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In many near-surface geophysical studies it is now common practice to collect co-located disparate geophysical data sets to explore subsurface structures. Reconstruction of physical parameter distributions underlying the available geophysical data sets usually requires the use of tomographic reconstruction techniques. To improve the quality of the obtained models, the information content of all data sets should be considered during the model generation process, e.g., by employing joint or cooperative inversion approaches. Here, we extend the zonal cooperative inversion methodology based on fuzzy c-means cluster analysis and conventional single-input data set inversion algorithms for the cooperative inversion of data sets with partially co-located model areas. This is done by considering recent developments in fuzzy c-means cluster analysis. Additionally, we show how supplementary a priori information can be incorporated in an automated fashion into the zonal cooperative inversion approach to further constrain the inversion. The only requirement is that this a priori information can be expressed numerically; e.g., by physical parameters or indicator variables. We demonstrate the applicability of the modified zonal cooperative inversion approach using synthetic and field data examples. In these examples, we cooperatively invert S- and P-wave traveltime data sets with partially co-located model areas using water saturation information expressed by indicator variables as additional a priori information. The approach results in a zoned multi-parameter model, which is consistent with all available information given to the zonal cooperative inversion and outlines the major subsurface units. In our field example, we further compare the obtained zonal model to sparsely available borehole and direct-push logs. This comparison provides further confidence in our zonal cooperative inversion model because the borehole and direct-push logs indicate a similar zonation.
Projected scenarios of climate change involve general predictions about the likely changes to the magnitude and frequency of landslides, particularly as a consequence of altered precipitation and temperature regimes. Whether such landslide response to contemporary or past climate change may be captured in differing scaling statistics of landslide size distributions and the erosion rates derived thereof remains debated. We test this notion with simple Monte Carlo and bootstrap simulations of statistical models commonly used to characterize empirical landslide size distributions. Our results show that significant changes to total volumes contained in such inventories may be masked by statistically indistinguishable scaling parameters, critically depending on, among others, the size of the largest of landslides recorded. Conversely, comparable model parameter values may obscure significant, i.e. more than twofold, changes to landslide occurrence, and thus inferred rates of hillslope denudation and sediment delivery to drainage networks. A time series of some of Earth's largest mass movements reveals clustering near and partly before the last glacial-interglacial transition and a distinct step-over from white noise to temporal clustering around this period. However, elucidating whether this is a distinct signal of first-order climate-change impact on slope stability or simply coincides with a transition from short-term statistical noise to long-term steady-state conditions remains an important research challenge.
The aims of this study were to identify areas of wind erosion and dust deposition and to quantify the effects of different grazing intensities on soil redistribution rates in grasslands based on the Cs-137 technique. Because the method uses a reference inventory as threshold for erosion or deposition, the classification of any other site as source or sink for dust depends on the accurate selection of this reference site.
Measurements of Cs-137 inventories and depth distributions were carried out at pasture sites with predominant species of Stipa grandis and Leymus chinensis which are grazed with different intensities. Additional measurements were made at arable land, plant-covered sand dunes and alluvial plains. Wind-induced soil erosion and dust deposition rates were calculated from Cs-137 inventories by means of the "Profile-Distribution" and the "Mass Balance II" models.
The selection of the reference site was based on fluid dynamical and process-determining parameters. The chosen site should meet the following four conditions: (i) located at a summit position with obviously low deposition rates, (ii) sufficient vegetation cover to prevent wind erosion, (iii) plane to exclude water erosion and (iv) in the wind/dust shadow of a higher elevation. The measured reference inventory of Cs-137 was 1967(+/- 102) Bqm(-2) located at a summit position of moderately grazed Leymus chinensis steppe. The Cs-137 inventories at other sites ranged from 1330 Bqm(-2) at heavily grazed sites to 5119 Bqm(-2) at river deposits, representing annual average soil losses of up to 130 tkm(-2) and deposits of up to 540 tkm(-2), respectively. The calculated annual averages of dust depositions at ungrazed Leymus chinensis sites were related to the dust storm frequencies of the last 50 years resulting in a description of the temporal variability of annual dust depositions from about 154 tkm(-2) in the 1960s to 26 tkm(-2) at recent times. Based on this quantification already 80% of the total dust depositions can be related to the 20 years between the 1960s and the end of the 1970s and only 20% to the time between 1980 and 2001.
