Topical Collection "Seismology and Earthquake Engineering"
A topical collection in Sensors (ISSN 1424-8220). This collection belongs to the section "Remote Sensors".
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Department of Geosciences, National Taiwan University, Taipei 106, Taiwan
Interests: seismology;earthquake early warning
Topical Collection Information
The continuous threat of earthquake events makes the constant and meticulous monitoring of active fault structures a necessity. Indeed, remote sensing techniques focused on earthquake events and their effects, active tectonic analyses and seismic hazard assessment have proven a powerful tool kit for the scientific community. In recent decades, many new techniques have been deployed to understand how earthquakes affect the natural and built environment.
It is possible to separate the issue of remote sensing data application into a few items: tectonic analysis of seismoactive regions, earthquake prediction and modeling of lithosphere–atmosphere–ionosphere coupling in seismic processes. Tectonic analysis does not provide essential information about earthquake mechanisms; satellite radar interferometry may not be able to capture preseismic deformation in time; and methods based on visible and thermal bands suffer from cloud cover.
The aim of this collection is to collect new research and developments in the field. We invite the submission of original contributions exploring the current research trends to be presented in this collection.
Prof. Dr. Yih-Min Wu
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Published Papers (4 papers)
Cluster Analysis of Seismicity in the Eastern Gulf of Corinth Based on a Waveform Template Matching Catalog
Cited by 1
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The Corinth Rift, in Central Greece, is one of the most seismically active areas in Europe. In the eastern part of the Gulf of Corinth, which has been the site of numerous large and destructive earthquakes in both historic and modern times, a
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The Corinth Rift, in Central Greece, is one of the most seismically active areas in Europe. In the eastern part of the Gulf of Corinth, which has been the site of numerous large and destructive earthquakes in both historic and modern times, a pronounced earthquake swarm occurred in 2020–2021 at the Perachora peninsula. Herein, we present an in-depth analysis of this sequence, employing a high-resolution relocated earthquake catalog, further enhanced by the application of a multi-channel template matching technique, producing additional detections of over 7600 events between January 2020 and June 2021. Single-station template matching enriches the original catalog thirty-fold, providing origin times and magnitudes for over 24,000 events. We explore the variable levels of spatial and temporal resolution in the catalogs of different completeness magnitudes and also of variable location uncertainties. We characterize the frequency–magnitude distributions using the Gutenberg–Richter scaling relation and discuss possible b
-value temporal variations that appear during the swarm and their implications for the stress levels in the area. The evolution of the swarm is further analyzed through spatiotemporal clustering methods, while the temporal properties of multiplet families indicate that short-lived seismic bursts, associated with the swarm, dominate the catalogs. Multiplet families present clustering effects at all time scales, suggesting triggering by aseismic factors, such as fluid diffusion, rather than constant stress loading, in accordance with the spatiotemporal migration patterns of seismicity.
Opera 2015 Project: Accurate Measurement Equipment for Earthquake Electromagnetic Emissions and Radio Seismic Indicator
Cited by 1
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Electromagnetic emissions from earthquakes are known as precursors and are of considerable importance for the purpose of early alarms. The propagation of low-frequency waves is favored, and the range between tens of mHz to tens of Hz has been heavily investigated in the
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Electromagnetic emissions from earthquakes are known as precursors and are of considerable importance for the purpose of early alarms. The propagation of low-frequency waves is favored, and the range between tens of mHz to tens of Hz has been heavily investigated in the last thirty years. This work describes the self-financed Opera 2015 project that initially consisted of six monitoring stations over Italy, equipped with electric and magnetic field sensors, among others. Insight of the designed antennas and low-noise electronic amplifiers provides both characterization of performance (similar to the best commercial products) and the elements to replicate the design for our own independent studies. Measured signals through data acquisition systems were then processed for spectral analysis and are available on the Opera 2015 website. Data provided by other world-known research institutes have also been considered for comparison. The work provides examples of processing methods and results representation, identifying many exogenous noise contributions of natural or human-made origin. The study of the results occurred for some years and led us to think that reliable precursors are confined to a short area around the earthquake due to the significant attenuation and the effect of overlapping noise sources. To this aim, a magnitude-distance indicator was developed to classify the detectability of the EQ events observed during 2015 and compared this with some other known earthquake events documented in the scientific literature.
The Effect of Soil-Structure Interaction on the Seismic Response of Structures Using Machine Learning, Finite Element Modeling and ASCE 7-16 Methods
Cited by 3
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Seismic design of structures taking into account the soil-structure interaction (SSI) methods is considered to be more efficient, cost effective, and safer then fixed-base designs, in most cases. Finite element methods that use direct equations to solve SSI problems are very popular, but
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Seismic design of structures taking into account the soil-structure interaction (SSI) methods is considered to be more efficient, cost effective, and safer then fixed-base designs, in most cases. Finite element methods that use direct equations to solve SSI problems are very popular, but the prices of the software are very high, and the analysis time is very long. Even though some low-cost and efficient software are available, the structures are mostly analyzed for the superstructure only, without using the geotechnical properties of the ground and its interaction effects. The reason is that a limited number of researchers have the knowledge of both geotechnical and structural engineering to model accurately the coupled soil-structure system. However, a cost-effective, less time-consuming and easy-to-implement technique is to analyze the structure along with ground properties using machine learning methods. The database techniques using machine learning are robust and provide reliable results. Thus, in this study, machine learning techniques, such as artificial neural networks and support vector machines are used to investigate the effect of soil-structure interactions on the seismic response of structures for different earthquake scenarios. Four frame structures are investigated by varying the soil and seismic properties. In addition, varying sample sizes and different optimization algorithms are used to obtain the best machine learning framework. The input parameters contain both soil and seismic properties, while the outputs consist of three engineering demand parameters. The network is trained using three and five-story buildings and tested on a three-story building with mass irregularity and a four-story building. Furthermore, the proposed method is compared with the dynamic responses obtained using fixed-base and ASCE 7-16 SSI methods. The proposed machine learning method showed better results compared with fixed-base and ASCE 7-16 methods with the nonlinear time history analysis results as a reference.
Comparison of Amplitude Measurements on Borehole Geophone and DAS Data
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DAS and geophones are the two most popular sensors for borehole seismic acquisition. As such, it is important to get a good understanding of how these two types of sensors compare to each other. The natural measurand for the techniques is different; millivolts
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DAS and geophones are the two most popular sensors for borehole seismic acquisition. As such, it is important to get a good understanding of how these two types of sensors compare to each other. The natural measurand for the techniques is different; millivolts are approximately proportional to particle velocities for geophones vs. changes in the phase of light linked to the changes in strain on the sensing fibre. This paper focuses on the experimental comparison of absolute values of these measurands derived from a VSP survey acquired in Curtin GeoLab training well. We describe the acquisition setup for the walk-away VSP acquired with DAS and geophones, allowing the direct comparison and the workflow, which we can use to represent the data in strain rate. Albeit this is unlikely to be universal, we find that the absolute values are similar for this experimental setup.