ALBERT

Description: Microbial permafrost communities play an important role in carbon cycling and greenhouse gas fluxes. Despite the importance of these processes, there is a lack of knowledge about how environmental and climatic changes affect the abundance and composition of microorganisms. Here, we investigated the changing distribution of permafrost microorganisms in response to climate and lake-level changes. The permafrost core was drilled at the near shore of Lake El'gygytgyn, Far East Russian Arctic, and a combined microbiological and lipid biomarker approach was applied. The lower part of the permafrost core, deposited under subaquatic conditions, contains only small amounts of microbial signals; total organic carbon (TOC) content is sparse. After exposure of the site to subaerial conditions during the Allerød, the abundance of Bacteria and Archaea started to increase and the lake-level change is especially evidenced by the relative proportion of archaeal biomarkers. This increase is supported by rising bacterial and archaeal 16S ribosomal ribonucleic acid (rRNA) gene copy numbers and significant amounts of TOC during the late Allerød. After a small decrease during the colder Younger Dryas, the TOC content and the microbial signals strongly increase during the Holocene, presumably stimulated by pedogenesis. The occurrence of intact phospholipids indicates the presence of living microorganisms in these deposits. Our data suggest that methane formation is mainly expected for the subaerial interval, especially the Holocene where methanogens were identified by fingerprinting. This study emphasises the role of the uppermost permafrost deposits as a hotspot of carbon cycling in arctic environments, especially in the light of expected future global warming.

Description: Nineteen whole-round core samples from the Nankai accretionary prism (IODP Expeditions 315, 316, and 333) from a depth range of 28–128 m below sea floor were experimentally deformed in a triaxial cell under consolidated and undrained conditions at confining pressures of 400–1000 kPa, room temperature, axial displacement rates of 0.01–9.0 mm/min, and up to axially compressive strains of ∼64%. Despite great similarities in composition and grain size distribution of the silty clay samples, two distinct “rheological groups” are distinguished: The first group shows deviatoric peak stress after only a few percent of compressional strain (〈10%) and a continuous stress decrease after peak conditions. Simultaneous to this decrease is a pore pressure increase indicating contractant behavior characteristic of structurally weak material. The second sample group weakens only moderately at a much higher strength level after significantly higher strain (〉10%), or does not weaken at all. This is characteristic of structurally strong material. The strong samples tend to be overconsolidated and are all from the drillsites at the accretionary prism toe, while the weak and normally consolidated samples come from the immediate hanging wall of a megasplay fault further upslope. Sediments from the incoming plate are also structurally weak. The observed differences in mechanical behavior may hold a key for understanding strain localization and brittle faulting within the uniform silty and clayey sedimentary sequence of the Nankai accretionary prism.

Description: Tomography is like a photograph that was taken by a camera with blurred and defective lenses that deform the shapes and colours of objects. Reporting quantitative parameters derived from tomographic inversion is not always adequate because tomographic results are often strongly biased. To quantify the results of tomographic inversion, we propose a forward modelling and tomographic inversion (FM&TI) approach that aims to find a more realistic solution than conventional tomographic inversion. The FM&TI scheme is based on the assumption that if two tomograms derived from the inversion of observed and synthetic data are identical, the synthetic structure may appear to be closer to the real unknown structure in the ground than the inversion result. However, the manual design of the synthetic velocity distribution is usually time-consuming and ambiguous. In this study, we propose an approach that automatically searches for a probabilistic model. In this approach, a synthetic model is iteratively updated while taking into account the bias of the model in previous stages of the FM&TI performance. Here, we present an example of synthetic modelling and real data processing for an active source refraction data set corresponding to a marine profile across the subduction zone in Chile at about 32°S latitude. A key feature of the model is a low-velocity channel above the subducted oceanic crust, which was defined in the synthetic model and expected in the real case. The conventional first arrival traveltime tomography was barely able to resolve this channel. However, after several iterations of the FM&TI modelling, we succeeded in reconstructing this channel clearly. In the paper, we briefly discuss the nature of this low-velocity subduction channel, and we compare the results with other studies.

Description: Magnetotellurics (MT) uses a frequency-dependent impedance tensor estimated from the spectra of associated time-varying horizontal electric and magnetic fields measured at the Earth's surface to image the sub-surface of the Earth. Most current methods use Fourier transform based procedures to estimate power spectral densities and, therefore, assume that the signals are stationary over the record length. Stationarity in geomagnetic data, however, is not always ensured given the variety of source mechanism causing the geomagnetic variations at different time and spatial scales. Additional complication and bias may arise from the presence of noise in the recorded electric and magnetic file data. Sophisticated MT data processing account for a potential bias through windowing of the time series as well as robust estimates of the impedance. We explore a new heuristic method for dealing with the non-stationarity of MT time series based on empirical mode decomposition. It is a dynamic time series analysis method, in which complicated data sets can be decomposed into a finite and small number of simple intrinsic mode functions. In this paper, we use the empirical mode decomposition method to decompose MT data into intrinsic mode functions and calculate the instantaneous frequencies and spectra to determine the impedance tensor. We investigate the reliability of the impedance estimates on synthetic data by comparing the results to those obtained by analytical methods. Finally, we apply our processing scheme to data measured from the Costa Rica subduction zone, and compare the results from our new method to the frequently-used BIRRP processing method.