Cs-137 technique is a promising method to assess the effect of grazing intensity and land use types on the spatial variability of wind-induced soil and dust redistribution processes in semi-arid grasslands. However, considerable efforts are needed to identify a reliable reference site, because erosion and deposition induced by wind may occur at the same places. The combination of the dust deposition rates derived from Cs-137 profile data with the dust storm frequencies is helpful for a better reconstruction of the temporal variability of dust deposition and wind erosion in this region. The calculated recent deposition rates of about 20 tkm(-2) are in good agreement with data of other authors.
Empirical species distribution models (SDMs) constitute often the tool of choice for the assessment of rapid climate change effects on species vulnerability. Conclusions regarding extinction risks might be misleading, however, because SDMs do not explicitly incorporate dispersal or other demographic processes. Here, we supplement SDMs with a dynamic population model 1) to predict climate-induced range dynamics for black grouse in Switzerland, 2) to compare direct and indirect measures of extinction risks, and 3) to quantify uncertainty in predictions as well as the sources of that uncertainty. To this end, we linked models of habitat suitability to a spatially explicit, individual-based model. In an extensive sensitivity analysis, we quantified uncertainty in various model outputs introduced by different SDM algorithms, by different climate scenarios and by demographic model parameters. Potentially suitable habitats were predicted to shift uphill and eastwards. By the end of the 21st century, abrupt habitat losses were predicted in the western Prealps for some climate scenarios. In contrast, population size and occupied area were primarily controlled by currently negative population growth and gradually declined from the beginning of the century across all climate scenarios and SDM algorithms. However, predictions of population dynamic features were highly variable across simulations. Results indicate that inferring extinction probabilities simply from the quantity of suitable habitat may underestimate extinction risks because this may ignore important interactions between life history traits and available habitat. Also, in dynamic range predictions uncertainty in SDM algorithms and climate scenarios can become secondary to uncertainty in dynamic model components. Our study emphasises the need for principal evaluation tools like sensitivity analysis in order to assess uncertainty and robustness in dynamic range predictions. A more direct benefit of such robustness analysis is an improved mechanistic understanding of dynamic species responses to climate change.
Tectonic and geological processes on Earth often result in structural anisotropy of the subsurface, which can be imaged by various geophysical methods. In order to achieve appropriate and realistic Earth models for interpretation, inversion algorithms have to allow for an anisotropic subsurface. Within the framework of this thesis, I analyzed a magnetotelluric (MT) data set taken from the Cape Fold Belt in South Africa. This data set exhibited strong indications for crustal anisotropy, e.g. MT phases out of the expected quadrant, which are beyond of fitting and interpreting with standard isotropic inversion algorithms. To overcome this obstacle, I have developed a two-dimensional inversion method for reconstructing anisotropic electrical conductivity distributions. The MT inverse problem represents in general a non-linear and ill-posed minimization problem with many degrees of freedom: In isotropic case, we have to assign an electrical conductivity value to each cell of a large grid to assimilate the Earth's subsurface, e.g. a grid with 100 x 50 cells results in 5000 unknown model parameters in an isotropic case; in contrast, we have the sixfold in an anisotropic scenario where the single value of electrical conductivity becomes a symmetric, real-valued tensor while the number of the data remains unchanged. In order to successfully invert for anisotropic conductivities and to overcome the non-uniqueness of the solution of the inverse problem it is necessary to use appropriate constraints on the class of allowed models. This becomes even more important as MT data is not equally sensitive to all anisotropic parameters. In this thesis, I have developed an algorithm through which the solution of the anisotropic inversion problem is calculated by minimization of a global penalty functional consisting of three entries: the data misfit, the model roughness constraint and the anisotropy constraint. For comparison, in an isotropic approach only the first two entries are minimized. The newly defined anisotropy term is measured by the sum of the square difference of the principal conductivity values of the