ALBERT

Description: Data and information are central to policy processes, as they frame the policy problem, the design and the implementation of policy, and evaluation of policy impacts. Better data and information infrastructure is expected to lead to better policies and outcomes, for example, by enabling transparent decision making and enhancing capacity and accountability. However, the collection, selection, representation, framing and application of data are not merely technical and apolitical procedures, but are dependent on the interests represented in the policy processes they aim to inform. Social scientists have pointed to the “politics of numbers” and their effects on forests and trees and on the people relying on them, as well as on those involved in their measurements. We use the case of the Reducing Emissions from Deforestation and Forest Degradation (REDD+) international initiative and focus on the central aspect of understanding drivers of deforestation and measures of REDD+ performance to unpack the politics of policy processes. Data and information are socially constructed, and their interpretations are shaped by the contexts in which they emerge. Dominant beliefs in the transformative power of new data and technologies cannot explain why, often, new information does not translate into policy change and action to halt deforestation. Technological advances in making new and ever larger amounts of data available for analysis are a necessary yet insufficient condition for changing the business as usual in deforestation. Through openness, reflexivity and the tackling of silences in data and information related to the global political economy of deforestation the scientific community can make a key contribution to more equitable policy change.

Description: The Mufushan area, which has abundant rare-metal pegmatites within and around the Mufushan Granite Complex, has become a major target for Ta-Nb-(Li-Be) exploration in South China. The age and origin of the pegmatites and associated rare-metal mineralization are still under debate. Here, we report the in situ U-Pb ages and geochemical characteristics of granites and pegmatites in the Guanyuan and Duanfengshan districts, which are located in the central and northern parts of the Mufushan Complex. Combined zircon, apatite and monazite U-(Th)-Pb dating revealed that biotite, two-mica, and muscovite granites from the Guanyuan and Duanfengshan districts were emplaced at 143–139 Ma, which overlaps with the U-Pb ages of columbite-group minerals (CGM) from different internal zones of the Duanfengshan pegmatites (142–140 Ma). Whole-rock major and trace element compositions and Sr-Nd-Hf isotope data reveal that the granites and pegmatites experienced continuous evolution from biotite, two-mica, and muscovite granites to pegmatite and that the magma originated from the partial melting of mica schists that are abundant in the Mufushan area. Temporal, chemical and mineralogical evidence indicates a genetic link between muscovite granite and Ta-Nb pegmatites. The textures and chemical compositions of CGM from different pegmatites exhibit features typical of magmatic CGM, indicating that fractional crystallization was the driving force that promoted Ta-Nb enrichment. The increasing alumina satu- ration index [ASI: molar Al/(Ca–1.67P + Na + K)] of pegmatitic melt due to albite crystallization may have been the main factor controlling CGM deposition, explaining why major Ta-Nb mineralization is bound to albite pegmatites. The Duanfengshan and other rare-metal pegmatites in the Mufushan area are derivatives of the most evolved granitic facies (i.e., muscovite granite) of the Mufushan Complex. The Duanfengshan and Renli pegmatite fields indicate that the Early Cretaceous (~140 Ma) may have been an important, underappreciated epoch for the formation of pegmatite-related rare-metal resources in the Mufushan area and beyond in South China.

Description: The St1 Deep Heat Project was started in 2014 by the Finnish energy companies St1 and Fortum. This site is at Fortum's district heating plant at the Aalto University campus, west of Helsinki. The project began with the drilling of a cored 2015 m pilot hole, which encountered a few meters of alluvium over the expected crystalline basement. Follow-on ~6000 m deviated injection-andproduction wells were completed in 2018 and 2020. These wells were extensively logged, and the deep wells were stimulated after completion. In Oct 2018 the ~2500 m to ~5000 m vertical portion of the injection well was profiled with the GFZ German Geoscience Center 17 level, 10 m spaced Sercel borehole geophone array (a Vertical Seismic Profile - VSP). Near-surface shots at 4 offset and 1 near-well shot-points were used as sources. These data were analyzed and compared to (a) drilling data, (b) logging data, (c) surface geology, and (d) a short run of Seismic While Drilling (SWD) data recorded in the pilot hole using hammer-drill signals from the production hole. The VSP data establishes that a seismic velocity reversal - from a P-velocity of ~6.5 to ~6.1 km/s - extends from ~3000 m down at least to the bottom VSP position at 5000 m and is also seen in the well logs. Aside from several shallower structures, the most significant reflection feature found in these data is a ~400 m thick horizon that intersects the ~6000 m wells at ~5000 m. This horizon includes internal reflections that appear to correlate with a drilling-encountered and loginterpreted fracture zone. Owing to complex surrounding velocity structure, this feature's lateral continuation and ultimate attitude have been difficult to resolve. In one interpretation it appears as a 45 ENE dipping extension of a shallower reflector seen in the SWD data. The productionwell's trajectory was based on this interpretation - which drilling seems to confirm. Its consequences for the EGS project will be tested with a circulation campaign over the next months.

