Satellite SAR Interferometry for Earth’s Crust Deformation Monitoring and Geological Phenomena Analysis

Synthetic aperture radar interferometry (InSAR) and the related processing techniques provide a unique tool for the quantitative measurement of the Earth’s surface deformation associated with certain geophysical processes (such as volcanic eruptions, landslides and earthquakes), thus making possible long-term monitoring of surface deformation and analysis of relevant geodynamic phenomena. This chapter provides an application-oriented perspective on the spaceborne InSAR technology with emphasis on subsequent geophysical investigations. First, the fundamentals of radar interferometry and differential interferometry, as well as error sources, are briefly introduced. Emphasis is then placed on the realistic simulation of the underlying geophysics processes, thus offering an unfolded perspective on both analytical and numerical approaches for modeling deformation sources. Finally, various experimental investigations conducted by acquiring SAR multitemporal observations on areas subject to deformation processes of particular geological interest are presented and discussed

NONLINEAR INVERSION STRATEGIES APPLIED TO SOURCE CHARACTERIZATION AND 3D EARTHQUAKE TOMOGRAPHY IN VOLCANIC ENVIRONMENTS: A CASE STUDY AT PACAYA VOLCANO, GUATEMALA

Full-waveform moment tensor inversion of volcanic seismic signals and travel-time 3D tomography of local earthquakes have been widely used to explore source processes related to magma transport as well as to image the location and size of magma storage systems. However, the inversion solutions and the associated reliability estimates are non-unique and bear intrinsic uncertainties due to simplifying assumptions about the source, inaccuracies in the velocity models, dependence on network configuration, and other a priori constraints imposed by the modeler. This work addresses the non-uniqueness and uncertainties of the model results by introducing non-linear inversion techniques that allow sampling the model space more effectively. We developed a nonlinear inversion approach for source type that uses a grid search over all possible moment tensor types and orientations to obtain a quantitative measure of the source mechanism reliability. For the tomography inverse problem, the solution space is fully explored using a ‘guided’ Monte-Carlo method in which starting velocity models are randomly selected and, through simulating annealing, only a subset of models that satisfies acceptability criteria is retained. Extensive synthetic tests are employed to test and validate the nonlinear inversion methods. The inversion procedures are then put into practice at Pacaya volcano, Guatemala. First, nonlinear moment tensor inversion is applied to explosion-related, long-period events that were recorded during a temporary installation of four broadband seismic stations in October-November 2013. The derived source reflects a shallow crack-like mechanism that is likely related to bubble-bursting events at the summit. Secondly, nonlinear travel-time local 3D tomography is employed to invert hundreds of local events that were detected during another temporary seismic network installation in January 2015. Re-location of the events using a 3D velocity model reveals the presence of a straight conduit possibly connecting a shallow magma reservoir to the surface. The inversion approaches proposed in this study allow a comprehensive assessment of the model solution space. This is revealed to be of crucial aid in the determination of the confidence level of model interpretations, especially in cases like Pacaya, where availability of resources and observational data is limited

A Methodology for Planning Road Best Management Practices Combining WEPP: Road Erosion Modeling and Simulated Annealing Optimization

Erosion from forest roads is a known problem in mountainous terrain. To abate these negative consequences, physical Best Management Practices (BMPs) are implemented, sometimes with no knowledge of erosion hot spots. With the need to minimize water quality impacts while at the same time accounting for multiple considerations and constraints, road BMP planning at the watershed scale is a difficult task. To assist in this planning process, a methodology is presented here that combines WEPP: Road erosion predictions with simulated annealing optimization. Under this methodology, erosion predictions associated with BMP options for a segment comprise the objective function of an optimization problem. This methodology was tested on a watershed in the Lake Tahoe Basin. WEPP: Road input data was gathered through road surveys. Modeling results predicted relatively little sediment leaving the forest buffer, as a result of numerous well-maintained BMPs and the dry climate found in the watershed. A sensitivity analysis for all WEPP: Road input parameters is presented, which provides insight into the general applicability of these erosion estimates as well as the relative importance of each input parameter. After evaluating erosion risk across the entire watershed, applicable BMPs were assigned to problem road segments and WEPP: Road was used to predict change in sediment leaving the buffer with BMP implementation at a given site. These predictions, combined with budget constraints as well as equipment scheduling considerations, were incorporated into an algorithm using simulated annealing as its optimization engine. Three modeled scenarios demonstrate the viability of this methodology in reducing total sediment leaving the road buffer over a planning horizon. Of the 173 segments surveyed, 38 segments could be treated using generic BMPs. For all three scenarios, BMP-SA reduced sediment leaving the buffer by as much as 70% over the course of a 20-year planning horizon. For the 38 segments treated with BMPs, sediment was reduced by greater than 90% over the planning horizon. This methodology is a viable approach for streamlining watershed-scale road network BMP planning, despite its heavy reliance on road erosion estimates

