An automatic phase picker for local and teleseismic events

1987 ◽  
Vol 77 (4) ◽  
pp. 1437-1445
Author(s):  
M. Baer ◽  
U. Kradolfer
Keyword(s):  
Arrival Time ◽  
Detection Algorithm ◽  
Dynamic Threshold ◽  
Data Set ◽  
Automatic Phase ◽  
Nonlinear Amplifier ◽  
First Motion ◽  
Transient Events ◽  
Teleseismic Events ◽  
Automatic Detection Algorithm

Abstract An automatic detection algorithm has been developed which is capable of time P-phase arrivals of both local and teleseismic earthquakes, but rejects noise bursts and transient events. For each signal trace, the envelope function is calculated and passed through a nonlinear amplifier. The resulting signal is then subjected to a statistical analysis to yield arrival time, first motion, and a measure of reliability to be placed on the P-arrival pick. An incorporated dynamic threshold lets the algorithm become very sensitive; thus, even weak signals are timed precisely. During an extended performance evaluation on a data set comprising 789 P phases of local events and 1857 P phases of teleseismic events picked by an analyst, the automatic picker selected 66 per cent of the local phases and 90 per cent of the teleseismic phases. The accuracy of the automatic picks was “ideal” (i.e., could not be improved by the analyst) for 60 per cent of the local events and 63 per cent of the teleseismic events.

scholarly journals Pedestrian detection algorithm in traffic scene based on weakly supervised hierarchical deep model

2016 ◽  
Vol 14 (1) ◽  
pp. 172988141769231 ◽  
Author(s):  
Yingfeng Cai ◽  
Youguo He ◽  
Hai Wang ◽  
Xiaoqiang Sun ◽  
Long Chen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Pedestrian Detection ◽  
Recognition Rate ◽  
Detection Algorithm ◽  
Training Methods ◽  
Two Dimensional ◽  
Data Set ◽  
Deep Model ◽  
Unsupervised Training ◽  
Weakly Supervised

The emergence and development of deep learning theory in machine learning field provide new method for visual-based pedestrian recognition technology. To achieve better performance in this application, an improved weakly supervised hierarchical deep learning pedestrian recognition algorithm with two-dimensional deep belief networks is proposed. The improvements are made by taking into consideration the weaknesses of structure and training methods of existing classifiers. First, traditional one-dimensional deep belief network is expanded to two-dimensional that allows image matrix to be loaded directly to preserve more information of a sample space. Then, a determination regularization term with small weight is added to the traditional unsupervised training objective function. By this modification, original unsupervised training is transformed to weakly supervised training. Subsequently, that gives the extracted features discrimination ability. Multiple sets of comparative experiments show that the performance of the proposed algorithm is better than other deep learning algorithms in recognition rate and outperforms most of the existing state-of-the-art methods in non-occlusion pedestrian data set while performs fair in weakly and heavily occlusion data set.

scholarly journals Atmospheric rivers moisture sources from a Lagrangian perspective

2016 ◽  
Vol 7 (2) ◽  
pp. 371-384 ◽  
Author(s):  
Alexandre M. Ramos ◽  
Raquel Nieto ◽  
Ricardo Tomé ◽  
Luis Gimeno ◽  
Ricardo M. Trigo ◽  
...  
Keyword(s):  
North Atlantic ◽  
Dispersion Model ◽  
Local Maximum ◽  
Detection Algorithm ◽  
Ocean Basin ◽  
Particle Dispersion ◽  
Western Coast ◽  
Moisture Uptake ◽  
Lagrangian Analysis ◽  
Data Set