Description: Geomechanics play an important role in any underground activity, such as carbon dioxide (CO2) and hydrogen (H2) geo-storage, owing to the considerable hazards linked to the injection and withdrawal of fluids into and from the subsurface. In order to quantify these risks, knowledge of full stress tensor is required. Yet, most of our stress information in the Australian target basins for geo-storage is limited to the stress orientations, while stress magnitude data is sparse. 3D geomechanical modelling has proved to be an invaluable tool for prediction of full stress tensor. Nevertheless, a model requires some stress magnitude data in order to tune the model to be representative of real stress state. In situations where stress magnitude data is lacking, this means that the model is susceptible to significant uncertainties. Herein, we present a novel strategy for stress modelling, which involves the utilisation of indirect data such as borehole breakouts, drilling-induced fractures, seismic activity records, and formation integrity tests to calibrate a 3D geomechanical model. We employ the northern Bowen Basin, an onshore basin in Queensland, Australia, as a case study for a comprehensive 3D geomechanical modelling approach. We assess all the indirect information in the model’s volume to narrow down the model predictions and find the most reliable stress state. This innovative approach is an important step forward in stress modelling of Australian basins, where lack of stress magnitudes is a great challenge for geomechanical assessment of geo-storage.

Description: The ionosphere is an ionized part of the upper atmosphere, where the number of free electrons is large enough to affect the propagation of radio signals, including those of the GNSS systems. The knowledge of electron density values in the ionosphere is crucial for both industrial and scientific applications. Here, we develop a novel empirical model of electron density in the topside ionosphere using the radio occultation profiles collected by the CHAMP, GRACE, and COSMIC missions. We assume a linear decay of scale height with altitude and model four parameters, namely the F2-peak density and height (NmF2 and hmF2) and the slope and intercept of the linear scale height decay (dHs/dh and HO). The resulting model (NET) is based on feedforward neural networks. The model inputs include the the geographic and geomagnetic position, the solar flux and geomagnetic indices. The resulting density reconstructions are validated on more than a hundred million in-situ measurements from CHAMP, CNOFS and Swarm satellites, as well as on the GRACE/KBR data, and the developed NET model is compared to several topside options of the International Reference Ionosphere (IRI) model. The NET model yields highly accurate reconstructions of the topside ionosphere and gives unbiased predictions for different locations, seasons, and solar activity conditions.

Description: Continental subduction is the major cause of regional heterogeneities in the lithospheric mantle and contrasting types of magmatism and mineralization in post-collisional settings. We illustrate the relation between the nature of the subducted crust and the character of magmatism for the Late Miocene New Guinea Orogen that formed by the collision of the Australian continental margin with an island arc. The bipartite nature of the subducted Australian plate margin, with Precambrian crust in the west and Phanerozoic accreted arcs in the east, is reflected in the contrasting magmatism along the strike of the New Guinea Orogen. The chemical signature of the subducted crust is particularly prominent in small-volume Late Miocene–Quaternary ultrapotassic rocks of New Guinea. In the west, ultrapotassic lavas have low εNd values (−12.6 to −20.9), indicating the recycling of ancient continental material. Conversely, high εNd values of +3.5 to +4.5 are found in ultrapotassic lavas from eastern New Guinea. This suggests recycling of juvenile continental material, similar to the orthogneisses exposed in the Late Miocene ultrahigh-pressure metamorphic complex of the D'Entrecasteaux Islands. By comparison with ultrapotassic rocks from other orogenic belts, we show that crustal recycling is responsible for regionally contrasting redox conditions in the lithospheric mantle, which may explain why porphyry-type deposits are important in some regions but absent in others.