3rd Probabilistic Workshop Technical Systems, Natural Hazards

Modern engineering structures should ensure an economic design, construction and operation of structures in compliance with the required safety for persons and the environment. In order to achieve this aim, all contingencies and associated consequences that may possibly occur throughout the life cycle of the considered structure have to be taken into account. Today, the development is often based on decision theory, methods of structural reliability and the modeling of consequences. Failure consequences are one of the significant issues that determine optimal structural reliability. In particular, consequences associated with the failure of structures are of interest, as they may lead to significant indirect consequences, also called follow-up consequences. However, apart from determining safety levels based on failure consequences, it is also crucially important to have effective models for stress forces and maintenance planning ... (aus dem Vorwort

Math Models and Heuristic Methods for Constructing Fair Political Districts

Political parties’ attempts to manipulate district boundaries in order to gain political advantages in the election system lead to huge inefficiency and unfair election results. Previously some studies have developed methods for forming political districts considering various factors such as population equality, compactness, and contiguity; but only a few recent studies have considered political fairness as an objective for redistricting political map.This study attempts to find a solution to draw political districts using political fairness as a factor in addition to integrity, population equality, contiguity, and compactness of the districts in order to prevent gerrymandering. In this research, we introduce two new metrics to measure political fairness that supplement efficiency gap which is the standard measure of political fairness. We then develop several mathematical models, that address various aspects of political redistricting to form state assembly, senate and congressional maps. Due to several drawbacks in these models, a heuristic methodology – in particular simulated annealing (SA) algorithm – is ultimately utilized to find a good solution for this problem. The algorithm is coded in C++ and then tested on three large scenarios. The first is a fictional rectangular state having 3000 wards. The second and third scenarios focus on combining nearly 7000 election wards to form U.S. Congressional and state legislative districts in Wisconsin respectively. The results for the Wisconsin scenarios are displayed as maps that are created using state-of-the-art ArcGIS software. A significant data collection and cleaning effort was undertaken before the Wisconsin scenarios were considered. Experimental results demonstrate the effectiveness of the proposed heuristic method, the efficiency of political redistricting problems in general, and the inevitable trade-off that are made between competing objectives in this highly challenging real-world problem

Bio-inspired optimization in integrated river basin management

Water resources worldwide are facing severe challenges in terms of quality and quantity. It is essential to conserve, manage, and optimize water resources and their quality through integrated water resources management (IWRM). IWRM is an interdisciplinary field that works on multiple levels to maximize the socio-economic and ecological benefits of water resources. Since this is directly influenced by the river’s ecological health, the point of interest should start at the basin-level. The main objective of this study is to evaluate the application of bio-inspired optimization techniques in integrated river basin management (IRBM). This study demonstrates the application of versatile, flexible and yet simple metaheuristic bio-inspired algorithms in IRBM. In a novel approach, bio-inspired optimization algorithms Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) are used to spatially distribute mitigation measures within a basin to reduce long-term annual mean total nitrogen (TN) concentration at the outlet of the basin. The Upper Fuhse river basin developed in the hydrological model, Hydrological Predictions for the Environment (HYPE), is used as a case study. ACO and PSO are coupled with the HYPE model to distribute a set of measures and compute the resulting TN reduction. The algorithms spatially distribute nine crop and subbasin-level mitigation measures under four categories. Both algorithms can successfully yield a discrete combination of measures to reduce long-term annual mean TN concentration. They achieved an 18.65% reduction, and their performance was on par with each other. This study has established the applicability of these bio-inspired optimization algorithms in successfully distributing the TN mitigation measures within the river basin. Stakeholder involvement is a crucial aspect of IRBM. It ensures that researchers and policymakers are aware of the ground reality through large amounts of information collected from the stakeholder. Including stakeholders in policy planning and decision-making legitimizes the decisions and eases their implementation. Therefore, a socio-hydrological framework is developed and tested in the Larqui river basin, Chile, based on a field survey to explore the conditions under which the farmers would implement or extend the width of vegetative filter strips (VFS) to prevent soil erosion. The framework consists of a behavioral, social model (extended Theory of Planned Behavior, TPB) and an agent-based model (developed in NetLogo) coupled with the results from the vegetative filter model (Vegetative Filter Strip Modeling System, VFSMOD-W). The results showed that the ABM corroborates with the survey results and the farmers are willing to extend the width of VFS as long as their utility stays positive. This framework can be used to develop tailor-made policies for river basins based on the conditions of the river basins and the stakeholders' requirements to motivate them to adopt sustainable practices. It is vital to assess whether the proposed management plans achieve the expected results for the river basin and if the stakeholders will accept and implement them. The assessment via simulation tools ensures effective implementation and realization of the target stipulated by the decision-makers. In this regard, this dissertation introduces the application of bio-inspired optimization techniques in the field of IRBM. The successful discrete combinatorial optimization in terms of the spatial distribution of mitigation measures by ACO and PSO and the novel socio-hydrological framework using ABM prove the forte and diverse applicability of bio-inspired optimization algorithms

