ALBERT

Description: Landslide is a geological disaster with extremely destructive effects, resulting in huge casualties and economic losses in China. The Loess Plateau is widely covered by several to tens of meters of loess, and the underlying bedrock with good water barrier properties. Due to the frequent rains, the soil body is easy to flow or slide along weak structural surfaces. There are lots of typical loess landslides in the Loess Plateau and they seriously affect the lives of residents, so studying landslide deformation in Loess Plateau is of great significance for both society and geological expert. Interferometric Synthetic aperture radar (InSAR), with the advantages of wide monitoring range, high density, high accuracy, and not affected by weather conditions, has become the most effective technical means for regional surface deformation monitoring and landslide identification. In this paper we perform landslide deformation survey based on the small baseline Subset InSAR (SBAS-InSAR) method in Tianshui, which is located on the Loess Plateau, using 594 interferograms from the Sentinel-1 satellite ranging from January, 2017 to December, 2022. SBAS-InSAR time series analysis connects independent SAR images based on certain spatial baseline and time baseline thresholds, and finally gets time series and velocity of the Loess Plateau. The locations of landslides from National Geological Disaster Survey Database provided by China Geological Survey are compared with our results to verify the applicability of SBAS-InSAR technology in the Loess Plateau. The comparison results show that SBAS-InSAR technique using sentinel-1 dataset can effectively identify landslides in most areas except for the areas covered by forest. The results of velocity map and landslide maps can be used for landslide identify and assessment in the Loess Plateau.

Description: The Turkey heat flow database includes several research articles obtained from the catalogue of The Global Heat Flow Data Assessment Project conducted by the International Heat Flow Commission (IHFC; www.ihfc-iugg.org). The presented database contains 725 heat-flow determinations compiled from 9 different publications generated between 1991-2023 reported within Turkey. For the reporting and sorting of the database, the structure documented by Fuchs et al. (2023) is followed. Within this dataset, 98% of the entries represent continental heat-flow data (onshore), while the remaining 2% correspond to marine data (offshore). 88% of the reported heat flow values were obtained via direct temperature measurements, while the remaining data (12%) were estimated from indirect Curie depth temperature calculations.

Description: Multi-temporal interferometric synthetic aperture radar (MT-InSAR) is a widely used technique for monitoring subtle ground instabilities, with a precision ranging from centimeters to millimeters. Traditional MT-InSAR analysis often employs low-pass temporal filtering to suppress stochastic noise and extract deformation features from measurements. However, these approaches may hinder the detection and estimation of transient slope instabilities triggered by external factors. In this study, we propose a methodology for characterizing transient deformation of reservoir bank slopes under the coupling effects of rainfall and reservoir water level (RWL) changes. Following MT-InSAR analysis, slope kinematics is analyzed using time series decomposition and independent component analysis (ICA) to separate trends from seasonality. Although triggers of slope instability exhibit similar periods of increase and decrease, they are not entirely synchronized. Therefore, the seasonal components of surface deformation along with environmental triggers are analyzed using a wavelet transform to determine the time-lag between them. This helps define an effective priori search windows for constraining transient behaviors in landslide kinematics. A constrained least-squares optimization is finally applied to extract step-like kinematics features. Our methodology’s effectiveness is evaluated using a dataset comprising 102 TerraSAR-X (TSX) images in High-resolution Spotlight (HS) mode, 197 Sentinel-1 images, 10 ALOS-1 and 12 ALOS-2 Stripmap images, collected from 2006 to 2022 across Badong County, located along the Yangtze River. After the successful validation against in-situ measurements and comparison with conventional post-processing strategies, we apply our method to generate a map of hazardous sliding areas and evaluate the regional-scale slope instability of slow-moving landslides across Badong County in the Three Gorges Area (TGA).

