An objective approach for feature extraction: distribution analysis and statistical descriptors for scale choice and channel network identification

A statistical approach to LiDAR derived topographic attributes for the automatic extraction of channel network and for the choice of the scale to apply for parameter evaluation is presented in this paper. The basis of this approach is to use distribution analysis and statistical descriptors to identify channels where terrain geometry denotes significant convergences. Two case study areas with different morphology and degree of organization are used with their 1 m LiDAR Digital Terrain Models (DTMs). Topographic attribute maps (curvature and openness) for various window sizes are derived from the DTMs in order to detect surface convergences. A statistical analysis on value distributions considering each window size is carried out for the choice of the optimum kernel. We propose a three-step method to extract the network based (a) on the normalization and overlapping of openness and minimum curvature to highlight the more likely surface convergences, (b) a weighting of the upslope area according to these normalized maps to identify drainage flow paths and flow accumulation consistent with terrain geometry, (c) the standard score normalization of the weighted upslope area and the use of standard score values as non subjective threshold for channel network identification. As a final step for optimal definition and representation of the whole network, a noise-filtering and connection procedure is applied. The advantage of the proposed methodology, and the efficiency and accurate localization of extracted features are demonstrated using LiDAR data of two different areas and comparing both extractions with field surveyed networks

Large scale characterization of artificial drainage networks: new opportinities from high resolution topography .

openTES-81

Peeking beyond peaks:Challenges and research potentials of continuous multimodal multi-objective optimization

Multi-objective (MO) optimization, i.e., the simultaneous optimization of multiple conflicting objectives, is gaining more and more attention in various research areas, such as evolutionary computation, machine learning (e.g., (hyper-)parameter optimization), or logistics (e.g., vehicle routing). Many works in this domain mention the structural problem property of multimodality as a challenge from two classical perspectives: (1) finding all globally optimal solution sets, and (2) avoiding to get trapped in local optima. Interestingly, these streams seem to transfer many traditional concepts of single-objective (SO) optimization into claims, assumptions, or even terminology regarding the MO domain, but mostly neglect the understanding of the structural properties as well as the algorithmic search behavior on a problem's landscape. However, some recent works counteract this trend, by investigating the fundamentals and characteristics of MO problems using new visualization techniques and gaining surprising insights. Using these visual insights, this work proposes a step towards a unified terminology to capture multimodality and locality in a broader way than it is usually done. This enables us to investigate current research activities in multimodal continuous MO optimization and to highlight new implications and promising research directions for the design of benchmark suites, the discovery of MO landscape features, the development of new MO (or even SO) optimization algorithms, and performance indicators. For all these topics, we provide a review of ideas and methods but also an outlook on future challenges, research potential and perspectives that result from recent developments.</p

Evaluating the Differences of Gridding Techniques for Digital Elevation Models Generation and Their Influence on the Modeling of Stony Debris Flows Routing: A Case Study From Rovina di Cancia Basin (North-Eastern Italian Alps)

Debris \ufb02ows are among the most hazardous phenomena in mountain areas. To cope with debris \ufb02ow hazard, it is common to delineate the risk-prone areas through routing models. The most important input to debris \ufb02ow routing models are the topographic data, usually in the form of Digital Elevation Models (DEMs). The quality of DEMs depends on the accuracy, density, and spatial distribution of the sampled points; on the characteristics of the surface; and on the applied gridding methodology. Therefore, the choice of the interpolation method affects the realistic representation of the channel and fan morphology, and thus potentially the debris \ufb02ow routing modeling outcomes. In this paper, we initially investigate the performance of common interpolation methods (i.e., linear triangulation, natural neighbor, nearest neighbor, Inverse Distance to a Power, ANUDEM, Radial Basis Functions, and ordinary kriging) in building DEMs with the complex topography of a debris \ufb02ow channel located in the Venetian Dolomites (North-eastern Italian Alps), by using small footprint full- waveform Light Detection And Ranging (LiDAR) data. The investigation is carried out through a combination of statistical analysis of vertical accuracy, algorithm robustness, and spatial clustering of vertical errors, and multi-criteria shape reliability assessment. After that, we examine the in\ufb02uence of the tested interpolation algorithms on the performance of a Geographic Information System (GIS)-based cell model for simulating stony debris \ufb02ows routing. In detail, we investigate both the correlation between the DEMs heights uncertainty resulting from the gridding procedure and that on the corresponding simulated erosion/deposition depths, both the effect of interpolation algorithms on simulated areas, erosion and deposition volumes, solid-liquid discharges, and channel morphology after the event. The comparison among the tested interpolation methods highlights that the ANUDEM and ordinary kriging algorithms are not suitable for building DEMs with complex topography. Conversely, the linear triangulation, the natural neighbor algorithm, and the thin-plate spline plus tension and completely regularized spline functions ensure the best trade-off among accuracy and shape reliability. Anyway, the evaluation of the effects of gridding techniques on debris \ufb02ow routing modeling reveals that the choice of the interpolation algorithm does not signi\ufb01cantly affect the model outcomes