Abstract. An automated atmospheric river (AR) detection algorithm is used for the North Atlantic Ocean basin, allowing the identification of the major ARs affecting western European coasts between 1979 and 2012 over the winter half-year (October to March). The entire western coast of Europe was divided into five domains, namely the Iberian Peninsula (9.75° W, 36–43.75° N), France (4.5° W, 43.75–50° N), UK (4.5° W, 50–59° N), southern Scandinavia and the Netherlands (5.25° E, 50–59° N), and northern Scandinavia (5.25° E, 59–70° N). Following the identification of the main ARs that made landfall in western Europe, a Lagrangian analysis was then applied in order to identify the main areas where the moisture uptake was anomalous and contributed to the ARs reaching each domain. The Lagrangian data set used was obtained from the FLEXPART (FLEXible PARTicle dispersion) model global simulation from 1979 to 2012 and was forced by ERA-Interim reanalysis on a 1° latitude–longitude grid. The results show that, in general, for all regions considered, the major climatological areas for the anomalous moisture uptake extend along the subtropical North Atlantic, from the Florida Peninsula (northward of 20° N) to each sink region, with the nearest coast to each sink region always appearing as a local maximum. In addition, during AR events the Atlantic subtropical source is reinforced and displaced, with a slight northward movement of the sources found when the sink region is positioned at higher latitudes. In conclusion, the results confirm not only the anomalous advection of moisture linked to ARs from subtropical ocean areas but also the existence of a tropical source, together with midlatitude anomaly sources at some locations closer to AR landfalls.

scholarly journals Automated detection and association of surface waves

1994 ◽  
Vol 37 (3) ◽  
Author(s):  
R. G. North ◽  
C. R. D. Woodgold
Keyword(s):  
Surface Waves ◽  
Group Velocity ◽  
Rayleigh Waves ◽  
Arrival Time ◽  
Narrow Band ◽  
Broad Band ◽  
Detection Algorithm ◽  
Automated Detection ◽  
Short Period ◽  
Group Velocities

An algorithm for the automatic detection and association of surface waves has been developed and tested over an 18 month interval on broad band data from the Yellowknife array (YKA). The detection algorithm uses a conventional STA/LTA scheme on data that have been narrow band filtered at 20 s periods and a test is then applied to identify dispersion. An average of 9 surface waves are detected daily using this technique. Beamforming is applied to determine the arrival azimuth; at a nonarray station this could be provided by poIarization analysis. The detected surface waves are associated daily with the events located by the short period array at Yellowknife, and later with the events listed in the USGS NEIC Monthly Summaries. Association requires matching both arrival time and azimuth of the Rayleigh waves. Regional calibration of group velocity and azimuth is required. . Large variations in both group velocity and azimuth corrections were found, as an example, signals from events in Fiji Tonga arrive with apparent group velocities of 2.9 3.5 krn/s and azimuths from 5 to + 40 degrees clockwise from true (great circle) azimuth, whereas signals from Kuriles Kamchatka have velocities of 2.4 2.9 km/s and azimuths off by 35 to 0 degrees. After applying the regional corrections, surface waves are considered associated if the arrival time matches to within 0.25 km/s in apparent group velocity and the azimuth is within 30 degrees of the median expected. Over the 18 month period studied, 32% of the automatically detected surface waves were associated with events located by the Yellowknife short period array, and 34% (1591) with NEIC events; there is about 70% overlap between the two sets of events. Had the automatic detections been reported to the USGS, YKA would have ranked second (after LZH) in terms of numbers of associated surface waves for the study period of April 1991 to September 1992.

scholarly journals Broadband waveforms and site effects at a borehole seismometer in the Po alluvial basin (Italy)

2001 ◽  
Vol 44 (1) ◽  
Author(s):  
M. Cocco ◽  
F. Ardizzoni ◽  
R. M. Azzara ◽  
L. Dall'Olio ◽  
A. Delladio ◽  
...  
Keyword(s):  
Site Effects ◽  
Velocity Structure ◽  
Dynamic Range ◽  
Site Response ◽  
Seismic Exploration ◽  
Ratio Method ◽  
Data Set ◽  
Broadband Seismometer ◽  
Local Earthquakes ◽  
Teleseismic Events

Broadband seismograms recorded at a borehole three-component (high dynamic range) seismic station in the Po Valley (Northern Italy) were analyzed to study the velocity structure of the shallow sedimentary layers as well as the local site effects in soft sediments. The broadband borehole seismometer was installed at a depth of 135 m just below the quaternary basement, while a second digital broadband seismometer was installed in the same site at the Earth surface. The velocity structure in the shallower layers was determined both by means of cross-hole and up-hole measurements and by inverting seismic data recorded during a seismic exploration experiment.Velocity discontinuities are quite well related to the stratigraphy of the site. We are interested to record local earthquakes as well as regional and teleseismic events. The analyzed data set includes local, regional and teleseismic events, most of which were recorded during the seismic sequence that started on October 15, 1996, near Reggio Emilia 80 km away from the borehole site. The orientation of the borehole sensor is determined using the recordings of a teleseismic event and of some local earthquakes. The noise reduction for the borehole sensor is 2 decades in power spectral density at frequencies larger than 1.0 Hz. We studied the site amplification of the shallow alluvial layers by applying the spectral ratio method. We analyzed the spectral ratios of noise recorded by the surface and borehole seismometers as well as those from local earthquakes. We compared these observations with a theoretical model for the site response computed by the Haskell-Thomson method.

