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Earthquake localization is both a necessity within the field of seismology, and a prerequisite for further analysis such as source studies and hazard assessment. Traditional localization methods often rely on manually picked phases. We present an alternative approach using deep learning that once trained can predict hypocenter locations efficiently. In seismology, neural networks have typically been trained with either single-station records or based on features that have been extracted previously from the waveforms. We use three-component full-waveform records of multiple stations directly. This means no information is lost during preprocessing and preparation of the data does not require expert knowledge. The first convolutional layer of our deep convolutional neural network (CNN) becomes sensitive to features that characterize the waveforms it is trained on. We show that this layer can therefore additionally be used as an event detector. As a test case, we trained our CNN using more than 2000 earthquake swarm events from West Bohemia, recorded by nine local three-component stations. The CNN successfully located 908 validation events with standard deviations of 56.4 m in east-west, 123.8 m in north-south, and 136.3 m in vertical direction compared to a double-difference relocated reference catalog. The detector is sensitive to events with magnitudes down to M-L = -0.8 with 3.5% false positive detections.
We present an experimental approach to study the three-dimensional microstructure of gas diffusion layer (GDL) materials under realistic compression conditions. A dedicated compression device was designed that allows for synchrotron-tomographic investigation of circular samples under well-defined compression conditions. The tomographic data provide the experimental basis for stochastic modeling of nonwoven GDL materials. A plain compression tool is used to study the fiber courses in the material at different compression stages. Transport relevant geometrical parameters, such as porosity, pore size, and tortuosity distributions, are exemplarily evaluated for a GDL sample in the uncompressed state and for a compression of 30 vol.%. To mimic the geometry of the flow-field, we employed a compression punch with an integrated channel-rib-profile. It turned out that the GDL material is homogeneously compressed under the ribs, however, much less compressed underneath the channel. GDL fibers extend far into the channel volume where they might interfere with the convective gas transport and the removal of liquid water from the cell. (C) 2015 AIP Publishing LLC.
In this study we investigate a dayside, midlatitude plasma depletion (DMLPD) encountered on 22 May 2014 by the Swarm and GRACE satellites, as well as ground-based instruments. The DMLPD was observed near Puerto Rico by Swarm near 10 LT under quiet geomagnetic conditions at altitudes of 475-520 km and magnetic latitudes of similar to 25 degrees-30 degrees. The DMLPD was also revealed in total electron content observations by the Saint Croix station and by the GRACE satellites (430 km) near 16 LT and near the same geographic location. The unique Swarm constellation enables the horizontal tilt of the DMLPD to be measured (35 degrees clockwise from the geomagnetic east-west direction). Ground-based airglow images at Arecibo showed no evidence for plasma density depletions during the night prior to this dayside event. The C/NOFS equatorial satellite showed evidence for very modest plasma density depletions that had rotated into the morningside from nightside. However, the equatorial depletions do not appear related to the DMLPD, for which the magnetic apex height is about 2500 km. The origins of the DMLPD are unknown, but may be related to gravity waves.
Compound natural hazards likeEl Ninoevents cause high damage to society, which to manage requires reliable risk assessments. Damage modelling is a prerequisite for quantitative risk estimations, yet many procedures still rely on expert knowledge, and empirical studies investigating damage from compound natural hazards hardly exist. A nationwide building survey in Peru after theEl Ninoevent 2017 - which caused intense rainfall, ponding water, flash floods and landslides - enables us to apply data-mining methods for statistical groundwork, using explanatory features generated from remote sensing products and open data. We separate regions of different dominant characteristics through unsupervised clustering, and investigate feature importance rankings for classifying damage via supervised machine learning. Besides the expected effect of precipitation, the classification algorithms select the topographic wetness index as most important feature, especially in low elevation areas. The slope length and steepness factor ranks high for mountains and canyons. Partial dependence plots further hint at amplified vulnerability in rural areas. An example of an empirical damage probability map, developed with a random forest model, is provided to demonstrate the technical feasibility.
Protection of natural or semi-natural ecosystems is an important part of societal strategies for maintaining biodiversity, ecosystem services, and achieving overall sustainable development. The assessment of multiple emerging land use trade-offs is complicated by the fact that land use changes occur and have consequences at local, regional, and even global scale. Outcomes also depend on the underlying socio-economic trends. We apply a coupled, multi-scale modelling system to assess an increase in nature protection areas as a key policy option in the European Union (EU). The main goal of the analysis is to understand the interactions between policy-induced land use changes across different scales and sectors under two contrasting future socio-economic pathways. We demonstrate how complementary insights into land system change can be gained by coupling land use models for agriculture, forestry, and urban areas for Europe, in connection with other world regions. The simulated policy case of nature protection shows how the allocation of a certain share of total available land to newly protected areas, with specific management restrictions imposed, may have a range of impacts on different land-based sectors until the year 2040. Agricultural land in Europe is slightly reduced, which is partly compensated for by higher management intensity. As a consequence of higher costs, total calorie supply per capita is reduced within the EU. While wood harvest is projected to decrease, carbon sequestration rates increase in European forests. At the same time, imports of industrial roundwood from other world regions are expected to increase. Some of the aggregate effects of nature protection have very different implications at the local to regional scale in different parts of Europe. Due to nature protection measures, agricultural production is shifted from more productive land in Europe to on average less productive land in other parts of the world. This increases, at the global level, the allocation of land resources for agriculture, leading to a decrease in tropical forest areas, reduced carbon stocks, and higher greenhouse gas emissions outside of Europe. The integrated modelling framework provides a method to assess the land use effects of a single policy option while accounting for the trade-offs between locations, and between regional, European, and global scales.
