Graph neural networks are a newly established category of machine learning algorithms dealing with relational data. They can be used for the analysis of both spatial and/or temporal data. They are capable of modeling how time series of nodes, which are located at different spatial positions, change by the exchange of information between nodes and their neighbors. As a result, time series can be predicted to future epochs.GNSS networks consist of stations at different locations, each producing time series of geodetic parameters, such as changes in their positions. In order to successfully apply graph neural networks to predict time series from GNSS networks, the physical properties of GNSS time series should be taken into account. Thus, we suggest a new graph neural network algorithm that has both a physical and a mathematical basis. The physical part is based on the fundamental concept of information exchange between nodes and their neighbors. Here, the temporal correlation between the changes of time series of the nodes and their neighbors is considered, which is computed by geophysical loading and/or climatic data. The mathematical part comes from the time series prediction by mathematical models, after the removal of trends and periodic effects using the singular spectrum analysis algorithm. In addition, it plays a role in the computation of the impact of neighboring nodes, based on the spatial correlation computed according to the pair-wise node-neighbor distance. The final prediction is the simple weighted summation of the predicted values of the time series of the node and those of its neighbors, in which weights are the multiplication of the spatial and temporal correlations.In order to show the efficiency of the proposed algorithm, we considered a global network of more than 18000 GNSS stations and defined the neighbors of each node as stations that are located within the range of 10 km. We performed several different analyses, including the comparison between different machine learning algorithms and statistical methods for the time series prediction part, the impact of the type of data used for the computation of temporal correlation (climatic and/or geophysical loading), and comparison with other state-of-the-art graph neural network algorithms. We demonstrate the superiority of our method to the current graph neural network algorithms when applied to time series of geodetic networks. In addition, we show that the best machine learning algorithm to use within our graph neural network architecture is the multilayer perceptron, which shows an average of 0.34 mm in prediction accuracy. Furthermore, we find that the statistical methods have lower accuracies than machine learning ones, as much as 44 percent.
The road traffic is highly sensitive to weather conditions. Accumulation of snow on the road can cause important safety problems. But road conditions monitoring is as hard as critical: in mid-latitude countries, on the one hand, the spatial variability of snowfall is high and on the other hand, accurate characterization of snow accumulation mainly relies on costly sensors. In recent decades, webcams have become ubiquitous along the road network. The quality of these webcams is variable but even low-resolution images capture information about the extent and the thickness of the snow layer. Their images are also currently used by forecasters to refine their analysis. The automatic extraction of relevant meteorological information is hence very useful. Recently, generic and efficient computer vision methods have emerged. Their application to image-based weather estimation has become an attractive field of research. However, the scope of existing work is generally limited to high-resolution images from one or a few cameras. In this study, we show that for a moderate effort of labelling, recent Machine Learning approaches allow us to predict quantitative indices of the snow depth for a large variety of webcam settings and illumination. Our approach is based on two datasets. The smallest one contains about 2.000 images coming from ten webcams that were set up near sensors devoted to snow depth measurements. The largest one contains 20,000 images coming from 200 cameras of the AMOS dataset. Meteorological standard rules of human observation and the specifics of the webcams have been taken into account to manually label each image. These labels are not only about the thickness and the extent of the snow layer but also describe the precipitation (rain or snow, presence of streaks), the optical range and the foreground noise. Both datasets contain night images (45%) and at least 15% of images corrupted by foreground noise (filth, droplets, and snowflakes on the lens). The labels of the AMOS subset allowed us to train ranking models for snow depth and visibility using a multi-task setting. The models are then calibrated on the smallest dataset. We tested several versions, built from pre-trained CNNs (ResNet152, DenseNet161, and VGG16). Results are promising with up to 85% accuracy for comparison tasks, but a 10% decrease can be observed when the test webcams have not been used during the training phase. A case study based on a widespread snow event over the French territory will be presented. We will show the potential of our method through a comparison with operational model forecasts.
