become helpful to obtain approximate practical solutions to inverse problems. In this tutorial article, particular examples of image denoising, image restoration, and computed-tomography (CT) image reconstruction will illustrate this cooperation between ML and inversion.

Stepwise evolution of a butterfly supergene via duplication and inversion

ABSTRACTSupergenes maintain adaptive clusters of alleles in the face of genetic mixing. Although usually attributed to inversions, there are few cases in which the specific mechanisms of recombination suppression, and their timing, have been reconstructed in detail. We investigated the origin of the BC supergene, which controls variation in warning colouration in the African Monarch butterfly, Danaus chrysippus. By generating chromosome-scale assemblies for all three alleles, we identified multiple structural differences. Most strikingly, we find that a region of >1 million bp underwent several segmental duplications at least 7.5 million years ago. The resulting duplicated fragments appear to have triggered four inversions in surrounding parts of the chromosome, resulting in stepwise growth of the region of suppressed recombination. Phylogenies for the inversions are incongruent with the species tree, and suggest that structural polymorphisms have persisted for at least 4.1 million years. In addition to the role of duplications in triggering inversions, our results suggest a previously undescribed mechanism of recombination suppression through independent losses of divergent duplicated tracts. Overall, our findings challenge the idea of instantaneous supergene evolution through a single inversion event, instead pointing towards a stepwise process involving a variety of structural changes.