Estimating azimuth and slowness from three-component and array stations

1990 ◽  
Vol 80 (6B) ◽  
pp. 1987-1998 ◽  
Author(s):  
Anne Suteau-Henson
Keyword(s):  
Arrival Time ◽  
Dominant Frequency ◽  
Polarization Analysis ◽  
Vertical Array ◽  
Regional Network ◽  
Array Measurements ◽  
Short Period ◽  
Wide Range ◽  
P Type ◽  
Teleseismic Events

Abstract The capabilities of three-component (3-C) and array stations for estimating azimuth and slowness are compared for short-period P-type phases recorded at the NORESS array. For vertical array data, azimuth and slowness estimates are obtained from broadband frequency-wavenumber (f-k) analysis. For 3-C data, polarization analysis is performed. The data processing is automated, using arrival time and dominant frequency information from the NORESS Bulletin. Independent determinations of azimuth and/or slowness, obtained from locations in the NEIS or regional network bulletins, are used as reference estimates. Over 100 events are analyzed, both teleseismic and regional. They were selected from a variety of distances and azimuths, and cover a wide range of signal-to-noise ratios (SNR). The capability of 3-C stations for azimuth and slowness estimation critically depends on SNR. For SNR below a threshold of ∼2, the scatter in the estimates is very large for both parameters, and the slowness of teleseismic events tends to be overestimated. Also, the results are site-dependent within the NORESS array. The array measurements obtained with the broadband f-k method are not significantly affected by noise at the levels of SNR considered. For events with sufficient SNR, both methods compare well, and only a slightly better performance is observed with the f-k method.

Volcanic Tremor Extraction and Earthquake Detection Using Music Information Retrieval Algorithms

2021 ◽  
Author(s):  
Zahra Zali ◽  
Matthias Ohrnberger ◽  
Frank Scherbaum ◽  
Fabrice Cotton ◽  
Eva P. S. Eibl
Keyword(s):  
Amplitude Spectrum ◽  
Volcanic Tremor ◽  
Volcanic Eruptions ◽  
Detection Algorithm ◽  
Transient Signal ◽  
Volcanic Origin ◽  
Time Frequency ◽  
Reconstructed Signal ◽  
Single Station ◽  
Transient Events

Abstract Volcanic tremor signals are usually observed before or during volcanic eruptions and must be monitored to evaluate the volcanic activity. A challenge in studying seismic signals of volcanic origin is the coexistence of transient signal swarms and long-lasting volcanic tremor signals. Separating transient events from volcanic tremors can, therefore, contribute to improving upon our understanding of the underlying physical processes. Exploiting the idea of harmonic–percussive separation in musical signal processing, we develop a method to extract the harmonic volcanic tremor signals and to detect transient events from seismic recordings. Based on the similarity properties of spectrogram frames in the time–frequency domain, we decompose the signal into two separate spectrograms representing repeating (harmonic) and nonrepeating (transient) patterns, which correspond to volcanic tremor signals and earthquake signals, respectively. We reconstruct the harmonic tremor signal in the time domain from the complex spectrogram of the repeating pattern by only considering the phase components for the frequency range in which the tremor amplitude spectrum is significantly contributing to the energy of the signal. The reconstructed signal is, therefore, clean tremor signal without transient events. Furthermore, we derive a characteristic function suitable for the detection of transient events (e.g., earthquakes) by integrating amplitudes of the nonrepeating spectrogram over frequency at each time frame. Considering transient events like earthquakes, 78% of the events are detected for signal-to-noise ratio = 0.1 in our semisynthetic tests. In addition, we compared the number of detected earthquakes using our method for one month of continuous data recorded during the Holuhraun 2014–2015 eruption in Iceland with the bulletin presented in Ágústsdóttir et al. (2019). Our single station event detection algorithm identified 84% of the bulletin events. Moreover, we detected a total of 12,619 events, which is more than twice the number of the bulletin events.