Deep-seated gravitational slope deformations (DSGSD) gains new attention in Taiwan due to their catastrophic impacts on lives and infrastructures during Typhoon Morakot in 2009. As the main Taiwan island is located on a complex convergent plate boundary, conventional observations and analyses suggest that the island’s strong tectonic activity has, along with its subtropical climate and intense human activity in mountain areas, contributed to the formation of deep-seated landslides. It is especially so for high-altitude areas featuring Miocene to Eocene meta-sandstone and slate successions, where reactivations of landslide terrains are observed from field observation and some GPS sites after specific events. Among them, Tienchih, located in Lalong River of Kaohsiung, and Yakou, few km east in Taitung County were assessed as highly landslide-prone area after the heavy precipitation of Typhoon Morakot (over 2700 mm of rainfall within only 5 days). In this areas, several deep-seated landslides were identified according to geomorphological features seen in the 1-m resolution LiDAR DEM and InSAR preliminary results. In Tienchih area, a catastrophic 240-mm displacement sized 6.7 ha was recorded by a continuous GPS site, TENC, in 2016 after a heavy rainfall occurred on June 2. The correlation in the temporal variation of continuous GPS displacement time series and rainfall suggests that the movement is possibly related to gravitational load overlying water-saturated sediments. In addition, the average annual displacement rate of this downslope movement was measured at 20-40 mm/yr using the recently developed temporarily coherence points InSAR (TCPInSAR) technique based on ALOS/PALSAR imagery collected between 2007 and 2011. Apart therefrom, the high-angle thrust with highly fractured metamorphosed sandstone on the hanging wall; and the river incision and lateral river bank erosion are considered as the triggering factor of this catastrophic landslide. Similar triggering factors are responsible for Yakou landslide, where the 2018 landslide event exposed an outstanding cross section of the predisposing geological setting characterized by a tightly folded sequence of metamorphosed sandstone and slates. Spectacular gravitational deformation structures (i.e. kink folds and shear zones) are also found along this slope testifying a long-term displacement history and shedding light on possible kinematic mechanisms controlling its evolution. Through field data, remote sensing techniques and optical methods (i.e. digital image correlation, 3D LiDAR point cloud comparison), we compared the two landslide sites unravelling different deformation styles and identifying nested sectors possibly evolving to collapse. Our primary results demonstrate that valley erosion and deep-seated gravitational creep are significant to the deformation of slate, indicating a block movement with shear concentration at the basal sliding surface with a mainly rotational-translational movement in Tienchih and a translational failure mechanism in Yakou.
Seismic images provided by standard Reverse Time Migration are usually contaminated by artefacts associated with the migration of multiples. Multiples can corrupt seismic images by producing both false negatives, i.e. by destructively interfering with primaries, and false positives, i.e. by focusing energy at unphysical interfaces. Free-surface multiples particularly affect seismic images resulting from marine data, while internal multiples strongly contaminate both land and marine data. Multiple prediction / primary synthesis methods are usually designed to operate on point source gathers, and can therefore be computationally demanding when large problems, involving hundreds of gathers, are considered. In this contribution, a new scheme for fully data-driven retrieval of primary responses of plane-wave sources is presented. The proposed scheme, based on convolutions and cross-correlations of the reflection response with itself, extends a recently devised Marchenko point-sources primary retrieval method for to plane-wave source data. As a result, the presented algorithm allows fully data-driven synthesis of primary reflections associated with plane-wave source data. Once primary plane-wave responses are estimated, they are used for multiple-free imaging via standard reverse time migration. Numerical tests of increasing complexity demonstrate the potential of the proposed algorithm to produce multiple-free images only involving the migration of few datasets. The plane-wave source primary synthesis algorithm discussed in this contribution could then be used as an initial and unexpensive processing step, potentially guiding more expensive target imaging techniques. Moreover, the method could be applied to large 3D problems for which standard methods are prohibitively expensive from a computational point of view.
In most university geosciences curricula, structural geology and tectonics (SGT) form a core part of teaching. While only a small percentage of Earth science graduates will become structural geologists, many will someday use structural concepts and techniques to solve problems in fields such as nuclear waste storage, the geology of growing urban environments, geohazards, unconventional reservoirs, geothermal energy, CO2 sequestration, energy storage and more. A basic understanding of structural geology is thus part of a critical knowledge foundation in Earth sciences and many related disciplines. In addition, new tools and data are becoming available at a rapid pace, and enable more integrated, multi-dimensional assessments of the geosphere and our societal interfaces with it. All of this provides new opportunities and challenges for STG courses.In April 2019, a pre-EGU two-day workshop (TeachSGT21) was organized during which strengths and weaknesses of, and threats to current SGT curricula were analyzed. Participants of the workshop covered 11 European and 2 overseas countries, and came from academia as well as industry. On the basis of the workshop, we now outline educational demands from industry and research and discuss the role and significance of field training. Further, we review initiatives that use innovative tools and techniques in teaching. While not claiming to represent all aspects of modern SGT teaching, we expect that our observations can stimulate reflection on degrees and approach and may help making choices in curriculum renewal.
