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Muography is increasingly used to image the density distribution of volcanic edifices, complementing traditional geophysical tomographies. Here, we present a new muon data processing algorithm, and apply it to a new generation of scintillator-based muon detectors, to image the relative density distribution in La Soufrière de Guadeloupe volcano (Lesser Antilles, France). Our processing method iteratively searches for the best fit of each muon trajectory, accounting for all the hits registered by the detector related to the particular muon event. We test the performance of our algorithm numerically, simulating the interaction of muons with our detector and accounting for its exact assemblage including the scintillator bars and lead shielding. We find that our new data processing mitigates the impact of spurious signals coming from secondary particles, and improves the amount of successfully reconstructed events. The resulting 2-D muon images at La Soufrière have higher angular resolution than previous ones and capture the heterogeneous structure of the dome. They show density anomalies located on the summit southern region, which includes a boiling acid lake and degassing fractures, where the rock is the most porous and fumarolic activity is ongoing. This work shows the importance of combining numerical simulations of muon propagation with precise raw data processing to obtain high-quality results. It is also a first step towards fully assessing the noise contamination sources when performing muon tomography, and their correction, prior to geophysical interpretations. [ABSTRACT FROM AUTHOR]

In the past years, large particle physics experiments have shown that muon rate variations detected in underground laboratories are sensitive to regional, middle‐atmosphere temperature variations. Potential applications include tracking short‐term atmosphere dynamics, such as Sudden Stratospheric Warmings. We report here that such sensitivity is not only limited to large surface detectors under high‐opacity conditions. We use a portable muon detector conceived for muon tomography for geophysical applications, and we study muon rate variations observed over 1 year of measurements at the Mont Terri Underground Rock Laboratory, Switzerland (opacity of ~700 meter water equivalent). We observe a direct correlation between middle‐atmosphere seasonal temperature variations and muon rate. Muon rate variations are also sensitive to the abnormal atmosphere heating in January–February 2017, associated to a Sudden Stratospheric Warming. Estimates of the effective temperature coefficient for our particular case agree with theoretical models and with those calculated from large neutrino experiments under comparable conditions. Thus, portable muon detectors may be useful to (1) study seasonal and short‐term middle‐atmosphere dynamics, especially in locations where data are lacking such as midlatitudes, and (2) improve the calibration of the effective temperature coefficient for different opacity conditions. Furthermore, we highlight the importance of assessing the impact of temperature on muon rate variations when considering geophysical applications. Depending on latitude and opacity conditions, this effect may be large enough to hide subsurface density variations due to changes in groundwater content and should therefore be removed from the time series. Plain Language Summary: Temperature variations in high altitudes of the atmosphere remain to be fully understood. During winter, sudden heating of this region can perturb its normal dynamics and result in extremely low temperatures at the surface of the Earth. We found that tracking the amount of muons at the Earth's surface may be a key tool to study these variations. Muons are elementary particles produced by the interaction of cosmic rays with the atmosphere. The flux of muons arriving at the Earth's surface is about 1 muon per minute per square centimeter. In fact, this flux depends on the atmospheric temperature distribution. Up until now only large muon detectors, developed for experiments under the most advantageous conditions, had measured changes in the flux of muons due to sudden heating of the high atmosphere. Here we show that these phenomena can also be observed by smaller, portable detectors built for geoscience applications. The technology to study sudden temperature variations in the atmosphere is expensive and only able to sample punctual points in the sky. Through an improved data coverage, the muon technique may bridge the gap towards a better understanding of the thermal state of the atmosphere and its dynamics. Key Points: We report muon rate variations associated to temperature changes in the middle atmosphere observed with a portable muon detectorThe effect is significant both for seasonal and short‐term temperature variations, even under low‐opacity conditions at midlatitudesWe highlight potential applications on atmosphere dynamics and the need to account for these phenomena in geophysical applications [ABSTRACT FROM AUTHOR]

Surface-based monitoring of mass transfer caused by injections and extractions in deep boreholes is crucial to maximize oil, gas and geothermal production. Inductive electromagnetic methods, such as magnetotellurics, are appealing for these applications due to their large penetration depths and sensitivity to changes in fluid conductivity and fracture connectivity. In this work, we propose a 3-D Markov chain Monte Carlo inversion of time-lapse magnetotelluric data to image mass transfer following a saline fluid injection. The inversion estimates the posterior probability density function of the resulting plume, and thereby quantifies model uncertainty. To decrease computation times, we base the parametrization on a reduced Legendre moment decomposition of the plume. A synthetic test shows that our methodology is effective when the electrical resistivity structure prior to the injection is well known. The centre of mass and spread of the plume are well retrieved. We then apply our inversion strategy to an injection experiment in an enhanced geothermal system at Paralana, South Australia, and compare it to a 3-D deterministic time-lapse inversion. The latter retrieves resistivity changes that are more shallow than the actual injection interval, whereas the probabilistic inversion retrieves plumes that are located at the correct depths and oriented in a preferential north-south direction. To explain the time-lapse data, the inversion requires unrealistically large resistivity changes with respect to the base model. We suggest that this is partly explained by unaccounted subsurface heterogeneities in the base model from which time-lapse changes are inferred. [ABSTRACT FROM AUTHOR]