scholarly journals Earthquake detection through computationally efficient similarity search

2015 ◽  
Vol 1 (11) ◽  
pp. e1501057 ◽  
Author(s):  
Clara E. Yoon ◽  
Ossian O’Reilly ◽  
Karianne J. Bergen ◽  
Gregory C. Beroza
Keyword(s):  
Detection Methods ◽  
Continuous Data ◽  
Full Potential ◽  
Data Mining Algorithm ◽  
Computationally Efficient ◽  
Widespread Application ◽  
Waveform Data ◽  
Earthquake Detection ◽  
Seismic Waveform ◽  
Scalable Search

Seismology is experiencing rapid growth in the quantity of data, which has outpaced the development of processing algorithms. Earthquake detection—identification of seismic events in continuous data—is a fundamental operation for observational seismology. We developed an efficient method to detect earthquakes using waveform similarity that overcomes the disadvantages of existing detection methods. Our method, called Fingerprint And Similarity Thresholding (FAST), can analyze a week of continuous seismic waveform data in less than 2 hours, or 140 times faster than autocorrelation. FAST adapts a data mining algorithm, originally designed to identify similar audio clips within large databases; it first creates compact “fingerprints” of waveforms by extracting key discriminative features, then groups similar fingerprints together within a database to facilitate fast, scalable search for similar fingerprint pairs, and finally generates a list of earthquake detections. FAST detected most (21 of 24) cataloged earthquakes and 68 uncataloged earthquakes in 1 week of continuous data from a station located near the Calaveras Fault in central California, achieving detection performance comparable to that of autocorrelation, with some additional false detections. FAST is expected to realize its full potential when applied to extremely long duration data sets over a distributed network of seismic stations. The widespread application of FAST has the potential to aid in the discovery of unexpected seismic signals, improve seismic monitoring, and promote a greater understanding of a variety of earthquake processes.

scholarly journals Batch seismic inversion using the iterative ensemble Kalman smoother

2021 ◽  
Author(s):  
Michael Gineste ◽  
Jo Eidsvik
Keyword(s):  
Seismic Data ◽  
Time Windows ◽  
Seismic Inversion ◽  
Efficient Estimation ◽  
Solution Strategy ◽  
Adaptive Procedure ◽  
Waveform Data ◽  
Kalman Smoother ◽  
Seismic Waveform ◽  
Single Batch

AbstractAn ensemble-based method for seismic inversion to estimate elastic attributes is considered, namely the iterative ensemble Kalman smoother. The main focus of this work is the challenge associated with ensemble-based inversion of seismic waveform data. The amount of seismic data is large and, depending on ensemble size, it cannot be processed in a single batch. Instead a solution strategy of partitioning the data recordings in time windows and processing these sequentially is suggested. This work demonstrates how this partitioning can be done adaptively, with a focus on reliable and efficient estimation. The adaptivity relies on an analysis of the update direction used in the iterative procedure, and an interpretation of contributions from prior and likelihood to this update. The idea is that these must balance; if the prior dominates, the estimation process is inefficient while the estimation is likely to overfit and diverge if data dominates. Two approaches to meet this balance are formulated and evaluated. One is based on an interpretation of eigenvalue distributions and how this enters and affects weighting of prior and likelihood contributions. The other is based on balancing the norm magnitude of prior and likelihood vector components in the update. Only the latter is found to sufficiently regularize the data window. Although no guarantees for avoiding ensemble divergence are provided in the paper, the results of the adaptive procedure indicate that robust estimation performance can be achieved for ensemble-based inversion of seismic waveform data.

Repeating Earthquakes Along the Colombian Subduction Zone

2020 ◽  
Vol 15 (5) ◽  
pp. 645-654
Author(s):  
Juan Carlos Bermúdez-Barrios ◽  
◽  
Hiroyuki Kumagai
Keyword(s):  
Subduction Zone ◽  
Threshold Value ◽  
Waveform Data ◽  
Repeating Earthquakes ◽  
Seismic Waveform ◽  
Interplate Coupling ◽  
Short Period ◽  
Tectonically Active ◽  
Waveform Similarity ◽  
Large Earthquakes

