Backward Monte-Carlo applied to muon transport

Niess, V.; Barnoud, Anne; Cârloganu, C.; Ménédeu, E. Le

doi:10.1016/j.cpc.2018.04.001

Cited by 38 publications

(63 citation statements)

References 24 publications

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“…Details on the production of atmospheric muons and their energy loss can be found in Tanabashi et al (2018). The muon transport problem can be efficiently solved by Monte-Carlo simulation, for example as described in Niess et al (2018a). After various processing steps (see Niess et al 2018b), one arrives at the final muography data that will be input into the geophysical inversion; that data comprises average densities along solid angles, by which we mean small bins across the azimuthal and polar angles, for example in one degree increments.…”

Section: Forward Modellingmentioning

confidence: 99%

Joint inversion methods with relative density offset correction for muon tomography and gravity data, with application to volcano imaging

Lelièvre

Barnoud

Niess

et al. 2019

Geophysical Journal International

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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.

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Section: Forward Modellingmentioning

confidence: 99%

Joint inversion methods with relative density offset correction for muon tomography and gravity data, with application to volcano imaging

Lelièvre

Barnoud

Niess

et al. 2019

Geophysical Journal International

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“…At each simulation step through the target material, the muon interactions with matter are split into a continuous component describing collective processes, such as multiple scattering and continuous energy loss, and one or more discrete interactions; the optimal threshold for transition between the two regimes depends on the application. The authors highlight a few case studies [150] where the outcomes of PUMAS, run in forward and backward mode, agree to better than 1%, stating that PUMAS can achieve an accuracy comparable to GEANT4 but with a speedup of two orders of magnitude in backward mode.…”

Section: Monte Carlo Issuesmentioning

confidence: 93%

Atmospheric muons as an imaging tool

2020

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Imaging methods based on the absorption or scattering of atmospheric muons, collectively named under the neologism "muography", exploit the abundant natural flux of muons produced from cosmic-ray interactions in the atmosphere. Recent years have seen a steep rise in the development of muography methods in a variety of innovative multidisciplinary approaches to study the interior of natural or man-made structures, establishing synergies between usually disconnected academic disciplines such as particle physics, geology, and archaeology. Muography also bears promise of immediate societal impact through geotechnical investigations, nuclear waste surveys, homeland security, and natural hazard monitoring. Our aim is to provide an introduction to this vibrant research area, starting from the physical principles at the basis of the methods and reviewing several recent developments in the application of muography methods to specific use cases, without any pretence of exhaustiveness. We then describe the main detector technologies and imaging methods, including their combination with conventional techniques from other disciplines, where appropriate. Finally, we discuss critically some outstanding issues that affect a broad variety of applications, and the current state of the art in addressing them. a

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“…These averaged densities μ are estimated from the flux of muons crossing the edifice (Cârloganu and the TOMUVOL Collaboration, 2018;Niess et al, 2018b) and are linearly related to the subsurface densities ρ via the sensitivity matrix M:…”

Section: Joint Inversion Methodsmentioning

confidence: 99%

“…Below this elevation, muons also cross the Petit Puy de Dôme, situated to the north-east of the Puy de Dôme (Figure 1), which is outside the region of interest in this study. We recall here the key steps of the process (Niess et al, 2016;Cârloganu and The TOMUVOL Collaboration, 2018;Niess et al, 2018a;Niess et al, 2018b;Niess et al, 2020). The muon tracks are summed up in bins of 1°by 1°in azimuth and elevation.…”

Section: Datamentioning

confidence: 99%

Robust Bayesian Joint Inversion of Gravimetric and Muographic Data for the Density Imaging of the Puy de Dôme Volcano (France)

et al. 2021

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Imaging the internal structure of volcanoes helps highlighting magma pathways and monitoring potential structural weaknesses. We jointly invert gravimetric and muographic data to determine the most precise image of the 3D density structure of the Puy de Dôme volcano (Chaîne des Puys, France) ever obtained. With rock thickness of up to 1,600 m along the muon lines of sight, it is, to our knowledge, the largest volcano ever imaged by combining muography and gravimetry. The inversion of gravimetric data is an ill-posed problem with a non-unique solution and a sensitivity rapidly decreasing with depth. Muography has the potential to constrain the absolute density of the studied structures but the use of the method is limited by the possible number of acquisition view points, by the long acquisition duration and by the noise contained in the data. To take advantage of both types of data in a joint inversion scheme, we develop a robust method adapted to the specificities of both the gravimetric and muographic data. Our method is based on a Bayesian formalism. It includes a smoothing relying on two regularization parameters (an a priori density standard deviation and an isotropic correlation length) which are automatically determined using a leave one out criterion. This smoothing overcomes artifacts linked to the data acquisition geometry of each dataset. A possible constant density offset between both datasets is also determined by least-squares. The potential of the method is shown using the Puy de Dôme volcano as case study as high quality gravimetric and muographic data are both available. Our results show that the dome is dry and permeable. Thanks to the muographic data, we better delineate a trachytic dense core surrounded by a less dense talus.

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Backward Monte-Carlo applied to muon transport

Cited by 38 publications

References 24 publications

Joint inversion methods with relative density offset correction for muon tomography and gravity data, with application to volcano imaging

Joint inversion methods with relative density offset correction for muon tomography and gravity data, with application to volcano imaging

Atmospheric muons as an imaging tool

Robust Bayesian Joint Inversion of Gravimetric and Muographic Data for the Density Imaging of the Puy de Dôme Volcano (France)

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