Under certain conditions, meter to house-sized boulders fall, jump, and roll from topographic highs to topographic lows, a landslide type termed rockfall. On the Moon, these features have first been observed in Lunar Orbiter photographs taken during the pre-Apollo era. Understanding the drivers of lunar rockfall can provide unique information about the seismicity and erosional state of the lunar surface, however this requires high resolution mapping of the spatial distribution and size of these features. Currently, it is believed that lunar rockfalls are driven by moonquakes, impact-induced shaking, and thermal fatigue. Since the Lunar Orbiter and Apollo programs, NASA’s Lunar Reconnaissance Orbiter Narrow Angle Camera (NAC) returned more than 2 million high-resolution (NAC) images from the lunar surface. As the manual extraction of rockfall size and location from image data is time intensive, the vast majority of NAC images have not yet been analyzed, and the distribution and number of rockfalls on the Moon remains unknown. Demonstrating the potential of AI for planetary science applications, we deployed a Convolutional Neural Network in combination with Google Cloud’s advanced computing capabilities to scan through the entire NAC image archive. We identified 136,610 rockfalls between 85°N and 85°S and created the first global, consistent rockfall map of the Moon. This map enabled us to analyze the spatial distribution and density of rockfalls across lunar terranes and geomorphic regions, as well as across the near- and farside, and the northern and southern hemisphere. The derived global rockfall map might also allow for the identification and localization of recent seismic activity on or underneath the surface of the Moon and could inform landing site selection for future geophysical surface payloads of Artemis, CLPS, or other missions. The used CNN will soon be available as a tool on NASA JPL’s Moon Trek platform that is part of NASA’s Solar System Treks (trek.nasa.gov/moon/).
The Siberian forests cover about 70% of the total area of the Eurasian boreal forest and are an important factor controlling global and regional climate. Forest fires and biogenic emissions from coniferous trees and forest litter are the main sources of carbonaceous aerosols emitted into the atmosphere over boreal forests. Typically, two classes of carbonaceous aerosol are commonly present in ambient air – elemental carbon (EC) (often referred to as black carbon or soot) and organic carbon (OC). Both OC and EC are important agents in the climate system, which affect the optical characteristics and thermal balance of the atmosphere both directly, by absorbing and scattering incoming solar radiation, and indirectly, by modifying cloud properties. In 2010, a filter-based sampler was mounted at the background ZOTTO station (60.8º N and 89.4 º E; 114 m a.s.l.) for aerosol chemical analysis. We present here the time series of carbonaceous aerosol data measurements for 10 years (2010 -2019). We investigate the seasonal variations in PM, EC, and OC. These data are supplemented by measurements of aerosol absorption (PSAP) and scattering (TSI 3563) coefficients. We analyze polluted, background and near-pristine periods, as well as the most pronounced pollution events and their sources, observed over the entire sampling campaign. We also present ground-based measurements of aerosol-cloud condensation nuclear (CCN) properties and hygroscopicity parameter values obtained from the CCN dataset. A method for assessing the condensation properties of aerosols from satellite measurements based on the data of the VIIRS multichannel radiometer installed on the polar satellite Suomi (USA) has been implemented. The CCN parameters of aerosol particles determined from satellite datasets have been compared with those obtained from ground-based measurements. Acknowledgments. This work was supported by the Russian Science Foundation (grant agreement no. 18-17-00076) and Max Planck Society (MPG).