Monsoon systems around the world are governed by the so-called moisture-advection feedback. Here we show that, in a minimal conceptual model, this feedback implies a critical threshold with respect to the atmospheric specific humidity q(o) over the ocean adjacent to the monsoon region. If q(o) falls short of this critical value q(o)(c), monsoon rainfall over land cannot be sustained. Such a case could occur if evaporation from the ocean was reduced, e.g. due to low sea surface temperatures. Within the restrictions of the conceptual model, we estimate q(o)(c) from present-day reanalysis data for four major monsoon systems, and demonstrate how this concept can help understand abrupt variations in monsoon strength on orbital timescales as found in proxy records.
The Upper Cretaceous (Campanian-Maastrichtian) bioclastic wedge of the Orfento Formation in the Montagna della Maiella, Italy, is compared to newly discovered contourite drifts in the Maldives. Like the drift deposits in the Maldives, the Orfento Formation fills a channel and builds a Miocene delta-shaped and mounded sedimentary body in the basin that is similar in size to the approximately 350 km(2) large coarse-grained bioclastic Miocene delta drifts in the Maldives. The composition of the bioclastic wedge of the Orfento Formation is also exclusively bioclastic debris sourced from the shallow-water areas and reworked clasts of the Orfento Formation itself. In the near mud-free succession, age-diagnostic fossils are sparse. The depositional textures vary from wackestone to float-rudstone and breccia/conglomerates, but rocks with grainstone and rudstone textures are the most common facies. In the channel, lensoid convex-upward breccias, cross-cutting channelized beds and thick grainstone lobes with abundant scours indicate alternating erosion and deposition from a high-energy current. In the basin, the mounded sedimentary body contains lobes with a divergent progradational geometry. The lobes are built by decametre thick composite megabeds consisting of sigmoidal clinoforms that typically have a channelized topset, a grainy foreset and a fine-grained bottomset with abundant irregular angular clasts. Up to 30 m thick channels filled with intraformational breccias and coarse grainstones pinch out downslope between the megabeds. In the distal portion of the wedge, stacked grainstone beds with foresets and reworked intraclasts document continuous sediment reworking and migration. The bioclastic wedge of the Orfento Formation has been variously interpreted as a succession of sea-level controlled slope deposits, a shoaling shoreface complex, or a carbonate tidal delta. Current-controlled delta drifts in the Maldives, however, offer a new interpretation because of their similarity in architecture and composition. These similarities include: (i) a feeder channel opening into the basin; (ii) an excavation moat at the exit of the channel; (iii) an overall mounded geometry with an apex that is in shallower water depth than the source channel; (iv) progradation of stacked lobes; (v) channels that pinch out in a basinward direction; and (vi) smaller channelized intervals that are arranged in a radial pattern. As a result, the Upper Cretaceous (Campanian-Maastrichtian) bioclastic wedge of the Orfento Formation in the Montagna della Maiella, Italy, is here interpreted as a carbonate delta drift.
The literature contains a sizable number of publications where weather types are used to decompose climate shifts or trends into contributions of frequency and mean of those types. They are all based on the product rule, that is, a transformation of a product of sums into a sum of products, the latter providing the decomposition. While there is nothing to argue about the transformation itself, its interpretation as a climate shift or trend decomposition is bound to fail. While the case of a climate shift may be viewed as an incomplete description of a more complex behaviour, trend decomposition indeed produces bogus trends, as demonstrated by a synthetic counterexample with well-defined trends in type frequency and mean. Consequently, decompositions based on that transformation, be it for climate shifts or trends, must not be used.
Design flood estimation is an essential part of flood risk assessment. Commonly applied are flood frequency analyses and design storm approaches, while the derived flood frequency using continuous simulation has been getting more attention recently. In this study, a continuous hydrological modelling approach on an hourly time scale, driven by a multi-site weather generator in combination with a -nearest neighbour resampling procedure, based on the method of fragments, is applied. The derived 100-year flood estimates in 16 catchments in Vorarlberg (Austria) are compared to (a) the flood frequency analysis based on observed discharges, and (b) a design storm approach. Besides the peak flows, the corresponding runoff volumes are analysed. The spatial dependence structure of the synthetically generated flood peaks is validated against observations. It can be demonstrated that the continuous modelling approach can achieve plausible results and shows a large variability in runoff volume across the flood events.
A confocal set-up is presented that improves micro-XRF and XAFS experiment with high-pressure e diamond-anvil cells (DACs) In this experiment a probing volume is defined by the focus of the incoming synchrotron radiation beam and that of a polycapillary X-ray half-lens with a very long working distance, which is placed in front of the fluorescence detector This set-up enhances the quality of the fluorescence and XAFS spectra, and thus the sensitivity for detecting elements at low concentrations. It efficiently suppresses signal from outside the sample chamber, which stems from elastic and inelastic scattering of the incoming beam by the diamond anvils as well as from excitation of fluorescence from the body of the DAC