ALBERT

Description: New inorganic and organic geochemical data from thucholite in the Upper Permian (Wuchiapingian) Kupferschiefer (T1) shale collected at the Polkowice-Sieroszowice Cu-Ag mine in Poland are presented. Thucholite, which forms spherical or granular clusters, appears scattered in the T1 dolomitic shale at the oxic-anoxic boundary occurring within the same shale member. The composition of thucholite concretions and the T1 shale differs by a higher content of U- and REE-enriched mineral phases within the thucholite concretions compared to the T1 shale, suggesting a different mineralising history. The differences also comprise higher Ntot, Ctot, Htot, Stot contents and higher C/N, C/S ratios in thucholite than in the T1 shale. The hydrocarbon composition of the thucholite and the surrounding T1 shale also varies. Both are dominated by polycyclic aromatic compounds and their phenyl derivatives. However, higher abundances of unsubstituted polycyclic aromatic hydrocarbons in the thucholite are indicative of its pyrogenic origin. Pyrolytic compounds such as benz[a]anthracene or benzo[a]pyrene are more typical of the thucholite than the T1 shale. Microscopic observations of the thucholite and its molecular composition suggest that it represents well-rounded small charcoal fragments. These charcoals were formed during low-temperature combustion, as confirmed by semifusinite reflectance values, indicating surface fire temperatures of about 400 °C, and the absence of the high-temperature pyrogenic polycyclic aromatic hydrocarbons. Charred detrital particles, likely the main source of insoluble organic matter in the thucholite, migrated to the sedimentary basin in the form of spherical carbonaceous particulates, which adsorbed uranium and REE in particular, which would further explain their different contents and sorption properties in the depositional environment. Finally, the difference in mineral content between thucholite and the T1 shale could also have been caused by microbes, which might have formed biofilms on mineral particles, and caused a change in the original mineral composition.

Description: Severe flood losses have been on the rise, and this trend is expected to become increasingly prevalent in the future due to climate and socio-economic changes. Swiftly identifying flooded areas is crucial for mitigating socio-economic losses and facilitating effective recovery. Synthetic aperture radar (SAR) sensors are operational in all-weather, day-and-night conditions and offer a rapid, accurate, and cost-effective means of obtaining information for quick flood mapping. However, the complex nature of SAR images, such as speckle noise, coupled with the often absence of training/labeled samples, presents significant challenges in their processing procedures. To alleviate such hindrances, we can benefit from unsupervised classification approaches (also known as clustering). Clustering methods offer valuable insights into newly acquired datasets without the need for training or labeled samples. However, traditional clustering approaches are predominantly linear-based and overlook the spatial information of neighboring pixels during analysis. Thus, to attenuate these challenges, we propose a deep-learning (DL)-based clustering approach for flood detection (DC4Flood) using SAR images. The primary advantage of DC4Flood over existing DL-based clustering approaches lies in its ability to capture multiscale spatial information. This is achieved using multiple dilated convolutions with varying dilation rates and subsequently fusing the extracted multiscale information to effectively and efficiently analyze SAR images in an unsupervised manner. Extensive experiments conducted on SAR images from six different flood events demonstrate the effectiveness of the proposed DC4Flood. The code of the work will be available at https://github.com/Kasra2020/DC4Flood .

Description: Project “Saptarshi” was initiated by the National Centre for Geodesy, Indian Institute of Technology Kanpur to set up the modern space geodetic infrastructure in the country. This project primarily focuses on the establishment of an Indian Geodetic VLBI network. The purpose of this paper is to anticipate the potential impact of the geodetic VLBI network in India to the national and international scientific products. Saptarshi proposes to establish three VLBI stations along with a correlator at one facility. In this work, we investigate how adding proposed Indian VLBI antennas will affect terrestrial and celestial reference frames as well as Earth Orientation Parameters (EOP). Additionally, we shortly demonstrate scenario of VLBI observations of one of the Indian regional navigation satellite system called Navigation with Indian Constellation (NavIC) to determine its orbit. Two VLBI networks were simulated to observe the NAVIC satellite along with quasars to check how well the orbit of this satellite can be recovered from VLBI observations. To investigate the impact on the terrestrial reference frame, three types of 24-h sessions, IVS-R1 (legacy), IVS-VGOS (next generation VLBI), and IVS-AOV (Asia Oceania VLBI), were studied to examine the gain in precision of geodetic parameters when adding the proposed Indian VLBI antennas. IVS-type Intensive sessions were also investigated with the proposed Indian antennas to assess the improvement in the estimation of dUT1 as one important VLBI product. Furthermore, the u-v coverage of some radio sources of the southern hemisphere was compared utilizing observing networks with and without the proposed Indian antennas. Apart from that, we briefly discuss other benefits of the establishment of Indian geodetic VLBI in the scientific fields of atmosphere, metrology, and space missions.