On the 1st November AD 1755, the tsunami, triggered by the 8.5 to 9 MW 1755 Lisbon earthquake, caused major inundations with sediment transport along the coastline of the Gulf of Cadiz. The study area, Conil de la Frontera (El Palmar de Vejer), located at the Gulf of Cadiz in southwestern Spain, was severely stuck by the AD 1755 Lisbon tsunami. Witness of the destruction and power of the tsunami inundation are the walls of Torre de Castilnovo, close to the study area, which got heavily destroyed. El Palmar de Vejer was chosen as a study area due to the topographical setting, characterized by the flat alluvial flood plain. With these peculiarities, the area presents good preconditions as a sedimentological archive for potential deposits of the AD 1755 tsunami. First, geophysical methods were used to identify potential sandy layers attributed to the AD 1755 tsunami. Ground-penetrating radar (270 MHz antenna) was used to systematically scan the ground to a depth of ca. 3 m. The evaluation of these radargrams were taken into account for the selection of GeoSlicer drilling locations. Based on the samples obtained, granulometric analyses were carried out (1) to identify the potential sandy tsunami deposit; (2) to analyze the different sedimentological depositional environments before, during and after the tsunami; (3) to detect tsunami sublayers deriving from different waves within the wave-train of the AD 1755 Lisbon tsunami, since 3 waves were reported. Furthermore, both inorganic and organic geochemical investigations were performed on the samples. With the help of inorganic geochemical analysis of major elements (Si, Sr, Ti, Ca, N, S) as well as elemental ratios can identify a distinction between marine and terrestrial depositional environments and accumulate more information about the deposit facies. By the use of organic geochemistry for the analysis of biomarker, several different natural compounds were detected (e.g., n-alkanes, n-aldehydes). Biomarker results suggest a distinct differentiation between the AD 1755 tsunami deposit and the surrounding background sediment layers above and below. The tsunami deposits contrasts to the post and pre-tsunami layers by different concentrations of biomarkers and deviant occurrence of specific compounds. The n-alkanes are manifesting the difference of marine and terrestrial sources of the different layers. Results of this study analyzing the Iberian sedimentary archives at Conil de la Frontera present strong evidence that a multi-proxy approach with the inclusion of geochemical applications can confidently detect tsunami deposits, distinguish them from surrounding background sediments and subsequently characterize the internal structure and composition of the tsunami deposit.
The Swiss Seismological Service (SED; http://www.seismo.ethz.ch) at ETH Zürich is the federal agency in charge of monitoring earthquakes in Switzerland and neighboring areas, and for the assessment of seismic hazard and risk for the region. The SED seismic network largely relies on software and databases integrated in the SeisComP3 monitoring suite for waveform acquisition, automatic and manual event processing, event alerting, web infrastructure, data archiving and dissemination. Data from all digital seismic stations acquired by the SED over the last 30 years - broadband (presently ~230), strong-motion (~185), short-period (~65), permanent and temporary - are homogeneously integrated in the seismic network processing tools and products. Waveform data from the Swiss National Seismic Networks are openly available through the SED website and ORFEUS EIDA / Strong-Motion (http://orfeus-eu.org/data/) data gateways. The SED earthquake catalogue is publicly available through FDSN Event web services at the SED (http://arclink.ethz.ch/fdsnws/event/1/). The Swiss seismic hazard maps are integrated in the EFEHR portal (http://www.efehr.org). The SED is updating its strategy for magnitude determination to make it fully consistent with the state-of-the-art in engineering seismology and seismic hazard studies in Switzerland, and to optimise the use of its dense seismic monitoring infrastructure. Among the planned changes are the: (a) adoption of a new ML relationship applicable in the near-source region at epicentral distances smaller than 15-20 km; (b) inclusion of ML station corrections based on empirically observed (de)amplification with respect to the Swiss reference rock velocity model and associated predictions; (c) seamless computation of Mw based on spectral fitting of recorded FAS using a Swiss specific model. In this contribution we present and discuss the updated magnitude computations for a playback dataset of thousands of recorded earthquakes, and compare them with the current official estimates. We discuss the expected impacts of the new magnitude determination strategy on the SED event processing chain in SeisComP3, the SED catalogues and other seismological products. We welcome community feedback on our planned transition strategy.