Colombia is tectonically active, and several large earthquakes have ruptured the Colombia-Ecuador subduction zone (CESZ) during the last century. Among them, the Colombia-Ecuador earthquake in 1906 (Mw 8.4) and the Tumaco earthquake in 1979 (Mw 8.3) generated destructive tsunamis. Therefore, it is important to characterize the seismic rupture processes and their relation with interplate coupling along the CESZ. We searched for repeating earthquakes by performing waveform similarity analysis. Cross correlation (CC) values were computed between earthquake pairs with hypocenter differences of less than 50 km that were located in the northern CESZ (1°–4°N) and that occurred from June 1993 to February 2018. We used broadband and short-period seismic waveform data from the Servicio Geológico Colombiano (SGC) seismic network. A CC threshold value of 0.90 was used to identify the waveform similarity and select repeating earthquakes. We found repeating earthquakes distributed near the trench and the coast. Our estimated repeating earthquakes near the trench suggest that the interplate coupling in this region is low. This is in clear constrast to the occurrence of a large slip in the 1906 Colombia-Ecuador earthquake along the trench in the southern part of the CESZ, and suggests that rupture modes are different between the northern and southern parts of CESZ near the trench.

Self-Learning Based Centrifugal Compressor Surge Mapping With Computationally Efficient Adaptive Asymmetric Support Vector Machine

2012 ◽  
Vol 134 (5) ◽  
Author(s):  
Xin Wu ◽  
Yaoyu Li
Keyword(s):  
Support Vector Machine ◽  
Feature Space ◽  
Ambient Air ◽  
Low Flow ◽  
Detection Methods ◽  
Support Vector ◽  
Three Dimension ◽  
Computationally Efficient ◽  
Surge Line ◽  
Self Learning

When an air compressor is operated at very low flow rate for a given discharge pressure, surge may occur, resulting in large oscillations in pressure and flow in the compressor. To prevent the damage of the compressor, on account of surge, the control strategy employed is typically to operate it below the surge line (a map of the conditions at which surge begins). Surge line is strongly affected by the ambient air conditions. Previous research has developed to derive data-driven surge maps based on an asymmetric support vector machine (ASVM). The ASVM penalizes the surge case with much greater cost to minimize the possibility of undetected surge. This paper concerns the development of adaptive ASVM based self-learning surge map modeling via the combination with signal processing techniques for surge detection. During the actual operation of a compressor after the ASVM based surge map is obtained with historic data, new surge points can be identified with the surge detection methods such as short-time Fourier transform or wavelet transform. The new surge point can be used to update the surge map. However, with increasing number of surge points, the complexity of support vector machine (SVM) would grow dramatically. In order to keep the surge map SVM at a relatively low dimension, an adaptive SVM modeling algorithm is developed to select the minimum set of necessary support vectors in a three-dimension feature space based on Gaussian curvature to guarantee a desirable classification between surge and nonsurge areas. The proposed method is validated by applying the surge test data obtained from a testbed compressor at a manufacturing plant.

scholarly journals Change-point detection in wind turbine SCADA data for robust condition monitoring with normal behaviour models

2020 ◽  
Author(s):  
Simon Letzgus
Keyword(s):  
Wind Turbine ◽  
Change Point ◽  
Significant Loss ◽  
Change Point Detection ◽  
Training Data ◽  
Change Points ◽  
Detection Methods ◽  
Full Potential ◽  
Normal Behaviour ◽  
Point Detection

Abstract. Analysis of data from wind turbine supervisory control and data acquisition (SCADA) systems has attracted considerable research interest in recent years. The data is predominantly used to gain insights into turbine condition without the need for additional sensing equipment. Most successful approaches apply semi-supervised anomaly detection methods, also called normal behaivour models, that use clean training data sets to establish healthy component baseline models. However, one of the major challenges when working with wind turbine SCADA data in practice is the presence of systematic changes in signal behaviour induced by malfunctions or maintenance actions. Even though this problem is well described in literature it has not been systematically addressed so far. This contribution is the first to comprehensively analyse the presence of change-points in wind turbine SCADA signals and introduce an algorithm for their automated detection. 600 signals from 33 turbines are analysed over an operational period of more than two years. During this time one third of the signals are affected by change-points. Kernel change-point detection methods have shown promising results in similar settings but their performance strongly depends on the choice of several hyperparameters. This contribution presents a comprehensive comparison between different kernels as well as kernel-bandwidth and regularisation-penalty selection heuristics. Moreover, an appropriate data pre-processing procedure is introduced. The results show that the combination of Laplace kernels with a newly introduced bandwidth and penalty selection heuristic robustly outperforms existing methods. In a signal validation setting more than 90 % of the signals were classified correctly regarding the presence or absence of change-points, resulting in a F1-score of 0.86. For a change-point-free sequence selection the most severe 60 % of all CPs could be automatically removed with a precision of more than 0.96 and therefore without a significant loss of training data. These results indicate that the algorithm can be a meaningful step towards automated SCADA data pre-processing which is key for data driven methods to reach their full potential. The algorithm is open source and its implementation in Python publicly available.

