Impact of uncertainties of GOCE gravity model on crustal thickness estimates

In the last few years many studies have applied data of satellite gravity sensors for solid Earth applications. The use of different methodologies has been shown to result in large variations in crustal thickness even when using the same data as source. It is, however, difficult to estimate what is a significant difference between such models. Up to now the impact of the inherent uncertainty of Gravity Field and steady-state Ocean Circulation Explorer (GOCE) data on solid Earth applications has never been quantified. With this study we will provide uncertainty boundaries for crustal modelling based on the GOCE TIM5 covariance matrix. Different noise realizations have been calculated using a Monte Carlo-like simulation and added to the TIM5 model coefficients. The resulting differences in crustal thickness amount to maximum }0.2 km, which is less than 1 per cent of the total thickness, and much smaller than many other uncertainties involved in the inversion process.</p

Crustal structure beneath broad-band seismic stations in the Mediterranean region

We have analysed receiver functions to derive simple models for crustal structure below 12 broad-band seismological stations from the MIDSEA project and 5 permanent broad-band stations in the Mediterranean region including northern Africa. To determine an accurate Moho depth we have reduced the trade-off between crustal velocities and discontinuity depth using a new grid search method, which is an extension of recently published methods to determine crustal thickness. In this method the best fitting synthetic receiver function, containing both the direct conversion and the reverberated phases, is identified on a model grid of varying Moho depth and varying Poisson's ratio. The values we found for Moho depth range from around 20 km for intra-oceanic islands and extended continental margins to near 45 km in regions where the Eurasian and African continents have collided. More detailed waveform modelling shows that all receiver functions can be well fit using a 2- or 3-layer model containing a sedimentary layer and/or a mid-crustal discontinuity. On comparing our results with Moho maps inferred from interpolated reflection and refraction data, we find that for some regions the agreement between our receiver function analysis and existing Moho maps is very good, while for other regions our observations deviate from the interpolated map values and extend beyond the geographic bounds of these map

A spatial-spectral approach for visualization of vegetation stress resulting from pipeline leakage

Hydrocarbon leakage into the environment is a major problem with large economic and environmental impacts. Traditional methods for investigating seepage and pollution, such as drilling, are time consuming, destructive and expensive. Remote sensing has proved to be a tool that offers a non-destructive investigation method and has a significant added value to traditional methods. Optical remote sensing has been extensively tested for exploration of onshore hydrocarbon reservoirs and detection of hydrocarbons at the Earth's surface. Theoretically, remote sensing is a suitable tool for direct and indirect detection of the presence of hydrocarbons in the environment. In this research we investigate a leaking pipeline through analysis of hyperspectral imagery (HyMap). Due to inhomogeneous field cover, variations between fields turned out to be much larger than infield variations related to pollution issues. To overcome this problem a spatial-spectral normalization procedure was developed using moving kernels to enhance pollution related anomalies. The final results shows local anomalies which are likely related to hydrocarbon pollution.</p

Joint inversion of local, regional and teleseismic data for crustal thickness in the Eurasia-Africa plate boundary region

A new map for the Moho discontinuity (EAM02) in the Eurasia-Africa plate boundary region is presented. Reliable results have also been obtained for the southern and eastern Mediterranean Basin, the northern African coasts and the eastern Atlantic Ocean, regions only occasionally considered in studies on the Mediterranean region. The Moho topography model is derived from two independent sets of constraints. Information contained in the fundamental and higher-mode Rayleigh waves obtained from waveform modelling is used to constrain the Moho depth between estimates of crustal thickness taken from published reflection and refraction surveys, gravity studies and receiver function analysis. Strong lateral variations in the Moho topography have been observed in the Mediterranean Sea, confirming the complex evolution of this plate boundary region. In the west, the Moho discontinuity has been found at 15-20 km depth, suggesting extended and, at least in some locations, oceanic crust, while in the east the crust is on average 25-30 km thick. There it is interpreted either as Mesozoic oceanic or thinned Precambrian continental crust covered by thick sedimentary deposits. Standard continental crust (30-35 km) is observed along the eastern part of the northern African coast, while to the west a rapid change from a relatively deep Moho (down to 42 km) below the Atlas Mountain Range to the thin crust of the southwestern Mediterranean Sea has been found. The crust beneath the eastern North Atlantic Ocean can be up to 5 km thicker compared with standard oceanic crust (6 km). The crust has been interpreted to be heterogeneous as a consequence of irregular magma supply at the Mid-Atlantic ridge. In addition, serpentinization of the sub-Moho mantle could contribute to the imaging of apparently anomalous thick oceanic crust. In Europe, the presence of crustal roots (>45 km) beneath the major mountain belts has been confirmed, while thin crust (<25 km) has been found beneath extensional basins. Comparing the obtained Moho topography and Moho depth computed assuming isostatic compensation at 60 km depth shows that most of the Mediterranean and eastern Atlantic region appears to be in isostatic equilibrium. The large positive residuals observed for the eastern Mediterranean are likely to be due to overestimating crustal thickness, owing to the thick sediment deposits presen

Impact of mesh and DEM resolutions in SEM simulation of 3D seismic response

This study shows that the resolution of a digital elevation model (DEM) and model mesh strongly influences 3D simulations of seismic response. Topographic heterogeneity scatters seismic waves and causes variation in seismic response (am-plification and deamplification of seismic amplitudes) at the Earth’s surface. DEM resolution influences the accuracy and detail with which the Earth’s surface can be represented and hence affects seismic simulation studies. Apart from the spatial resolution of a DEM, the mesh resolution, adopted in the creation of a 3D spectral element meshing, also changes the detailedness of surface topography. Working with high-resolution data is in most cases not possible on a regional scale because of its costliness in terms of time, money, and computation. In this study, we evaluate how low the resolution of DEM and mesh can become before the results are significantly affected. We simulated models with different combinations of DEM and mesh resolutions. The peak ground displacement (PGD) obtained from these simulations was compared with the PGD of the model with the finest mesh and DEM resolution. Our results show that any mesh or DEM resolution of 540 m or coarser will give unrealistic results. These results are valid for similar terrains as studied here and might not be directly applicable to regions with significantly different topography

Using Google Earth engine for geological remote sensing

Geological remote sensing is an essential tool in mineral exploration, employing satellite, airborne, and drone-based imagery to identify and analyse potential mineral deposits from a distance. This technique allows geologists to survey vast and often inaccessible areas, identifying critical geological features indicative of minerals without needing immediate physical sampling. Remote sensing geology is possible in areas with “good exposure”, which typically refers to arid and semi-arid areas. Surface cover is often considered to be invariant (or only changing on a geological timescale), which holds for rocks and minerals and, to a lesser degree, for soils. What of course does change over time is the acquisition environment, driven by seasonal change and the weather. Mapping at regional scale however requires an image collection acquired over a longer time span, and possibly including temperate and cultivated regions. For small-scale studies, data acquired at a single moment seem to suffice, but results still differ from image to image.Sensors with a continuous multi-temporal operation (e.g. Landsat 8 OLI and Sentinel-2 MSI) enable to monitor land surface processes over time, but also allow to choose an optimal moment of seasonal acquisition. Handling the vast amount of data from ESA and NASA’s earth observation programmes has led to the development of cloud-based processing environments. This presentation will shed a light on the use of Google Earth Engine for mapping surface dynamics and studying the influence of time on geological remote sensing results