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英国论文代写范文精选-Analysis of geological and geomorphological features

2016-06-14 | 来源:51due教员组 | 类别:更多范文

51Due英国论文代写网精选assignment代写范文:“Analysis of geological and geomorphological features”,这篇论文讨论了地貌和地质特征的分析。数字高程度模型已经从根本上改变了我们对高程度信息的分析方式。因为地质现象是由不同的地貌构成的,所以地貌多尺度分析方法是至关重要的。事实上,我们的环境是频率信息描述的空间域或时间域。真正的地质现象包括形成过程和结构元素的嵌套,它们相互依赖,相互交错。

Digital elevation models have fundamentally changed the way we perceive elevation information. In the late 1990s, the emergence of high resolution elevation data allowed the exploration of our environment and its morphology with an unprecedented level of detail, making new applications possible.

The visual analysis of a shaded DEM efficiently supports the detection of a great amount of features at various scales. Nowadays, earth-Science experts such as geologists and geomorphologists use high resolution DEMs to visually assess geomorphological features.

Thanks to the finer description offered by this new and rich source of information, the study of visual perception, i.e. the visible phenomena or relevant structures, has evolved considerably. In-deed, it is now possible for instance to visually analyze a hillside and its details.

Since geological phenomena are composed of different nested topographical features, a multiscale approach is essential in geomorphological analysis. Klinkenberg suggested that a phenomenon fits over scales and that its features are nested in discrete scale intervals, leading to a strong correlation between features and phenomena.

In human vision, the neural network is able to distinguish specific features in relation to a corresponding scale, as well as to carry out a multiresolution analysis. As suggested byMarr, our visual system is probably linked to tuned cellsor, in other words, it has specific frequency intervals to which it is sensitive.

Therefore, computer systems and the visual representations we make of processed data should reflect this multiscale nature. Nevertheless, in most current systems, information is perceived like a static image. There are two ways to interpret inform action in an image: either we know what the image contains and we focus on retrieving this information using the most appropriate technique; or we do not know what the image contains and we use a more general method to identify and differentiate relevant content in the data.

The latter applies to DEM analysis. Often, a given DEM contains specific information like a geological phenomenon, but the identification of its components is not straightforward. Consequently, topographical features have to be classified according to their representative scale. In other words, it is necessary to find the best correlation between a certain level of generalization and the scale of a particular feature of the topography. This process is called a multiscale analysis of structural topographical features.

High resolution DEMs offer the base conditions to perform such an analysis. High resolution describes very fine structural levels that stem from different processes. For example, a micro-fold may result from a landslide or from erosion.

Thus, the imprint of this feature will not be contained in the same spatial context regarding its relation to coarser features. Although high resolution provides a much better visual rendering of the territory and of its structures, the relations between topographical structures and formations are more complicated. Hence they represent a new challenge for quantitative geomorphology and geomorphometry.

A first attempt to address this challenge was carried out through the development of form indicators, also known as geomorphometry indicators. Geomorphometric indicators are spatial features that can be extracted from a DEM, such as slope, aspect, and curvature. These indicators are geometric because they are computed using the adjustment of a mathematical surface on elevation models.

The detection of geomorphological features using this type of indicators is complicated because such indices are dedicated to local scale analysis only. Moreover, in high resolution DEMs, features are nested one into the other, making the interpretation of indicators difficult. This is a computational scale problem also observed in the use of geomorphometric indicators for the prediction of environmental parameters such as wind speed and orographic precipitation.

Wilson and Gallant showed that the characterization of landscape processes and features based on one specific scale is far too simple to model our environment. In recent years, multiresolution analysis tools based on a generalization of Evans' geomorphometric indicators have been developed.

These tools provide multiple results for one indicator at multiple scales. There is no feature extraction, but rather a multiscale/multiresolution topographical analysis and the extraction of a geometric network. These methods rely essentially on a geometrical analysis, while Jordan and colleagues combined geomorphometric indicators and digital image processing techniques to detect tectonic faults with DEMs.

To enable the detection of a phenomenon and its underlying features, it is necessary to identify the specific scales at which significant features and their intrinsic relations emerge. A way to move toward the extraction of geomorphological indicators at different scales is to consider DEMs in the frequency domain.

Indeed, our environment is composed of frequency information characterizing either the spatial domain or the temporal domain. A limitation is that almost no natural phenomenon related to Earth science is stationary and homogeneous, and this made many studies fail. Real phenomena consist of a nesting of processes and structural elements, which are inter-dependent, have various scales, and interact in the natural system. This means that the size and shape of every structure depends on the phenomenon it belongs to, but also on the functional scale of the system.

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