A target-oriented scheme for efficient inversion of time-lapse seismic waveform data

2018 ◽  
Author(s):  
Xingguo Huang ◽  
Morten Jakobsen ◽  
Kjersti Solberg Eikrem ◽  
Geir Nævdal
Keyword(s):  
Time Lapse ◽  
Waveform Data ◽  
Seismic Waveform

Source Characteristics of the 2017 Ms 6.6 (Mw 6.3) Jinghe Earthquake in the Northeastern Tien Shan

2020 ◽  
Vol 91 (2A) ◽  
pp. 745-757
Author(s):  
Xu Zhang ◽  
Li-Sheng Xu ◽  
Jun Luo ◽  
Wanpeng Feng ◽  
Hai-Lin Du ◽  
...  
Keyword(s):  
Energy Release ◽  
Joint Inversion ◽  
Near Field ◽  
Tien Shan ◽  
Rupture Velocity ◽  
The West ◽  
Waveform Data ◽  
Source Characteristics ◽  
Seismic Waveform ◽  
Finite Fault

Abstract On 8 August 2017, an Ms 6.6 earthquake occurred in the northeastern Tien Shan orogenic belt. To reveal the source characteristics of this earthquake completely, the teleseismic and near-field seismic waveform data were collected as well as the coseismic Interferometric Synthetic Aperture Radar displacement data, and the methods of the backprojection and the finite-fault joint inversion were adopted. The backprojection of the teleseismic recordings indicates a unilateral rupture propagating 15 km westward. Two stages of the rupture were recognized from the backprojection results: in the first ∼5  s, the rupture took place near the hypocenter, with an accelerating energy release but a small rupture velocity; then the rupture extended to the west, with a decelerating energy release but a relatively fast rupture velocity. The joint inversion of the multiple datasets shows a major slip asperity of about 24  km × 18  km. The asperity extended mainly to the west, with a duration of approximately 10 s. The average rupture velocity over the asperity was estimated to be approximately 2.0  km/s, which is close to that 1.9  km/s estimated by the backprojection. It is interesting that the high-frequency sources were aligned almost on the margin of the slip asperity. Moreover, the occurrence of the earthquake sequence is found to relate with the low-VP/VS zone, implying a tectonic property, which controls the nucleation and rupture of earthquakes.

From National to Transnational Seismic Monitoring Products and Services in the Republic of Bulgaria, Republic of Moldova, Romania, and Ukraine

2021 ◽  
Author(s):  
Alexandru Mărmureanu ◽  
Constantin Ionescu ◽  
Bogdan Grecu ◽  
Dragos Toma-Danila ◽  
Alexandru Tiganescu ◽  
...  
Keyword(s):  
Risk Mitigation ◽  
Seismic Monitoring ◽  
Current Status ◽  
Waveform Data ◽  
Strong Earthquakes ◽  
Crustal Earthquakes ◽  
Seismic Waveform ◽  
The Republic ◽  
Seismic Networks ◽  
Research Facilities

Abstract The aim of this article is to analyze the background, current status, and outlook of seismic monitoring products and services in Bulgaria, Moldova, Romania, and Ukraine. These countries manage seismic networks that contribute to the European Integrated Data Archive node in the framework of the Observatories and Research Facilities for European Seismology, which represents a collaborative effort in coordinating observational seismology across the European region through the collection, archiving, and dissemination of seismic waveform data, metadata, and related products. All of the aforementioned countries share a common threat: strong earthquakes occurring in the Vrancea area located in central-eastern Romania at intermediate depths (usually in the 60–180 km interval). Events such as the ones on 10 November 1940 and 4 March 1977 generated high damage in Romania, northern Bulgaria, and Moldova. In addition to Vrancea, crustal earthquakes in areas such as Shabla or Dulovo can lead to cross-border damage. Therefore, understanding the way national seismic networks are distributed, how they cooperate, and the products and services that they provide in (near) real time and their terms is of significant interest in the context of necessary hazard harmonization and joint emergency intervention and risk mitigation actions.

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