Description: The global navigation satellite system (GNSS)-interferometric reflectometry technique has been applied to retrieve snow depth, which has a high potential for application. The GNSS reflectometry classical algorithm retrieves the snow depth by extracting the frequency of the multipath signal and substituting it into an empirical formula. However, the retrieval errors of high and low snow depths are large due to the influence of factors such as surface vegetation and terrain environment. In this paper, we propose a snow depth retrieval algorithm based on a particle swarm optimized long short-term memory (PSO-LSTM) neural network. The algorithm extracted three characteristic parameters (frequency, amplitude, and phase) from the signal-to-noise ratio (SNR) data as inputs, and optimized the LSTM hyperparameters by the PSO algorithm to improve the retrieval accuracy for low snow depths and snow depths close to the antenna. The snow depth retrieval results of global positioning system L1 band SNR data collected from the P351 station in 2022 and AB33 station in 2017 were evaluated in this paper. The snow depth retrieval results of the PSO-LSTM algorithm for P351 station were in high agreement with the snow depth data provided by the snowpack telemetry network; the coefficient of determination () reached 0.986, and the root mean square error (RMSE) and mean absolute error (MAE) were 7.30 cm and 4.94 cm, respectively. Compared with the classical algorithm, the PSO-LSTM algorithm decreased the RMSE and MAE by 53.0% and 30.4% for the retrieval results of snow depths below 15 cm at the P351 station, and by 76.8% and 84.4% for the retrieval results of snow depths above 117 cm from the 1st day to the 137th day, respectively. Similarly, the RMSE, MAE, and for the 2017 retrieval results at AB33 station were 5.90 cm, 4.25 cm, and 0.965, respectively. Compared with the classical algorithm, the PSO-LSTM algorithm decreased the RMSE and MAE by 47.9% and 33.0% for the retrieval results of snow depths below 15 cm at the AB33 station, and by 75.4% and 82.3% for the retrieval results of snow depths above 56 cm from the day 46 to day 120. In addition, the snow depth retrieval algorithm was proposed in this paper does not require antenna height and empirical formulas to realize snow depth retrieval, and at the same time, the algorithm effectively improved the retrieval accuracy for both high and low snow depths with strong robustness.

Description: This study contributes to geothermal exploration in 1660–1520 Ma old, reworked bedrock in Sweden. Our primary objectives are to constrain the orientation of horizontal stresses, and to discuss implications for geothermal exploration. High-resolution acoustic televiewer image data reveal the downhole distribution of stress indicators (borehole breakouts, drilling-induced fractures and petal centreline fractures) and pre-existing structures (natural fractures, foliation). About 135 m of stress indicators are measured from 0.2–1.0 km. The results suggest a uniform NNW–SSE mean maximum horizontal stress orientation. A total of 1525 pre-existing structures (natural fractures, foliation) are mapped in borehole GE-1. The prevailing stress regime controls whether natural fractures and foliation are well-oriented for stimulation. For strike-slip and normal faulting stress regimes, well-oriented fractures steeply dip towards the WSW. For a reverse faulting stress regime, shallow dipping fractures are well-oriented for stimulation. The downhole distribution of stress indicators and other stress measurements in the region and other parts of Fennoscandia tentatively suggest a strike-slip stress regime, but additional studies are needed to constrain the complete stress field at study depth and towards engineered geothermal systems reservoir target depths. Our secondary objective is to highlight that interpretation of high-resolution acoustic data, particularly in metamorphic crystalline rocks, is subjective and that more guidelines for data interpretation are needed. The interactive interpretation of the images is based on visual analyses of complex pre-existing structures and stress indicators with highly variable shapes. The application of three methods for data analyses in the GE-1 borehole proposes that drilling-induced fractures are little influenced by the method applied. Interpretations on individual borehole breakout azimuths may, however, result in over 10° differences in orientation.

Description: Soil bacterial communities play a critical role in shaping soil stability and formation, exhibiting a dynamic interaction with local climate and soil depth. We employed an innovative DNA separation method to characterize microbial assemblages in low-biomass environments such as deserts and distinguish between intracellular DNA (iDNA) and extracellular DNA (eDNA) in soils. This approach, combined with analyses of physicochemical properties and co-occurrence networks, investigated soil bacterial communities across four sites representing diverse climatic gradients (i.e., arid, semi-arid, Mediterranean, and humid) along the Chilean Coastal Cordillera. The separation method yielded a distinctive unimodal pattern in the iDNA pool alpha diversity, increasing from arid to semi-arid climates and decreasing in humid environments, highlighting the rapid feedback of the iDNA community to increasing soil moisture. In the arid region, harsh surface conditions restrict bacterial growth, leading to peak iDNA abundance and diversity occurring in slightly deeper layers than the other sites. Our findings confirmed the association between specialist bacteria and ecosystem-functional traits. We observed transitions from Halomonas and Delftia, resistant to extreme arid environments, to Class AD3 and the genus Bradyrhizobium, associated with plants and organic matter in humid environments. The distance-based redundancy analysis (dbRDA) analysis revealed that soil pH and moisture were the key parameters that influenced bacterial community variation. The eDNA community correlated slightly better with the environment than the iDNA community, whereas the iDNA community was more sensitive to changes in soil physicochemical parameters. Soil depth was found to influence the iDNA community significantly but not the eDNA community, which might be related to depth-related metabolic activity. Our investigation into iDNA communities uncovered deterministic community assembly and distinct co-occurrence modules correlated with unique bacterial taxa, thereby showing connections with sites and key environmental factors. The study additionally revealed the effects of climatic gradients and soil depth on living and dead bacterial communities, emphasizing the need to distinguish between iDNA and eDNA pools.