Passive imaging of collisional orogens: a review of a decade of geophysical studies in the Pyrénées

This contribution reviews the challenges of imaging collisional orogens, focusing on the example of the Pyrenean domain. Indeed, important progresses have been accomplished regarding our understanding of the architecture of this mountain range over the last decades, thanks to the development of innovative passive imaging techniques, relying on a more thorough exploitation of the information in seismic signals, as well as new seismic acquisitions. New tomographic images provide evidence for continental subduction of Iberian crust beneath the western and central Pyrénées, but not beneath the eastern Pyrénées. Relics of a Cretaceous hyper-extended and segmented rift are found within the North Pyrenean Zone, where the imaged crust is thinner (10–25 km). This zone of thinned crust coincides with a band of positive Bouguer anomalies that is absent in the Eastern Pyrénées. Overall, the new tomographic images provide further support to the idea that the Pyrénées result from the inversion of hyperextended segmented rift systems.

Muographic monitoring of hydrogeomorphic changes induced by post-eruptive lahars and erosion of Sakurajima volcano

Post-eruptive destabilization of volcanic edifices by gravity driven debris flows or erosion can catastrophically impact the landscapes, economies and human societies surrounding active volcanoes. In this work, we propose cosmic-ray muon imaging (muography) as a tool for the remote monitoring of hydrogeomorphic responses to volcano landscape disturbances. We conducted the muographic monitoring of Sakurajima volcano, Kyushu, Japan and measured continuous post-eruptive activity with over 30 lahars per year. The sensitive surface area of the Multi-Wire-Proportional-Chamber-based Muography Observation System was upgraded to 7.67 m$$^2$$ 2 ; this made it possible for the density of tephra within the crater region to be measured in 40 days. We observed the muon flux decrease from 10 to 40% through the different regions of the crater from September 2019 to October 2020 due to the continuous deposition of tephra fallouts. In spite of the long-term mass increase, significant mass decreases were also observed after the onsets of rain-triggered lahars that induced the erosion of sedimented tephra. The first muographic observation of these post-eruptive phenomena demonstrate that this passive imaging technique has the potential to contribute to the assessment of indirect volcanic hazards.

Muographic Monitoring of Hydrogeomorphic Changes Induced by Post-Eruptive Lahars and Erosion of Sakurajima Volcano

Post-eruptive destabilization of volcanic edifices by gravity driven debris flows or erosion can catastrophically impact the landscapes, economies and human societies surrounding active volcanoes. In this work, we propose muography as a tool for the remote monitoring of hydrogeomorphic responses to volcano landscape disturbances. We conducted the muographic monitoring of Sakurajima volcano, Kyushu, Japan and measured continuous post-eruptive activity with over 30 lahars per year. The sensitive surface area of the Multi-Wire-Proportional-Chamber-based Muography Observation System was upgraded to 7.67 m2 ; this made it possible for the density of tephra within the crater region to be measured in 40 days. We observed the muon flux decrease from 10 % to 40 % through the different regions of the crater from September 2019 to October 2020 due to the continuous deposition of tephra fallouts. In spite of the long-term mass increase, significant mass decreases were also observed after the onsets of rain-triggered lahars that induced the erosion of sedimented tephra. The first muographic observation of these post-eruptive phenomena demonstrate that this passive imaging technique has the potential to contribute to the assessment of indirect volcanic hazards.

BathyNet: A Deep Neural Network for Water Depth Mapping from Multispectral Aerial Images

Besides airborne laser bathymetry and multimedia photogrammetry, spectrally derived bathymetry provides a third optical method for deriving water depths. In this paper, we introduce BathyNet, an U-net like convolutional neural network, based on high-resolution, multispectral RGBC (red, green, blue, coastal blue) aerial images. The approach combines photogrammetric and radiometric methods: Preprocessing of the raw aerial images relies on strict ray tracing of the potentially oblique image rays, considering the intrinsic and extrinsic camera parameters. The actual depth estimation exploits the radiometric image content in a deep learning framework. 3D water surface and water bottom models derived from simultaneously captured laser bathymetry point clouds serve as reference and training data for both image preprocessing and actual depth estimation. As such, the approach highlights the benefits of jointly processing data from hybrid active and passive imaging sensors. The RGBC images and laser data of four groundwater supplied lakes around Augsburg, Germany, captured in April 2018 served as the basis for testing and validating the approach. With systematic depth biases less than 15 cm and a standard deviation of around 40 cm, the results satisfy the vertical accuracy limit Bc7 defined by the International Hydrographic Organization. Further improvements are anticipated by extending BathyNet to include a simultaneous semantic segmentation branch.