model. The basic idea of this constraint is straightforward. If an isotropic model is already adequate to explain the data, there is no need to introduce electrical anisotropy at all. In order to ensure successful inversion, appropriate trade-off parameters, also known as regularization parameters, have to be chosen for the different model constraints. Synthetic tests show that using fixed trade-off parameters usually causes the inversion to end up by either a smooth model with large RMS error or a rough model with small RMS error. Using of a relaxation approach on the regularization parameters after each successful inversion iteration will result in smoother inversion model and a better convergence. This approach seems to be a sophisticated way for the selection of trade-off parameters. In general, the proposed inversion method is adequate for resolving the principal conductivities defined in horizontal plane. Once none of the principal directions of the anisotropic structure is coincided with the predefined strike direction, only the corresponding effective conductivities, which is the projection of the principal conductivities onto the model coordinate axes direction, can be resolved and the information about the rotation angles is lost. In the end the MT data from the Cape Fold Belt in South Africa has been analyzed. The MT data exhibits an area (> 10 km) where MT phases over 90 degrees occur. This part of data cannot be modeled by standard isotropic modeling procedures and hence can not be properly interpreted. The proposed inversion method, however, could not reproduce the anomalous large phases as desired because of losing the information about rotation angles. MT phases outside the first quadrant are usually obtained by different anisotropic anomalies with oblique anisotropy strike. In order to achieve this challenge, the algorithm needs further developments. However, forward modeling studies with the MT data have shown that surface highly conductive heterogeneity in combination with a mid-crustal electrically anisotropic zone are required to fit the data. According to known geological and tectonic information the mid-crustal zone is interpreted as a deep aquifer related to the fractured Table Mountain Group rocks in the Cape Fold Belt.
Triassic Latemar cycle tops - Subaerial exposure of platform carbonates under tropical arid climate
(2012)
The Triassic Latemar platform in the Dolomites, Italy, is the site of several ongoing controversies. Perhaps the most interesting debate focuses on apparent cyclic deposition within the Latemar platform, whose nature and duration are still open to debate. Further disagreement concerns the lack of meteoric diagenesis-related isotope shifts at cycle tops that bear circumstantial petrographic evidence for subaerial emergence. Here, an evaluation of the nature of Latemar cycle tops is presented combining evidence from previous work and new field, petrographic and geochemical data. Cycle tops are ranked according to increasing exposure duration and spatial extent: type I surfaces lacking unequivocal evidence of prolonged supratidal conditions; type II dolomite caps formed in warm, evaporitic, intertidal lagoonal waters followed by exposure of perhaps intermediate duration; type III clastic-rich, red calcareous horizons with some showing platform-wide extent, representing prolonged supratidal conditions, and type IV discontinuities in tepee belts, genetically related to type II and III surfaces, but likely representing shorter-lived exposure stages. Petrographic and geochemical criteria indicate that most diagenesis occurred in the shallow marine and burial domain whilst an extensive meteoric overprint of cycle tops is lacking. This is underlined by the scarcity of meteoric diagenetic fabrics such as gravitational cements that, where present, are here interpreted as marine-vadose in origin. The scarcity of carbon and oxygen isotope signatures commonly assigned to subaerial exposure stages is best explained in the context of mid-Triassic climate. The low latitude, tropical but arid setting of the Latemar, situated in the western extension of the Tethys ocean, its isolation from nearby continental areas and overall short-term emergence episodes are in agreement with a limited degree of meteoric alteration of most cycle tops. High amounts of aeolian clastic material beneath some cycle tops, along with high Fe and Mn elemental abundances argue for intermittent subaerial conditions. This study proposes an enhancement of the classical Allan and Matthews (1982) isotope model for subaerial exposure under strongly arid climates. As the subaerial exposure nature of Latemar cycle tops, and therefore eustasy as the cause for cyclicity, have been previously challenged due to the lack of meteoric-induced isotopic signatures, the outcome of this study is of significance for the ongoing Latemar stratigraphic controversy.