Hydrological impacts of climate change at catchment scale : a case study in the Grand-Duchy of Luxembourg

As a consequence of an increase of days with westerly atmospheric fluxes, bringing humid air masses from the Atlantic Ocean to Western Europe, important changes in the annual and seasonal distribution of rainfall have been observed over the past 150 years. Annual rainfall totals observed during the second half of the 19th century were less important than those observed during the second half of the 20th century. Moreover, during the past 50 years winter rainfall totals have significantly increased, while summer rainfall totals have been decreasing. Streamflow observations through the second half of the 20th century have shown a significant increase of winter maximum daily streamflow, in reaction to the winter rainfall increase. The modelling of the streamflow under the 19th century climatological conditions suggests that since then, the number of winter flood days has increased, while the occurrence of summer flood days has decreased. Moreover, high floods appear to have been more frequent in the second half of the 20th century

Seismic geometric attribute analysis for fracture characterization: New methodologies and applications

In 3D subsurface exploration, detection of faults and fractures from 3D seismic data is vital to robust structural and stratigraphic analysis in the subsurface, and great efforts have been made in the development and application of various seismic attributes (e.g. coherence, semblance, curvature, and flexure). However, the existing algorithms and workflows are not accurate and efficient enough for robust fracture detection, especially in naturally fractured reservoirs with complicated structural geometry and fracture network. My Ph.D. research is proposing the following scopes of work to enhance our capability and to help improve the resolution on fracture characterization and prediction.;For discontinuity attribute, previous methods have difficulty highlighting subtle discontinuities from seismic data in cases where the local amplitude variation is non-zero mean. This study proposes implementing a gray-level transformation and the Canny edge detector for improved imaging of discontinuities. Specifically, the new process transforms seismic signals to be zero mean and helps amplify subtle discontinuities, leading to an enhanced visualization for structural and stratigraphic details. Applications to various 3D seismic datasets demonstrate that the new algorithm is superior to previous discontinuity-detection methods. Integrating both discontinuity magnitude and discontinuity azimuth helps better define channels, faults and fractures, than the traditional similarity, amplitude gradient and semblance attributes.;For flexure attribute, the existing algorithm is computationally intensive and limited by the lateral resolution for steeply-dipping formations. This study proposes a new and robust volume-based algorithm that evaluate flexure attribute more accurately and effectively. The algorithms first volumetrically fit a cubic surface by using a diamond 13-node grid cell to seismic data, and then compute flexure using the spatial derivatives of the built surface. To avoid introducing interpreter bias, this study introduces a new workflow for automatically building surfaces that best represent the geometry of seismic reflections. A dip-steering approach based on 3D complex seismic trace analysis is implemented to enhance the accuracy of surface construction and to reduce computational time. Applications to two 3D seismic surveys demonstrate the accuracy and efficiency of the new flexure algorithm for characterizing faults and fractures in fractured reservoirs.;For robust fracture detection, this study presents a new methodology to compute both magnitude and directions of most extreme flexure attribute. The new method first computes azimuthal flexure; and then implements a discrete azimuth-scanning approach to finding the magnitude and azimuth of most extreme flexure. Specially, a set of flexure values is estimated and compared by substituting all possible azimuths between 0 degree (Inline) and 180 degree (Crossline) into the newly-developed equation for computing azimuthal flexure. The added value of the new algorithm is demonstrated through applications to the seismic data set from Teapot Dome of Wyoming. The results indicate that most extreme flexure and its associated azimuthal directions help reveal structural complexities that are not discernible from conventional coherence or geometric attributes.;Given that the azimuth-scanning approach for computing maximum/minimum flexure is time-consuming, this study proposes fracture detection using most positive/negative flexures; since for gently-dipping structures, most positive is similar to maximum flexure while most negative flexure to minimum flexure. After setting the first reflection derivatives (or apparent dips) to be zero, the localized reflection is rotated to be horizontal and thereby the equation for computing azimuthal flexure is significantly simplified, from which a new analytical approach is proposed for computing most positive/negative flexures. Comparisons demonstrate that positive/negative flexures can provide quantitative fracture characterization similar to most extreme flexure, but the computation is 8 times faster than the azimuth-scanning approach.;Due to the overestimate by using most positive/negative flexure for fracture characterization, 3D surface rotation is then introduced for flexure extraction in the presence of structural dip, which makes it possible for solving the problem in an analytical manner. The improved computational efficiency and accuracy is demonstrated by both synthetic testing and applications to real 3D seismic datasets, compared to the existing discrete azimuth-scanning approach.;Last but not the least, strain analysis is also important for understanding structural deformation, predicting natural fracture system, and planning well bores. Physically, open fractures are most likely to develop in extensional domains whereas closed fractures in compressional ones. The beam model has been proposed for describing the strain distribution within a geological formation with a certain thickness, in which, however, the extensional zone cannot be distinguished from the compression one with the aid of traditional geometric attributes, including discontinuity, dip, and curvature. To resolve this problem, this study proposes a new algorithm for strain reconstruction using apparent dips at each sample location within a seismic cube