Automatic event detection in microblogs using incremental machine learning

In seismology, when dealing with low signal-to-noise recordings, traditional event detection methods are often unable to recognise all the weak events hidden within the seismic noise. We are interested in investigating how machine learning techniques can be a useful tool to improve automatic event detection by recognising the similarity between events. We are &#160;interested in studying areas where anthropogenic activity, related to the exploitation of subsoil resources, can generate induced seismicity. Therefore, it is essential to increase the detection of weak events to improve knowledge about the seismicity of the area and its related consequences. The SOM (Self-Organizing Map) is an unsupervised machine learning approach that is widely used for clustering, visualization and data-exploration tasks in various applications. The SOM carries out a nonlinear mapping of data onto a two-dimensional map, preserving the most important topological and metric relationships of the data. One of the reasons for using SOM for clustering indeed is to benefit from its topological structure when interpreting the data clusters.&#160; In the preprocessing stage, features extraction is done by using both the linear prediction coding (LPC) technique for coding the spectrograms, and a waveform parameterization for characterizing amplitude characteristics in the time domain, for each of the three components. The SOM was trained on dataset, recorded at the St Gallen geothermal site, composed of 388 records of seismic noise and 347 earthquakes with magnitude (MLcorr) between -1.2 and 3.5 collected by the Swiss Seismological Service in 2013 while realizing well control measures after drilling and acidizing the GT-1 well. We obtained promising first results as SOM strategy correctly discriminates all known earthquakes events, clustering them into different nodes, distant from the group of nodes where noise falls. We also jointly tested synthetic traces in which we have hidden events traces within seismic noise or noise artificially generated. We studied the signals of each cluster individually, assessing the similarities of the waveform and spectral characteristics for the three components. In addition, the results are also evaluated in terms of events location, hypocentral distance, magnitude, and origin time. This work has been supported by PRIN-2017 MATISSE project, No 20177EPPN2, funded by the Italian Ministry of Education and Research.

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Automatic event detection for long-term monitoring of hydrophone data

Proceedings of 2011 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing ◽

10.1109/pacrim.2011.6032973 ◽

2011 ◽

Author(s):

F. Sattar ◽

P. F. Driessen ◽

G. Tzanetakis ◽

S. R. Ness ◽

W. H. Page

Keyword(s):

Event Detection ◽

Long Term Monitoring ◽

Automatic Event Detection ◽

Term Monitoring

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Event Detection and Localization Using Machine Learning on a Staircase

Conference Proceedings of the Society for Experimental Mechanics Series - Dynamics of Civil Structures, Volume 2 ◽

10.1007/978-3-030-12115-0_30 ◽

2019 ◽

pp. 219-223

Author(s):

Blake Feichtl ◽

Caleb Thompson ◽

Tyler Liboro ◽

Saad Siddiqui ◽

V. V. N. Sriram Malladi ◽

...

Keyword(s):

Machine Learning ◽

Event Detection ◽

Detection And Localization

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High-Level Automatic Event Detection and User Classification in a Social Network Context

Graphical Models for Security - Lecture Notes in Computer Science ◽

10.1007/978-3-030-36537-0_10 ◽

2019 ◽

pp. 203-219

Author(s):

Fabio Persia ◽

Sven Helmer

Keyword(s):

Social Network ◽

Event Detection ◽

User Classification ◽

High Level ◽

Automatic Event Detection

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OKSP: A Novel Deep Learning Automatic Event Detection Pipeline for Seismic Monitoring in Costa Rica

10.1109/bip53678.2021.9612832 ◽

2021 ◽

Author(s):

Leonardo van der Laat ◽

Ronald J.L. Baldares ◽

Esteban J. Chaves ◽

Esteban Meneses

Keyword(s):

Deep Learning ◽

Costa Rica ◽

Event Detection ◽

Seismic Monitoring ◽

Automatic Event Detection

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Impact of Renewable Energy Penetration on PMU Based Grid Event Detection Using Machine Learning Framework

10.1109/icps52420.2021.9670151 ◽

2021 ◽

Author(s):

Rajib Majumdar ◽

Abhishek Rai ◽

Paramita Chattopadhyay

Keyword(s):

Machine Learning ◽

Renewable Energy ◽

Event Detection ◽

Learning Framework

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Combing empirical matched field processing at IMS station SPITS and convolutional neural networks for calving event detection in Svalbard

10.5194/egusphere-egu21-1994 ◽

2021 ◽

Author(s):

Andreas Köhler ◽

Steffen Mæland

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Time Series ◽

Convolutional Neural Networks ◽

Event Detection ◽

The Arctic ◽

Beam Power ◽

Seismic Events ◽

Tidewater Glaciers ◽

Seismic Arrays

We combine the empirical matched field (EMF) method and machine learning using Convolutional Neural Networks (CNNs) for calving event detection at the IMS station SPITS and GSN station KBS on the Arctic Archipelago of Svalbard. EMF detection with seismic arrays seeks to identify all signals similar to a single template generated by seismic events in a confined target region. In contrast to master event cross-correlation detectors, the detection statistic is not the waveform similarity, but the array beam power obtained using empirical phase delays (steering parameters) between the array stations. Unlike common delay-and-sum beamforming, the steering parameters do not need to represent a plane wave and are directly computed from the template signal without assuming a particular apparent velocity and back-azimuth. As for all detectors, the false alarms rate depends strongly on the beam power threshold setting and therefore needs appropriate tuning or alternatively post-processing. Here, we combine the EMF detector using a low detection threshold with a post-detection classification step. The classifier uses spectrograms of single-station three-component records and state-of-the-art CNNs pre-trained for image recognition. Spectrograms of three-component seismic data are hereby combined as RGB images. We apply the methodology to detect calving events at tidewater glaciers in the Kongsfjord region in Northwestern Svalbard. The EMF detector uses data of the SPITS array, at about 100 km distance to the glaciers, while the CNN classifier processes data from the single three-component station KBS at 15 km distance using time windows where the event is expected according to the EMF detection. The EMF detector combines templates for the P and for the S wave onsets of a confirmed, large calving event. The CNN spectrogram classifier is trained using classes of confirmed calving signals from four different glaciers in the Kongsfjord region, seismic noise examples, and regional tectonic seismic events. By splitting the data into training and test data set, the CNN classifier yields a recognition rate of 89% on average. This is encouragingly high given the complex nature of calving signals and their visually similar waveforms. Subsequently, we process continuous data of 6 months in 2016 using the EMF-CNN method to produce a time series of glacier calving. About 90% of the confirmed calving signals used for the CNN training are detected by EMF processing, and around 80% are assigned to the correct glacier after CNN classification. Such calving time series allow us to estimate and monitor ice loss at tidewater glaciers which in turn can help to better understand the impact of climate change in Polar regions. Combining the superior detection capability of (less common) seismic arrays at a larger source distance with a powerful machine learning classifier at single three-component stations closer to the source, is a promising approach not only for environmental monitoring, but also for event detection and classification in a CTBTO verification context.

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