.[1] Seismic data analysis is a powerful tool for remote characterization of rock slope failures. Here we develop quantitative estimates of fundamental rockslide properties (e.g., volume) based solely on data from an existing regional seismic network. We assembled a data set of twenty known rockslides in the central Alps (with volumes between 1,000 and 2,000,000 m 3 ) and analyzed their corresponding seismograms. Common signal characteristics include emergent onsets, slowly decaying tails, and a triangular spectrogram shape. The main component of seismic energy is contained in frequencies below ∼3-4 Hz, while higher-frequency signals may be caused by block impacts. Location estimates were generated using automatic arrival time picks and resulted in a mean location error of 10.9 km. A linear relationship for the detection limit of a rockslide as a function of volume was identified for our seismic station network. To estimate rockslide volume, runout distance, drop height, potential energy, and Fahrböschung (angle of reach), we extracted five simple metrics from each seismogram: signal duration, peak value of the ground velocity envelope, velocity envelope area, risetime, and average ground velocity. Using multivariate linear regression, the combination of duration, peak envelope velocity, and envelope area best estimated event parameters, with r 2 values ranging between 0.8 and 0.88. Three new rockslides were then used to validate our method, and volume, runout, drop height, and potential energy were estimated within the correct order of magnitude. When provided with a suitable data set of rockslide events, our method can be easily adapted to other regions and seismic networks.

Cycles of glaciation impose mechanical stresses on underlying bedrock as glaciers advance, erode, and retreat. Fracture initiation and propagation constitute rock mass damage and act as preparatory factors for slope failures; however, the mechanics of paraglacial rock slope damage remain poorly characterized. Using conceptual numerical models closely based on the Aletsch Glacier region of Switzerland, we explore how in situ stress changes associated with fluctuating ice thickness can drive progressive rock mass failure preparing future slope instabilities. Our simulations reveal that glacial cycles as purely mechanical loading and unloading phenomena produce relatively limited new damage. However, ice fluctuations can increase the criticality of fractures in adjacent slopes, which may in turn increase the efficacy of fatigue processes. Bedrock erosion during glaciation promotes significant new damage during first deglaciation. An already weakened rock slope is more susceptible to damage from glacier loading and unloading and may fail completely. We find that damage kinematics are controlled by discontinuity geometry and the relative position of the glacier; ice advance and retreat both generate damage. We correlate model results with mapped landslides around the Great Aletsch Glacier. Our result that most damage occurs during first deglaciation agrees with the relative age of the majority of identified landslides. The kinematics and dimensions of a slope failure produced in our models are also in good agreement with characteristics of instabilities observed in the field. Our results extend simplified assumptions of glacial debuttressing, demonstrating in detail how cycles of ice loading, erosion, and unloading drive paraglacial rock slope damage.

Mapping landslides using automated methods is a challenging task, which is still largely done using human efforts. Today, the availability of high-resolution EO data products is increasing exponentially, and one of the targets is to exploit this data source for the rapid generation of landslide inventory. Conventional methods like pixel-based and object-based machine learning strategies have been studied extensively in the last decade. In addition, recent advances in CNN (convolutional neural network), a type of deep-learning method, has been widely successful in extracting information from images and have outperformed other conventional learning methods. In the last few years, there have been only a few attempts to adapt CNN for landslide mapping. In this study, we introduce a modified U-Net model for semantic segmentation of landslides at a regional scale from EO data using ResNet34 blocks for feature extraction. We also compare this with conventional pixel-based and object-based methods. The experiment was done in Douglas County, a study area selected in the south of Portland in Oregon, USA, and landslide inventory extracted from SLIDO (Statewide Landslide Information Database of Oregon) was considered as the ground truth. Landslide mapping is an imbalanced learning problem with very limited availability of training data. Our network was trained on a combination of focal Tversky loss and cross-entropy loss functions using augmented image tiles sampled from a selected training area. The deep-learning method was observed to have a better performance than the conventional methods with an MCC (Matthews correlation coefficient) score of 0.495 and a POD (probability of detection) rate of 0.72 .

Abstract. In this contribution, we present a review of scientific research results that address seismo-hydromechanically coupled processes relevant for the development of a sustainable heat exchanger in low-permeability crystalline rock and introduce the design of the In situ Stimulation and Circulation (ISC) experiment at the Grimsel Test Site dedicated to studying such processes under controlled conditions. The review shows that research on reservoir stimulation for deep geothermal energy exploitation has been largely based on laboratory observations, large-scale projects and numerical models. Observations of full-scale reservoir stimulations have yielded important results. However, the limited access to the reservoir and limitations in the control on the experimental conditions during deep reservoir stimulations is insufficient to resolve the details of the hydromechanical processes that would enhance process understanding in a way that aids future stimulation design. Small-scale laboratory experiments provide fundamental insights into various processes relevant for enhanced geothermal energy, but suffer from (1) difficulties and uncertainties in upscaling the results to the field scale and (2) relatively homogeneous material and stress conditions that lead to an oversimplistic fracture flow and/or hydraulic fracture propagation behavior that is not representative of a heterogeneous reservoir. Thus, there is a need for intermediate-scale hydraulic stimulation experiments with high experimental control that bridge the various scales and for which access to the target rock mass with a comprehensive monitoring system is possible. The ISC experiment is designed to address open research questions in a naturally fractured and faulted crystalline rock mass at the Grimsel Test Site (Switzerland). Two hydraulic injection phases were executed to enhance the permeability of the rock mass. During the injection phases the rock mass deformation across fractures and within intact rock, the pore pressure distribution and propagation, and the microseismic response were monitored at a high spatial and temporal resolution.