Description: In diesem Bericht werden die durch das GFZ Potsdam am 29. und 30. November 2023 durchgeführte bohrlochgeophysikalische Messungen in den Bohrungen Gt Khn 1/88 und Gt Khn 2/87 in Karlshagen (Mecklenburg-Vorpommern) dokumen-tiert. Die Messungen wurden mit dem Ziel der Gewinnung hochaufgelöster und un-gestörter Temperatur-Tiefen-Profile durchgeführt. Die Stillstandszeiten seit Erstel-lung liegen bei mehreren Jahrzehnten; jene seit letzter Befahrung bei fünfzehn Jahren, weshalb von ungestörten Gebirgstemperaturen ausgegangen werden kann. In der Bohrung Gt Khn 2/87 wurde bei 1786,5 m Teufe eine Temperatur von 57,8 °C, welches einem mittleren Temperaturgradienten von 27,8 °C/km entspricht, ge-messen. Die Bohrung Gt Khn 1/88 konnte bis zu einer Teufe von 325,1 m befahren werden, die gemessene Temperatur betrug 16,2 °C, der entsprechende mittlere ge-othermische Gradient beträgt ca. 23,6 °C/km. This report documents the borehole geophysical logging performed by GFZ Potsdam in the Gt Khn 1/88 and Gt Khn 2/87 boreholes in Karlshagen (Mecklenburg-Western Pomerania) on the 29th and 30th of November 2023. The measurements were conducted to achieve high-resolution and undisturbed temperature-depth pro-files. The shut-in times since the boreholes were drilled are several decades; the shut-in time since last activities in the boreholes are in the order of 15 years. There-fore, undisturbed formation temperatures can be expected in the boreholes. In the Gt Khn 2/87 borehole, a temperature of 57.8 °C was measured at a depth of 1786.5 m, which corresponds to an average temperature gradient of 27.8 °C/km. The Gt Khn 1/88 borehole could be logged to a depth of 325.1 m and the measured temperature at this depth was 16.2 °C, corresponding to an average geothermal gradient of approx. 23.6 °C/km.

Description: The data publication contains the compilation of global heat-flow data by the International Heat Flow Commission (IHFC; www.ihfc-iugg.org) of the International Association of Seismology and Physics of the Earth's Interior (IASPEI). The presented data update release 2024 contains data generated between 1939 and 2024 and constitutes the second intermediate update benefiting from the global collaborative assessment and quality control of the Global Heat Flow Database running since May 2021 (http://assessment.ihfc-iugg.org). The data release comprises new original heat-flow data published since April 2023 (the update 2023). It contains 91,182 heat-flow data from 1,586 publications. 57% of the reported heat-flow values are from the continental domain (n ~ 54,553), while the remaining 43% are located in the oceanic domain (n ~ 36,692).

Description: Characterizing and predicting reservoir behavior pose significant challenges in geothermal reservoir engineering. For sustainable geothermal field management, the potential occurrence of thermal breakthroughs in producers during the injection of cold water necessitates a profound understanding of how production is influenced by the injection philosophy. The fractured nature of geothermal reservoirs adds complexity and nonlinearity to the relationship between production and injection wells. In this study, we explore alternative models to simulate reservoir behavior as substitutes for full reservoir simulations. Utilizing deep learning algorithms, we investigate two different architectures, namely the standard Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). These models map injection flow rates at the injectors to tracer concentration data at the producers through layers, nodes, and activation functions. Training these models on a synthetic geothermal reservoir, Notably, the GRU architecture consistently demonstrates superior predictive capabilities across various scenarios for all three producers. Specifically, in configurations involving a target feed, the GRU model yields higher R2 values compared to its LSTM counterpart, indicating its effectiveness in capturing inter-well relationships. Furthermore, when considering all producers collectively, the GRU model exhibits a trend of lower test errors and higher R2 values, reaffirming its proficiency in modeling complex reservoir dynamics. These findings underscore the significance of GRU as a preferred choice for accurate reservoir behavior prediction. In addition to the aforementioned insights, our study contributes novel approaches to reservoir behavior prediction in the field of geothermal reservoir engineering. By leveraging deep learning algorithms, specifically the GRU and LSTM architectures, we introduce innovative methodologies for simulating reservoir behavior as alternatives to full reservoir simulations. Through comprehensive analysis, we demonstrate the superior predictive capabilities of the GRU model, particularly in capturing inter-well relationships and modeling complex reservoir dynamics.