Earthquake-triggered landslide dams are potentially dangerous disrupters of water and sediment flux in mountain rivers, and capable of releasing catastrophic outburst flows to downstream areas. We analyze an inventory of 828 landslide dams in the Longmen Shan mountains, China, triggered by the M-w 7.9 2008 Wenchuan earthquake. This database is unique in that it is the largest of its kind attributable to a single regional-scale triggering event: 501 of the spatially clustered landslides fully blocked rivers, while the remainder only partially obstructed or diverted channels in steep watersheds of the hanging wall of the Yingxiu-Beichuan Fault Zone. The size distributions of the earthquake-triggered landslides, landslide dams, and associated lakes (a) can be modeled by an inverse gamma distribution; (b) show that moderate-size slope failures caused the majority of blockages; and (c) allow a detailed assessment of seismically induced river-blockage effects on regional water and sediment storage. Monte Carlo simulations based on volumetric scaling relationships for soil and bedrock failures respectively indicate that 14% (18%) of the estimated total coseismic landslide volume of 6.4 (14.6) x 10(9) m(3) was contained in landslide dams, representing only 1.4% of the >60,000 slope failures attributed to the earthquake. These dams have created storage capacity of similar to 0.6x 10(9) m(3) for incoming water and sediment. About 25% of the dams containing 2% of the total river-blocking debris volume failed one week after the earthquake; these figures had risen to 60% (similar to 20%), and >90% (>90%) within one month, and one:year, respectively, thus also emptying similar to 92% of the total potential water and sediment storage behind these, dams within one year following the earthquake. Currently only similar to 0.08 x 10(9) m(3) remain available as natural reservoirs for storing water and sediment, while similar to 0.19 x 10(9) m(3), i.e. about a third of the total river-blocking debris volume, has been eroded by rivers. Dam volume and upstream catchment area control to first order the longevity of the barriers, and bivariate domain plots are consistent with the observation that most earthquake-triggered landslide dams were ephemeral. We conclude that the river-blocking portion of coseismic slope failures disproportionately modulates the post-seismic sediment flux in the Longmen Shan on annual to decadal timescales.
The Maule earthquake of 27th February 2010 (M-w = 8.8) affected similar to 500 km of the Nazca-South America plate boundary in south-central Chile producing spectacular crustal deformation. Here, we present a detailed estimate of static coseismic surface offsets as measured by survey and continuous GPS, both in near- and far-field regions. Earthquake slip along the megathrust has been inferred from a Joint inversion of our new data together with published GPS, InSAR, and land-level changes data using Green's functions generated by a spherical finite-element model with realistic subduction zone geometry. The combination of the data sets provided a good resolution, indicating that most of the slip was well resolved. Coseismic slip was concentrated north of the epicenter with up to 16 m of slip, whereas to the south it reached over 10 m within two minor patches. A comparison of coseismic slip with the slip deficit accumulated since the last great earthquake in 1835 suggests that the 2010 event closed a mature seismic gap. Slip deficit distribution shows an apparent local overshoot that highlight cycle-to-cycle variability, which has to be taken into account when anticipating future events from interseismic observations. Rupture propagation was obviously not affected by bathymetric features of the incoming plate. Instead, splay faults in the upper plate seem to have limited rupture propagation in the updip and along-strike directions. Additionally, we found that along-strike gradients in slip are spatially correlated with geometrical inflections of the megathrust. Our study suggests that persistent tectonic features may control strain accumulation and release along subduction megathrusts.
The Seismic Hazard Harmonization in Europe (SHARE) project, which began in June 2009, aims at establishing new standards for probabilistic seismic hazard assessment in the Euro-Mediterranean region. In this context, a logic tree for ground-motion prediction in Europe has been constructed. Ground-motion prediction equations (GMPEs) and weights have been determined so that the logic tree captures epistemic uncertainty in ground-motion prediction for six different tectonic regimes in Europe. Here we present the strategy that we adopted to build such a logic tree. This strategy has the particularity of combining two complementary and independent approaches: expert judgment and data testing. A set of six experts was asked to weight pre-selected GMPEs while the ability of these GMPEs to predict available data was evaluated with the method of Scherbaum et al. (Bull Seismol Soc Am 99:3234-3247, 2009). Results of both approaches were taken into account to commonly select the smallest set of GMPEs to capture the uncertainty in ground-motion prediction in Europe. For stable continental regions, two models, both from eastern North America, have been selected for shields, and three GMPEs from active shallow crustal regions have been added for continental crust. For subduction zones, four models, all non-European, have been chosen. Finally, for active shallow crustal regions, we selected four models, each of them from a different host region but only two of them were kept for long periods. In most cases, a common agreement has been also reached for the weights. In case of divergence, a sensitivity analysis of the weights on the seismic hazard has been conducted, showing that once the GMPEs have been selected, the associated set of weights has a smaller influence on the hazard.