Reinforcement Learning for Machine Translation: from Simulations to Real-World Applications

If a machine translation is wrong, how we can tell the underlying model to fix it? Answering this question requires (1) a machine learning algorithm to define update rules, (2) an interface for feedback to be submitted, and (3) expertise on the side of the human who gives the feedback. This thesis investigates solutions for machine learning updates, the suitability of feedback interfaces, and the dependency on reliability and expertise for different types of feedback. We start with an interactive online learning scenario where a machine translation (MT) system receives bandit feedback (i.e. only once per source) instead of references for learning. Policy gradient algorithms for statistical and neural MT are developed to learn from absolute and pairwise judgments. Our experiments on domain adaptation with simulated online feedback show that the models can largely improve under weak feedback, with variance reduction techniques being very effective. In production environments offline learning is often preferred over online learning. We evaluate algorithms for counterfactual learning from human feedback in a study on eBay product title translations. Feedback is either collected via explicit star ratings from users, or implicitly from the user interaction with cross-lingual product search. Leveraging implicit feedback turns out to be more successful due to lower levels of noise. We compare the reliability and learnability of absolute Likert-scale ratings with pairwise preferences in a smaller user study, and find that absolute ratings are overall more effective for improvements in down-stream tasks. Furthermore, we discover that error markings provide a cheap and practical alternative to error corrections. In a generalized interactive learning framework we propose a self-regulation approach, where the learner, guided by a regulator module, decides which type of feedback to choose for each input. The regulator is reinforced to find a good trade-off between supervision effect and cost. In our experiments, it discovers strategies that are more efficient than active learning and standard fully supervised learning

Integrated multi-scale methods for modeling the deformation field of volcanic sources

The modeling of volcanic deformation sources represents a crucial task for investigating and monitoring the activity of magmatic systems. In this framework, inverse methods are the most used approach to image deforming volcanic bodies by considering the assumptions of the elasticity theory. However, several issues affect the inverse modeling and the interpretation of the ground deformation phenomena, such as the inherent ambiguity, the theoretical ambiguity and the related choice of the forward problem. Despite assuming appropriate a priori information and constraints, we are led to an ambiguous estimate of the physical and geometrical parameters of volcanic bodies and, in turn, to an unreliable analysis of the hazard evaluation and risk assessment. In this scenario, we propose a new approach for the interpretation of the large amount of deformation data retrieved by the SBAS-DInSAR technique in volcanic environments. The proposed approach is based on the assumptions of the homogeneous and harmonic elastic fields, which satisfy the Laplace's equation; specifically, we consider Multiridge, ScalFun and THD methods to provide in a fast way preliminary information on the active volcanic source, even for the analysis of complex cases, such as the depth, the horizontal position, the geometrical configuration and the horizontal extent. In this thesis, firstly we analyse the biharmonic general solution of the elastic problem to state the deformation field surely satisfy the Laplace's equation in the case of hydrostatic pressure condition within a source embedded in a homogeneous elastic half-space. Then, we show the results of different simulations by highlighting how the proposed approach allows overcoming many ambiguities since it provides unique information about the geometrical parameters of the active source. Finally, we show the results of Multiridge, ScalFun and THD methods used for the analysis of the deformation components recorded at Okmok volcano, Uturuncu volcano, Campi Flegrei caldera, Fernandina volcano and Yellowstone caldera. We conclude this thesis by remarking the proposed approach represents a crucial tool for fixing modeling ambiguities and to provide useful information for monitoring purposes and/or for constraining the geometry of the volcanic systems