Sharp interfaces often separate regions in the subsurface with distinctively different properties due to processes in geological evolution – and these interfaces are relevant for a variety of scientific investigations, as well as practical applications. The delineation of these layers with different properties is commonly attempted on the basis of geological and geophysical data, for example as picks in prevalent seismic reflectors, interpreted from potential field measurements, and derived from observations in drillholes. We evaluate here a specific method to determine the position and shape of such an interface using measurements of state variables related to a physical flow field described with an elliptic PDE. A typical example is the measurement of temperatures related to heat flow through zones with distinctively different thermal conductivities. We use a level-set function to describe the interface and determine the optimal interface shape for a 2-D case. This type of shape inversion has been successfully attempted before, and we extend on this previous work by including additional shape constraints on orientation, interface, and observations of specific segmentation outcomes. These constrains are motivated by geological information that may be available, for example as derived and interpreted from additional geophysical measurements. We model this as an image segmentation problem, where we are looking for a segmentation of the image domain whose induced temperature minimizes the squared L2 distance to temperature measurements on a lower dimensional set. From an optimal control perspective, the segmentation is the control and the temperature the state. Numerically, the segmentation is represented by a level set and the minimization is done using a gradient flow, where the derivative with respect to the level set is computed using dualization. Moreover, we include additional geologically motivated constraints by adding soft penalties to the objective function. We test our method with several conceptual examples to determine the feasibility and limitations, especially with regard to different interface shapes and the amount of available information and additional geological constraints, as well as the influence of noise on the detection accuracy. Results show that these additional constraints help determining an interface. However, measurement noise and a non-homogeneous spatial distribution of physical properties reduces the accuracy of the derived interface.
Active intraplate deformation as a far-field effect of the India-Eurasia convergence has led to four Mw∼8 earthquakes in western and southern Mongolia in the past century. Palaeoseismological and morphotectonic studies have shown that these earthquakes are characteristic events along transpressive fault systems with cumulative offsets. The tectonically active Gobi Altai and Hangay mountains are separated by the seismically quiescent Valley of Gobi Lakes, which consists of major perennial rivers draining into endorheic lakes. Despite the scarcity of recorded earthquakes, Quaternary deposits in the Valley of Gobi Lakes are crosscut by multiple fault scarps with significant, landscape-altering displacements. To assess past earthquakes and the potential seismic hazard of this area, we apply remote sensing, tectono-morphometric techniques and cosmogenic nuclide dating to estimate the amount of deformation the faults in the Valley of Gobi Lakes are accommodating, and to determine the effect of these faults on local landscape evolution. The Tuyn Gol (gol = Mongolian for river) is crosscut by four E-W to NE-SW trending fault scarps that display variable fault kinematics due to scarp orientation differences relative to a stable NE-SW principle stress direction. Mapping of the >40–90 km long Valley of Gobi Lake faults shows that they can accommodate M ∼ 7 earthquakes. Offset measurements of the Tuyn Gol deposits allow Middle Pleistocene to modern vertical slip rate estimates and M ∼ 7 earthquake recurrence intervals of 0.012 ± 0.007–0.13 ± 0.07 mm/yr and 5.24 ± 2.61–81.57 ± 46.05 kyr, respectively. Cumulative vertical displacement amounts to 0.27 ± 0.08 mm/yr, which is similar to that of major tectonic structures such as the Bogd fault in the Gobi Altai. This implies that the total active deformation accommodated by southern Mongolian faults may be larger than previously expected and distributed across more faults between the Hangay and the (Gobi) Altai mountain ranges. Geomorphological observations and surface exposure dating indicate that the Tuyn Gol drainage system developed over four to five 100 kyr climate cycles, during which active deformation played an important role in drainage reorganization. Our results demonstrate the dominant role of tectonics on local landscape dynamics, indicating the importance of studying regional geomorphology to adequately estimate the earthquake potential of faults that were considered inactive.
