Application of Artificial Intelligence in predicting earthquakes: state-of-the-art and future challenges

Predicting the time, location and magnitude of an earthquake is a challenging job as an earthquake does not show specific patterns resulting in inaccurate predictions. Techniques based on Artificial Intelligence (AI) are well known for their capability to find hidden patterns in data. In the case of earthquake prediction, these models also produce a promising outcome. This work systematically explores the contributions made to date in earthquake prediction using AI-based techniques. A total of 84 scientific research papers, which reported the use of AI-based techniques in earthquake prediction, have been selected from different academic databases. These studies include a range of AI techniques including rule-based methods, shallow machine learning and deep learning algorithms. Covering all existing AI-based techniques in earthquake prediction, this paper provides an account of the available methodologies and a comparative analysis of their performances. The performance comparison has been reported from the perspective of used datasets and evaluation metrics. Furthermore, using comparative analysis of performances the paper aims to facilitate the selection of appropriate techniques for earthquake prediction. Towards the end, it outlines some open challenges and potential research directions in the field

A disaster response model driven by spatial-temporal forecasts

In this research, we propose a disaster response model combining preparedness and responsiveness strategies. The selective response depends on the level of accuracy that our forecasting models can achieve. In order to decide the right geographical space and time window of response, forecasts are prepared and assessed through a spatial–temporal aggregation framework, until we find the optimum level of aggregation. The research considers major earthquake data for the period 1985–2014. Building on the produced forecasts, we develop accordingly a disaster response model. The model is dynamic in nature, as it is updated every time a new event is added in the database. Any forecasting model can be optimized though the proposed spatial–temporal forecasting framework, and as such our results can be easily generalized. This is true for other forecasting methods and in other disaster response contexts

Recommendations for the quantitative analysis of landslide risk

This paper presents recommended methodologies for the quantitative analysis of landslide hazard, vulnerability and risk at different spatial scales (site-specific, local, regional and national), as well as for the verification and validation of the results. The methodologies described focus on the evaluation of the probabilities of occurrence of different landslide types with certain characteristics. Methods used to determine the spatial distribution of landslide intensity, the characterisation of the elements at risk, the assessment of the potential degree of damage and the quantification of the vulnerability of the elements at risk, and those used to perform the quantitative risk analysis are also described. The paper is intended for use by scientists and practising engineers, geologists and other landslide experts.JRC.H.5-Land Resources Managemen

구조물의 지진 취약도 해석 고도화: 지진강도척도 및 지진동 선택 알고리즘 개발

학위논문 (석사)-- 서울대학교 대학원 : 건설환경공학부, 2017. 2. 송준호.Structural collapse is the dominant cause of deaths and injuries under seismic excitation. Thus, collapse prevention of building during strong earthquake is the most important design objective of modern seismic design provisions to promote life-safety and to prevent socio-economic losses. In order to ensure an acceptably small likelihood of structural collapse under the earthquake load, nonlinear dynamic analysis coupled with probabilistic seismic hazard analysis is needed. However, nonlinear structural responses under seismic excitation vary greatly even if ground motions are scaled to get the same level of intensity measure (e.g., ground motions are scaled to get the same spectral acceleration at first mode period of structure). Furthermore, a large set of ground motions are needed for comprehensive reflection of hazard characteristics at a given site, which incurs high computational cost during dynamic analyses. To reduce the variability of structural responses as well as the number of ground motion time series used in nonlinear stochastic analyses, the study aims to develop a new seismic intensity measure by combining a cumulative IM, e.g. Arias intensity (Arias 1970) and a peak IM, e.g. spectral acceleration, and a new algorithm about selecting ground motion time series for IDA. To this end, various techniques of statistical methods such as linear regression, clustering analysis, and best subset selection method are employed. In order to demonstrate the proposed intensity measure (IM) and algorithm, nonlinear dynamic analyses are performed using a validated computational model of ductile steel frame structure and one of the reinforced concrete (RC) structural frames modeled by Haselton et al. (2011). It is found that using a developed IM and ground motion selection algorithm, one can obtain a reliable estimation on the collapse potential of structure using far less number of ground motion time histories with uncertainty reduced.Chapter 1. Introduction 1 1.1. Study Background 1 1.2. Objectives, Framework and Importance of the Research 2 1.3. Organization of the Study 4 Chapter 2. Incremental Dynamic Analysis and Collapse Fragilities 6 2.1. Incremental Dynamic Analysis 6 2.2. Statistical Procedure for Fitting Fragility Functions to Structural Analysis Data 8 2.2.1. Maximum Likelihood Estimate (MLE) Formulation 9 2.2.2. Fragility Function based on Probabilistic Seismic Demand Model 10 Chapter 3. New Seismic Intensity Measure for Collapse Prediction Combining Cumulative and Peak Indices 13 3.1. A Four-Story Ductile Structural Frame Collapse Case Study 13 3.2. Existing IMs for Ground Motions 15 3.2.1. Basic Index 16 3.2.2. Peak Index 16 3.2.3. Cumulative Index 17 3.3. Energy-based Collapse Criteria and Descriptor 18 3.3.1. Energy-based Collapse Criteria 18 3.3.2. Energy-based Collapse Descriptor 20 3.4. Development of a New Intensity Measure 22 3.4.1. Seismic Input Energy 22 3.4.2. Dissipated Hysteretic Energy 25 3.4.3. A New Intensity Measure 27 3.4.4. Application of New IM to Reinforced Concrete Structural Frame 29 3.5. Influence of Energy Balance Ratio Between EI and EDegrading on Structural Collapse Capacity 32 Chapter 4. A Ground Motions Selection Procedure Using Clustering-based Adaptive Sampling 35 4.1. Ground Motion Selection Algorithm 35 4.2. Identification of Critical Features for Incremental Dynamic Analysis 40 4.3. Euclidian Metric Distance 41 4.4. Numerical Examples 43 4.4.1. Numerical Example 1 43 4.4.2. Numerical Example 2 46 4.4.3. Numerical Example 3 48 4.4.4. Numerical Example 4 50 Chapter 5. Conclusions 53 Appendix A 55 References 57 Abstract in Korean 63Maste

