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Item type:Publication, Inferring Zonal Wind Profiles in the Equatorial Electrojet From Coherent Scatter(2023-01-01)Zonal wind estimates in the equatorial electrojet derived from coherent scatter echoes, specular meteor trail echoes, and optical limb scans are compared.While the three techniques exhibit broad overall agreement, significant differences in the results of the three techniques appear.The differences can be attributed in large part to horizontal inhomogeneity in the winds and the dissimilar averaging kernels of the three techniques. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Automated Bedform Identification: A Meta-Analysis of Current Methods and the Heterogeneity of Their Outputs(2023-12-26)Ongoing efforts to characterize underwater dunes have led to a considerable number of freely available tools that identify these bedforms in a (semi-)automated way. However, these tools differ with regard to their research focus and appear to produce results that are far from unequivocal. We scrutinize this assumption by comparing the results of five recently published dune identification tools in a comprehensive meta-analysis. Specifically, we analyse dune populations identified in three bathymetries under diverse flow conditions and compare the resulting dune characteristics in a quantitative manner. Besides the impact of underlying definitions, it is shown that the main heterogeneity arises from the consideration of a secondary dune scale, which has a significant influence on statistical distributions. Based on the quantitative results, we discuss the individual strengths and limitations of each algorithm, with the aim of outlining adequate fields of application. Yet, the concerted bedform analysis and subsequent combination of results have another benefit: the creation of a benchmarking data set which is inherently less biased by individual focus and therefore a valuable instrument for future validations. Nevertheless, it is apparent that the available tools are still very specific and that end-users would profit by their merging into a universal and modular toolbox. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Magma Storage Below Sabancaya Volcano (Southern Peru) Imaged by Broadband Magnetotellurics(Wiley, 2026-02-01)Sabancaya volcano is one of the most active volcanoes in the Central Andes. Its ongoing eruptive process is accompanied by large‐scale deformation, with activation of the Huambo‐Cabanaconde fault system, marked by intense seismicity over an area of about 50 × 30 . We present a pilot magnetotelluric survey performed in 2022, covering the Ampato‐Sabancaya complex, Hualca‐Hualca volcano, as well as the related system of normal faults. Our three‐dimensional electrical resistivity model reveals pronounced vertical gradients and lateral contrasts at elevations above sea level, along with generally low resistivity values at depth. Seismicity at depths km below sea level predominantly occurs in a low resistivity environment: 90% of seismic events occur at resistivity values below 10 m. Two prominent electrical conductors (<0.5 and 2–4 m) are imaged at depths 11–18 km and 3–8 km, respectively. Using petrological constraints, we interpret them as the signature of the magmatic plumbing system, connecting the Hualca‐Hualca and Ampato‐Sabancaya volcanoes. The deeper conductor is inferred to represent a magma reservoir situated beneath the older Hualca‐Hualca volcano, consistent with long‐term deformation and seismicity. It is connected to the laterally offset shallow magma chamber below Sabancaya. At depth 2–10 km, a strong conductor (<0.1 m) is imaged in the Huambo‐Cabanaconde fault zone. The extremely high conductivity of this body is attributed to the abundance of ultra‐saline brines, originating from the deep magma reservoir below. We speculate that the strong seismicity cluster detected in 2013 facilitated the passage of magmatic fluids exsolved from the magma reservoir, and replenished this ultra‐conductive body.2 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, AI-based geological subsurface reconstruction using sparse convolutional autoencoders(Elsevier BV, 2025-10-01)Subsurface reconstruction is critical for geological modeling and resource exploration. Conventional spatial interpolation methods are limited by stationarity and spatial isotropy assumptions, while advanced geostatistical techniques require specialized datasets. Deep learning approaches often need large datasets, which is impractical for geoscientific applications. This study presents an AI-based methodology using a sparse convolutional autoencoder for robust subsurface modeling under data constraints and integrating secondary data sources such as Vertical Electrical Sounding (VES) data. A four-stage testing framework was implemented: (1) emulating conventional interpolation for baseline performance; (2) reconstructing subsurface geometries from synthetic data; (3) incorporating geophysical constraints through VES forward modeling; and (4) validating the methodology using a real-world case study from the Huancayo tectonic basin in the Peruvian Andes, using 41 VES measurements across two cross-sections (12 and 14 km long). Results demonstrate that the proposed model effectively emulates kriging interpolation (mean squared error: 1.5 × 10−3 to 1.2 × 10−3 with 100–800 training examples) through transfer learning from an inverse-distance, pre-trained model. In subsurface reconstruction, the model outperforms kriging (37.4–61.7 % improvement across 1–15 % sampling densities) through its ability to adapt to non-stationary conditions. When incorporating synthetic VES data, the model effectively reconstructed subsurface geometries with error reduction from 4.1 × 10−1 to 9.1 × 10−3 as stations increased from 1 to 40, demonstrating diminishing returns beyond this point. Application to the Huancayo basin case study validated the model's practical applicability by successfully identifying previously unmapped features including the contact between basement and sedimentary infill, folds and faults. The methodology demonstrates the AI's capability to enhance geological understanding in complex tectonic settings, revealing subtle features and refining existing assumptions about subsurface architecture.2