Machine learning approaches and deep learning-based methods are efficient tools to address problems for which large amounts of observations and data are documented. They have proven excellent performance for many applications in the geosciences and remote sensing area. However, to one of the most fundamental data types in geoscientific studies, mineral thin sections, they have not yet been applied to its full potential. Mineral thin sections contain a treasure of information. It is anticipated that thin section samples can be systematically and quantitatively analyzed with a specifically designed system equipped with ML approaches or deep learning methods such as CNNs. The development of any artificial intelligence application that enables automated image analysis requires consistent and sufficiently large training datasets with ground truth labels. However, a dataset which serves for visual object detection in petrographic thin sections analysis is still missing. We wish to close this data gap by generating a large dataset of pixel-wise annotated microscopic images for thin sections. The variation of optical features of certain minerals under different settings of a petrographic microscope is closely related to crystallographic characteristics that can be indicative for a mineral. In order to fully capture optical features into digital images, we generated raw data of microscopic images for different rock samples by using virtual petrographic microscopy (ViP), a cutting-edge methodology that is able to automatically scan entire thin sections in Gigapixel resolution under various polarization angle and illumination conditions. We proved that using ViP data will result in better segmentation result compared to single image acquisition. Image annotation, especially pixel-wise annotation is always a time-consuming and inefficient process. Moreover, it would be particularly challenging when to manually create dense semantic labels for ViP data in view of its size and dimensionality. To address this problem, we proposed a human-computer collaborative annotation pipeline where computers extract image boundaries by splitting images into superpixels, while human-annotators subsequently associate each superpixel manually with a class label with a single mouse click or brush stroke. This frees the human annotator from the burden of painstakingly delineating the exact boundaries of grains by hand and it has the potential to significantly speed up the annotation process. Instead of providing a discrete representation of images, superpixels are better aligned with region boundaries and largely reduce the image complexity. The use of superpixel segmentation in the annotation pipeline not only significantly reduce the manual workload for human annotators but also provides a significant dataset reduction by reducing the number of image primitives to operate on. In order to find the most suitable algorithms to generate superpixel segmentation, we evaluated state-of-art superpixel algorithms with regard to standard error metrics based on scanned ViP images and corresponding boundary maps traced by hand. We also proposed a novel adaption of the SLIC superpixel extraction algorithm that can cope with the multiple information layers of ViP data. We plan to use these superpixel algorithms in our pipeline to generate open data sets of several types of mineral thin sections for training of ML and DL algorithms.
Shear ruptures propagating along natural faults or simulated faults in analog laboratory experiments present a wide range of rupture velocities. Most ruptures propagate at velocities below the Rayleigh wave speed and Linear Elastic Fracture Mechanics (LEFM) theory has been shown to predict quantitatively well the observed propagation speed. However, early theoretical and numerical work suggested that ruptures may surpass the shear wave speed and propagate at velocities that can reach the longitudinal wave speed. This was later confirmed in laboratory experiments and observed as supershear earthquakes in nature. While the transition from sub-Rayleigh to supershear propagation has been studied extensively, current knowledge of propagation speed in the supershear regime is limited to a couple of idealistic set-ups. Here, we analyse the propagation speed of supershear ruptures along various nonuniform interfaces using simulations and experiments. We show that an approximate fracture mechanics theory describes well supershear rupture speeds as observed in our experiments and simulations. Furthermore, the theory uncovers a critical rupture length below which supershear propagation is impossible. Beyond this critical length, a rupture can sustain supershear propagation for arbitrarily low prestress levels if local non-uniformities cause transition. The presented theory provides a tool to better understand the potential for supershear ruptures in more realistic heterogeneous systems.
World Reference Base for Soil Resources (WRB) is an international soil classification system for naming soils and creating legends for soil maps. It is edited by a Working Group of the International Union of Soil Sciences (IUSS). The currently valid edition is the update 2015 of the third edition 2014. WRB has two hierarchical levels: The first level presents 32 Reference Soil Groups (RSGs), which are identified using a key. RSGs are groups of soils that have undergone similar pedogenesis or have been formed from similar parent material or represent major ecological regions. In the second level, the soil names are constructed by adding a set of qualifiers to the name of the RSG. For the second level, 185 qualifiers are defined. Some can be combined with many RSGs, others with only a few or even with just one. The qualifiers available for use with a particular RSG are listed in the key, along with the RSG. They are divided into principal and supplementary qualifiers. The principal qualifiers are regarded to be the most significant for the further characterization of soils of the particular RSG. They are ranked and given in an order of importance. The supplementary qualifiers are not ranked but used in alphabetical order. The definitions of RSGs and qualifiers are to a certain extent based on diagnostics: Diagnostic materials are materials that significantly influence pedogenic processes. Diagnostic properties are typical results of soil-forming processes or reflect special conditions of soil formation. Diagnostic horizons are typical results of soil-forming processes, but with a minimum thickness and therefore recognizable as horizontal layers. For naming a soil, all applying qualifiers must be listed in the soil name. For map legends, generalization is required, and the number of qualifiers depends on the scale and the purpose of the map. Qualifiers may be combined with specifiers to form subqualifiers for a further speciation of the qualifiers. WRB provides codes for the RSGs, qualifiers, and specifiers and syntax rules for the combination of the codes. At the end of this article, examples are provided for naming soils and creating map legends, including the use of codes.