Description: Teleseismic back-projection imaging has emerged as a powerful tool for understanding the rupture propagation of large earthquakes. However, its application often suffers from artifacts related to the receiver array geometry. We developed a teleseismic back-projection technique that can accommodate data from multiple arrays. Combined processing of P and pP waveforms may further improve the resolution. The method is suitable for defining arrays ad-hoc to achieve a good azimuthal distribution for most earthquakes. We present a catalog of short-period rupture histories (0.5-2.0 Hz) for all earthquakes from 2010 to 2022 with Mw {greater than or equal to} 7.5 and depth less than 200 km (56 events). The method provides automatic estimates of rupture length, directivity, speed, and aspect ratio, a proxy for rupture complexity. We obtained short-period rupture length scaling relations that are in good agreement with previously published relations based on estimates of total slip. Rupture speeds were consistently in the sub-Rayleigh regime for thrust and normal earthquakes, whereas a tenth of strike-slip events propagated at supershear speeds. Many rupture histories exhibited complex behaviors, e.g., rupture on conjugate faults, bilateral propagation, and dynamic triggering by a P wave. For megathrust earthquakes, ruptures encircling asperities were frequently observed, with down-dip, up-dip, and balanced patterns. Although there is a preference for short-period emissions to emanate from central and down-dip parts of the megathrust, emissions up-dip of the main asperity are more frequent than suggested by earlier results. The data are presented as follows (and described in detail in the associated README): SUPPORTING DATA SET S1 (2024-001_Vera-et-al_Supporting-Data-S1.zip) This Data Set (S1) consists of *.bp files containing (1) short-period earthquake rupture patterns, (2) energy radiated maps, and (3) source time functions derived from back-projections (0.5-2.0 Hz). The Data Set S1 includes 56 folders, representing 56 processed earthquakes between 2010 and 2022 with a moment magnitude (Mw) greater than or equal to 7.5 and a depth less than 200 km. These folders are labeled in the format YYYYMMDDhhmm_EVENT_NAME_REGION (UTC) in *.bp format. SUPPORTING DATA SET S2 (2024-001_Vera-et-al_Supporting-Data-S2.csv) This Data Set (S2) comprises a *.csv file containing earthquake source information used in the back-projection and the resulting ruptur...

Description: Landslides are a major type of natural hazard that cause significant human and economic losses in mountainous regions worldwide. Optical and synthetic aperture radar (SAR) satellite data are increasingly being used to support landslide investigation due to their multi-spectral and textural characteristics, multi-temporal revisit rates, and large area coverage. Understanding landslide occurrence, kinematics and correlation to external triggering factors is essential for landslide hazard assessment. Landslides are usually triggered by rainfall and thus, are often covered by clouds, which limits the use of optical images only. Exploiting SAR data, and their cloud penetration and all weather measurement capability, provides more precise temporal characterization of landslide kinematics and its occurrence. However, except for a few research studies, the full potential of SAR data for operational landslide analysis are not fully exploited yet. This is a very demanding task, considering the availability of a vast amount of Sentinel-1 data that have been globally available since October 2014.In this presentation we summarise all the achievements that were made within the framework of MultiSat4SLOWS project (Multi-Satellite imaging for Space-based Landslide Occurrence and Warning Service), financed within the Helmholtz Imaging 2020 call. The project aims on developing a multi-sensor approach for detection and analysis of the landslide occurrence time and its spatial extent using freely available SAR data from Sentinel-1. Within this project, we generated a reference database based on Sentinel-1 and -2 data for training, testing and validation of deep learning algorithms. The reference database contains various landslide examples that occurred worldwide and include pre- and post-event polarimetric, coherence and backscatter features. Also, we investigated the applicability of SAR/InSAR time-series data for landslide time detection. Finally, we introduce a prototype of a Visual Analytics platform for rapid landslide analysis of spatial and temporal ground deformation patterns and correlation with external triggering factors.

Description: In geosciences, machine learning (ML) has become essential for solving complex problems, such as predicting natural disasters or analysing the impact of extreme temperatures on mortality rates. However, the integration of ML into geoscience scenarios faces significant challenges, especially in explaining the influence of hyperparameters (HP) on model performance and model behaviour in specific scenarios. The Explainable Artificial Intelligence (XAI) system ClarifAI developed at GFZ addresses these challenges by combining XAI concepts with interactive visualisation. ClarifAI currently provides users with two interactive XAI methods: HyperParameter Explorer (HPExplorer) and Hypothetical Scenario Explorer (HSExplorer). HPExplorer allows interactive exploration of the HP space by computing an interactive tour through stable regions of the HP space. We define a stable region in HP space as a subspace of HP space in which ML models show similar model performance. We also employ HP importance analysis to deepen the understanding of the impact of separate HPs on model performance.The Hypothetical Scenarios Explorer (HSExplorer) helps users explore model behaviour by allowing them to test how changes in input data affect the model's response. In our presentation, we will demonstrate how HSExplorer helps users understand the impact of individual HPs on model performance. As ClarifAI is an important research area in our lab, we are interested in discussing relevant XAI challenges with the XAI community in ESSI. Our goal is to create a comprehensive set of tools that explain the mechanics of ML models and allow practitioners to apply ML to a wide range of geoscience applications.