A method of locating the 3D centers of retroreflectors based on deep learning

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
BinBin Zhang ◽  
Fumin Zhang ◽  
Xinghua Qu
Keyword(s):  
Deep Learning ◽  
Data Augmentation ◽  
Measurement Techniques ◽  
Recognition Rate ◽  
Detection Algorithm ◽  
Large Field ◽  
Small Scale ◽  
Data Set ◽  
Content Type ◽  
Ellipse Detection

Purpose Laser-based measurement techniques offer various advantages over conventional measurement techniques, such as no-destructive, no-contact, fast and long measuring distance. In cooperative laser ranging systems, it’s crucial to extract center coordinates of retroreflectors to accomplish automatic measurement. To solve this problem, this paper aims to propose a novel method. Design/methodology/approach We propose a method using Mask RCNN (Region Convolutional Neural Network), with ResNet101 (Residual Network 101) and FPN (Feature Pyramid Network) as the backbone, to localize retroreflectors, realizing automatic recognition in different backgrounds. Compared with two other deep learning algorithms, experiments show that the recognition rate of Mask RCNN is better especially for small-scale targets. Based on this, an ellipse detection algorithm is introduced to obtain the ellipses of retroreflectors from recognized target areas. The center coordinates of retroreflectors in the camera coordinate system are obtained by using a mathematics method. Findings To verify the accuracy of this method, an experiment was carried out: the distance between two retroreflectors with a known distance of 1,000.109 mm was measured, with 2.596 mm root-mean-squar error, meeting the requirements of the coarse location of retroreflectors. Research limitations/implications The research limitations/implications are as follows: (i) As the data set only has 200 pictures, although we have used some data augmentation methods such as rotating, mirroring and cropping, there is still room for improvement in the generalization ability of detection. (ii) The ellipse detection algorithm needs to work in relatively dark conditions, as the retroreflector is made of stainless steel, which easily reflects light. Originality/value The originality/value of the article lies in being able to obtain center coordinates of multiple retroreflectors automatically even in a cluttered background; being able to recognize retroreflectors with different sizes, especially for small targets; meeting the recognition requirement of multiple targets in a large field of view and obtaining 3 D centers of targets by monocular model-based vision.

Improvement of Universal Dynamic Threshold Cloud Detection Algorithm and Its Application in High Resolution Satellite

2018 ◽  
Vol 38 (10) ◽  
pp. 1028002 ◽  
Author(s):  
王权 Wang Quan ◽  
孙林 Sun Lin ◽  
韦晶 Wei Jing ◽  
周雪莹 Zhou Xueying ◽  
陈婷婷 Chen Tingting ◽  
...  
Keyword(s):  
High Resolution ◽  
Detection Algorithm ◽  
Dynamic Threshold ◽  
Cloud Detection

Automatic Detection of Corrugation: Preliminary Results in the Dutch Network Using Axle Box Acceleration Measurements

2015 ◽  
Author(s):  
Shaoguang Li ◽  
Alfredo Núñez ◽  
Zili Li ◽  
Rolf Dollevoet
Keyword(s):  
Automatic Detection ◽  
Detection Algorithm ◽  
System Response ◽  
Growth Mechanisms ◽  
Preliminary Results ◽  
Rail Network ◽  
Track System ◽  
Good Potential ◽  
Automatic Detection Algorithm ◽  
Wheel Track

Short pitch corrugation is commonly seen in all kinds of tracks. There is not yet a conclusive explanation in the literature for its initiation and growth mechanisms. In this paper, we use an axle box acceleration (ABA) measurement system to detect corrugation. ABA can be easily implemented in operational trains, providing direct and reliable health monitoring of the track. We have extended a detection algorithm for rail surface local short wavelength defects to also detect short pitch corrugation, which is a continuous defect over the track. A 3D transient FE wheel-track model is employed to find theoretical signature tunes of the wheel-track system response when passing over a short pitch corrugation. Numerical simulations agree with ABA measurement obtained in the Dutch rail network. Based on the signature tune identified, an automatic detection algorithm is developed. Preliminary results with the algorithm are discussed. Field observations show a good potential of the detection algorithm to be used by inframanagers, to detect and monitor corrugation.

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