Site characterization is key for seismic hazard analysis and risk mitigation but to obtain the subsurface properties is often a challenge. Over the last decades, increasing numbers of induced seismic events caused by gas production triggered the research on seismicity and site response in Groningen, the Netherlands. Waves are amplified within an approximately 800 m thick blanket of unconsolidated sediments at the surface. We have used both the ambient field and teleseismic arrivals to determine shear velocity profiles for this top soft sedimentary layer. Local shear wave velocity profiles are retrieved from H/V ratios of strong teleseismic events at 70 stations, each equipped with 1 accelerometer at the surface and 4 geophones at 50m depth intervals down to 200m. Using an existing shear velocity model for the top 200 meters, the body-wave H/V ratios are used to invert for the lower 600 meters of the soft sediments. Two borehole log datasets are used for calibration. From ambient noise data, we compute the probability density functions of the H/V ratios. Doing this for one month of data, results in stable mean H/V ratios. Based on the body- and surface wave forward modeling we find that the ambient field between 0.1 and 0.25 Hz is dominated by body waves, while at frequencies between 0.25 and 1.0 Hz, the higher mode Rayleigh waves are dominant. With this interpretation, a close relationship is observed between the H/V spectral ratios from the ambient field, S-wave resonance frequencies and the ellipticities of the Rayleigh waves. Furthermore, the good consistency between the earthquake and noise based models do confirm the robustness of the derived shear wave velocity profiles. The power of this study is the combination of body-and surface wave H/V ratios retrieved from multiple H/V approaches and the possibility to quality check the velocity profiles. These methods allow evaluation of the ambient seismic field. Furthermore, additional shallow impedance contrasts are determined. Such contrasts can cause additional wave amplification, which is important to take into account in site response modeling.
Turbidity currents are submarine density flows that very efficiently transport vast amounts of sediment towards the deep ocean. They are highly energetic and capable to damage human-made infrastructures laid on the seabed. They consecutively erode, transfer and deposit large volumes of sediment so that they eventually determine hydrocar-bon reservoir distribution and define environmental conditions of underwater habitats. The processes that control turbidity currents are, however, still poorly understood mainly because measuring and characterising them is ex-tremely challenging. This difficulty in monitoring turbidity currents in the field has prompted their study through other approaches such as numerical modelling. Numerical models can provide the highest temporal and spatial resolution of mechanisms that drive turbidity currents. These models also calculate in detail the stratigraphic effect that modelled turbidity currents induce on the seabed. Numerical models calculate these changes based on the for-mulation of equations which parameterize the processes that occur in the flow-seabed system. Defining adequate parameter values is key to obtaining results that either support or reject postulated hypotheses on the turbidity current behaviours and implications. We model a 3-dimensional low-density turbidity current in the deep ocean using, for the first time, the process-based model Delft3D software. This work shows the variation in flow behaviour and seabed composition when varying environmental parameters such as turbulence and sediment composition. We identify erosional and depo-sitional patterns and relate them to differences in flow characteristics. Further validation of this Delft3D model with experimental and field measurements of turbidity currents would offer an exceptional tool to confirm previ-ous hypotheses and reveal new insights into turbidity current and seafloor interactions, and into the morphological changes of the seafloor that can be expected in different spatial and temporal scales.
With the recent successful landing and the ongoing deployment of its instruments, the InSight mission will soon provide the first series of Martian seismic recordings. Such data will allow the present-day deep structure of the planet to be revealed. While inverse methods are powerful tools to achieve this goal, they are prone to two main limitations: the large size of the parameter space to be explored, and the non-uniqueness of the solution. This is particularly true for spatially uneven and relatively small amounts of seismic recordings, as in the case of InSight, which consists of a single station, and a precious, but limited amount of expected Marsquakes. To better constrain the interior of Mars using the upcoming InSight recordings, we have developed a Monte-Carlo Markov-Chain inversion of seismic data, where the modeling of Mars’ thermochemical history is part of the forward problem. Such a modeling accounts for the main Martian envelopes: an essentially metallic core surrounded by a convective silicate envelope, overlaid by a stagnant lithospheric lid. The latter includes a crust enriched in time-decaying, heat-producing radioactive elements. This thermo-chemical frame is integrated via a parametrized approach, allowing the long-term planetary evolution to be accurately modeled at a reasonable computational cost.