High-resolution 3D geological models are crucial for underground development projects and corresponding numerical simulations with applications in e.g., tunneling, hydrocarbon exploration, geothermal exploitation and mining. Most geological models are based on sparse geological data sampled pointwise or along lines (e.g., boreholes), leading to oversimplified model geometries. In the framework of a hydraulic stimulation experiment in crystalline rock at the Grimsel Test Site, we collected geological data in 15 boreholes using a variety of methods to characterize a decameter-scale rock volume. The experiment aims to identify and understand relevant thermo-hydro-mechanical-seismic coupled rock mass responses during high-pressure fluid injections. Prior to fluid injections, we characterized the rock mass using geological, hydraulic and geophysical prospecting. The combination of methods allowed for compilation of a deterministic 3D geological analog that includes five shear zones, fracture density information and fracture locations. The model may serve as a decameter-scale analog of crystalline basement rocks, which are often targeted for enhanced geothermal systems. In this contribution, we summarize the geological data and the resulting geological interpretation.

[1] Bedrock stresses induced through exhumation and tectonic processes play a key role in the topographic evolution of alpine valleys. Using a finite difference model combining the effects of tectonics, erosion, and long-term bedrock strength, we assess the development of near-surface in situ stresses and predict bedrock behavior in response to glacial erosion in an Alpine Valley (the Matter Valley, southern Switzerland). Initial stresses are derived from the regional tectonic history, which is characterized by ongoing transtensional or extensional strain throughout exhumation of the brittle crust. We find that bedrock stresses beneath glacial ice in an initial V-shaped topography are sufficient to induce localized extensional fracturing in a zone extending laterally 600 m from the valley axis. The limit of this zone is reflected in the landscape today by a valley "shoulder," separating linear upper mountain slopes from the deep U-shaped inner valley. We propose that this extensional fracture development enhanced glacial quarrying between the valley shoulder and axis and identify a positive feedback where enhanced quarrying promoted valley incision, which in turn increased in situ stress concentrations near the valley floor, assisting erosion and further driving rapid U-shaped valley development. During interglacial periods, these stresses were relieved through brittle strain or topographic modification, and without significant erosion to reach more highly stressed bedrock, subsequent glaciation caused a reduction in differential stress and suppressed extensional fracturing. A combination of stress relief during interglacial periods, and increased ice accumulation rates in highly incised valleys, will reduce the likelihood of repeat enhanced erosion events.

Crustal stresses beneath evolving alpine landscapes result from a combination of tectonic strain, bedrock exhumation, and topography. The stress field is regulated by elastic material properties and the brittle strength of critically stressed elements, particularly those within preexisting faults and intact rock. Combining Byerlee's law for crustal stresses with a recently developed trilinear fracture envelope, we propose an extension of the critically stressed crust concept to constrain in situ stresses through microcrack generation and extensional fracture propagation. A compilation of 814 global in situ stress measurements suggests microcrack development will limit long‐term rock strength, and maximum differential stresses in the upper ~1 km of the crust can be controlled by cohesive bedrock behavior. Using an elastoplastic, 2‐D finite‐difference model, we approximate in situ stress development within landscapes undergoing high rates of exhumation in both normal and reverse tectonic regimes. Critical near‐surface stresses in these environments are defined by the microcrack initiation threshold, estimated to be roughly one third of the unconfined compressive strength of intact rock, while stresses deeper in the crust adhere to Byerlee's law. Our models indicate that exhumation‐induced stresses limited by long‐term rock strength are the primary contributor to the near‐surface stress field, while topographic relief reduces stress near valley axes. Simulating glacial erosion then allows us to illustrate a path‐dependent relationship between critical stress development, fracture formation, and geomorphic processes. We find that glacial unloading can generate new microcracks in near‐surface bedrock, resulting in unstable macroscopic extensional (or “exfoliation”) fracture propagation during incision of U‐shaped alpine valleys.

Past exploration missions have revealed that the lunar topography is eroded through mass wasting processes such as rockfalls and other types of landslides, similar to Earth. We have analyzed an archive of more than 2 million high-resolution images using an AI and big datadriven approach and created the first global map of 136.610 lunar rockfall events. Using this map, we show that mass wasting is primarily driven by impacts and impact-induced fracture networks. We further identify a large number of currently unknown rockfall clusters, potentially revealing regions of recent seismic activity. Our observations show that the oldest, pre-Nectarian topography still hosts rockfalls, indicating that its erosion has been active throughout the late Copernican age and likely continues today. Our findings have important implications for the estimation of the Moon's erosional state and other airless bodies as well as for the understanding of the topographic evolution of planetary surfaces in general.