Seismic risk of Open Spaces in Historic Built Environments: A matrix-based approach for emergency management and disaster response

Abstract Earthquakes affect the safety of the users hosted in both indoor and outdoor urban built environments, especially in Historic Built Environments (HBEs). Many full HBE-scale risk-assessment methods are defined, while methodologies oriented to local analysis of meso-scale elements, such as Open Spaces (OSs), are still limited. Nevertheless, OSs play a crucial role in the first emergency phases, like in the evacuation process, since they host emergency paths and gathering areas. The seismic risk of an OS mainly depends on the combination of the damage suffered from facing buildings and the exposure, which mainly refers to the quantification of human lives. Damage levels result from the combination of vulnerability and hazard-related issues, while exposure is essentially affected by the number of OS users, whose spatial distribution is strongly time-dependent. Methods to quickly combine these issues are needed, especially in view of the deeper insights for the implementation of risk-reduction strategies (i.e. according to simulation-based approaches). This work offers a novel methodology to quickly perform Seismic Risk Assessment and Management of an OS by correlating damage levels to exposure-related issues. The method is composed of two specific matrices, which are developed according to quick literature-based approaches prone to rapid meso-scale applications in HBEs, also by non-expert technicians. The "damage matrix" links the site hazard to the building vulnerability. The assessed damage levels are combined with the users' exposure into the "consequences matrix", to estimate the risk in emergency conditions for the OS users, thus supporting decision-makers in promoting robustness/preparedness strategies

A multibranch, multitarget neural network for rapid point-source inversion in a microseismic environment: examples from the Hengill Geothermal Field, Iceland

Despite advanced seismological techniques, automatic source characterization for microseismic earthquakes remains difficult and challenging since current inversion and modelling of high-frequency signals are complex and time consuming. For real-time applications such as induced seismicity monitoring, the application of standard methods is often not fast enough for true complete real-time information on seismic sources. In this paper, we present an alternative approach based on recent advances in deep learning for rapid source-parameter estimation of microseismic earthquakes. The seismic inversion is represented in compact form by two convolutional neural networks, with individual feature extraction, and a fully connected neural network, for feature aggregation, to simultaneously obtain full moment tensor and spatial location of microseismic sources. Specifically, a multibranch neural network algorithm is trained to encapsulate the information about the relationship between seismic waveforms and underlying point-source mechanisms and locations. The learning-based model allows rapid inversion (within a fraction of second) once input data are available. A key advantage of the algorithm is that it can be trained using synthetic seismic data only, so it is directly applicable to scenarios where there are insufficient real data for training. Moreover, we find that the method is robust with respect to perturbations such as observational noise and data incompleteness (missing stations). We apply the new approach on synthesized and example recorded small magnitude (M <= 1.6) earthquakes at the Hellisheioi geothermal field in the Hengill area, Iceland. For the examined events, the model achieves excellent performance and shows very good agreement with the inverted solutions determined through standard methodology. In this study, we seek to demonstrate that this approach is viable for microseismicity real-time estimation of source parameters and can be integrated into advanced decision-support tools for controlling induced seismicity

Modelling of Harbour and Coastal Structures

As the most heavily populated areas in the world, coastal zones host the majority and some of the most important human settlements, infrastructures and economic activities. Harbour and coastal structures are essential to the above, facilitating the transport of people and goods through ports, and protecting low-lying areas against flooding and erosion. While these structures were previously based on relatively rigid concepts about service life, at present, the design—or the upgrading—of these structures should effectively proof them against future pressures, enhancing their resilience and long-term sustainability. This Special Issue brings together a versatile collection of articles on the modelling of harbour and coastal structures, covering a wide array of topics on the design of such structures through a study of their interactions with waves and coastal morphology, as well as their role in coastal protection and harbour design in present and future climates