Deep Learning for earthquake detection

The creation of accurate ground motion models can only be achieved with the help ofvast amounts of labelled data. The manual cataloging makes the processing of this data.The project proposes the automation of the labeling through real data and DL.Previous study show that CNN are the best architecture for this type of problems. Theproject also implements FFNN, a simpler architecture, with the intent of achieving com-petitive results. To make this objective possible the project proposes a novel preprocessing.The results show that CNN reach an accuracy of 98.2%, while FFNN achieves 91.2%.Moreover, the project includes an encoder based algorithm to approximate arrival timesto the station. Finally, the project make use of meta-learning to detect seismic eventsproviding from a single station.Models realistes per la detecció automàtica de sismes només es poden aconseguir analitzant un gran volum de dades. Aquestes dades són difícils de processar amb un catalogat manual. Aquest projecte proposa la automatització d'aquest procés mitjançant l'ús de dades reals i tècniques de DL. Estudis anteriors demostren que els millors resultats per resoldre problemes d'aquest tipus s'obtenen utilitzant CNN. A part d'aquestes xarxes, el projecte implementa unes xarxes més simples, com les FFNN, esperant obtenir resultats competitius en comparació als obtinguts per les CNN. Per obtenir aquest objectiu, el projecte proposa una tècnica de preprocessament de traces sísmiques innovadora. Els resultats mostren que les CNN arriben a detectar el 98,2% dels events, mentre que les FFNN ho fan en un 91,2%. Per un altra part, el projecte inclou un algoritme basat en codificadors per aproximar el temps de arribada del event a la estació. Finalment, el projecte utilitza la metodologia de meta-learning per realitzar la detecció de sismes per les dades que provenen exclusivament d'una única estació.Modelos realistas para la detección automática de sismos solo se pueden conseguir anal-izando grandes cantidades de datos. Estos datos son difíciles de procesar por catalogado manual. Este proyecto plantea la automatización de este proceso usando datos reales y técnicas de DL. Estudios anteriores demuestran que los mejores resultados, para resolver este tipo de problemas, se alcanzan usando CNN. A parte de estas redes, el proyecto implementa redes más simples, como las FFNN, esperando obtener resultados competitivos respecto a los obtenidos por las CNN. Para alcanzar este objetivo, el proyecto propone una técnica de preprocesamiento novedosa de trazas sísmicas. Los resultados muestran que CNN llega a detectar el 98,2% de los eventos, mientras que FFNN lo realiza en un 91,2%. Por otra parte, el proyecto incluye un algoritmo basado en codificadores para aproximar el tiempo de arrivo del evento a la estación. Finalmente, el proyecto utiliza la metodología de meta-learning para realizar detección de sismos para los datos que provienen de una única estación

Dataset for hardware Trojan detection

Nowadays, cloud services rely extensively on the use of virtual machines to enforce security by isolation. However, hardware trojan attacks break this assumption. Within these attacks, cache side-channel attacks such as Spectre and Meltdown are the focus of this work. In this project, we develop a set of tools to generate a dataset; and a dataset that will allow the use of Machine Learning techniques to detect Spectre and Meltdown attacks (i.e. using a cache side-channel). When released, this dataset will enable researchers to compare their ML-based detection proposals based on the same dataset (which is not currently the case). Also, it eliminates the need of an infected computer to generate the attacks and the corresponding dataset for subsequent research studies

Artificial neural networks as emerging tools for earthquake detection

As seismic networks continue to spread and monitoring sensors become more ef¿cient, the abundance of data highly surpasses the processing capabilities of earthquake interpretation analysts. Earthquake catalogs are fundamental for fault system studies, event modellings, seismic hazard assessment, forecasting, and ultimately, for mitigating the seismic risk. These have fueled the research for the automation of interpretation tasks such as event detection, event identi¿cation, hypocenter location, and source mechanism analysis. Over the last forty years, traditional algorithms based on quantitative analyses of seismic traces in the time or frequency domain, have been developed to assist interpretation. Alternatively, recentadvancesarerelatedtotheapplicationofArti¿cial Neural Networks (ANNs), a subset of machine learning techniques that is pushing the state-of-the-art forward in many areas. Appropriated trained ANN can mimic the interpretation abilities of best human analysts, avoiding the individual weaknesses of most traditional algorithms, and spending modest computational resources at the operational stage. In this paper, we will survey the latest ANN applications to the automatic interpretation of seismic data, with a special focus on earthquake detection, and the estimation of onset times. For a comparative framework, we give an insight into the labor of human interpreters, who may face uncertainties in the case of small magnitude earthquakes.Peer ReviewedPostprint (published version

Long short-term memory networks for earthquake detection in Venezuelan regions

Reliable earthquake detection and location algorithms are necessary to properly catalog and analyze the continuously growing seismic records. This paper reports the results of applying Long Short-Term Memory (LSTM) networks to single-station three-channel waveforms for P-wave earthquake detection in western and north central regions of Venezuela. Precisely, we apply our technique to study the seismicity along the dextral strike-slip Boconó and La Victoria - San Sebastián faults, with complex tectonics driven by the interactions between the South American and Caribbean plates.Peer ReviewedPostprint (author's final draft

Small-layered feed-forward and convolutional neural networks for efficient P wave earthquake detection

The number and efficiency of seismic networks have steadily increase over time delivering large datasets to be analyzed for earthquake occurrence. Automatic tools for accurate earthquake detection are under emerging and intense development. This paper first proposes a new windowing procedure of seismic traces that highly facilitates earthquake detection. This procedure applies regular trace filtering and normalization, but also performs a strict window alignment to P wave onset. These event-aligned windows represent the input data to our P wave detection networks, with relatively small or moderate number of layers. We then develop Feed-Forward (FFNN) and Convolutional (CNN) neural networks and explore multiple architecture configurations to find relevant hyperparameter patterns for better detection. To assess network performance, we adopt the widely used metrics of accuracy (ACC) and the area under the curve (AUC) of the Receiver Operating Characteristic function. In terms of ACC, the best FFNN and CNN reach performances of 91% and 98%, respectively. On the other hand, the best FFNN and CNN in terms of AUC achieve performances of 96% and 99%, respectively. Thus, our novel trace windowing procedure allows developing networks with few hyperparameters, for correct earthquake detection under low computational costs. Finally, we use the CNN with best AUC performance as an effective trace filtering with the purpose of P wave arrival time estimation.This work is partially supported by the Generalitat de Catalunya under grant 2017-SGR-962 and the DRAC project (001-P-001723). The research leading to these results has received funding from the European Union’s Horizon 2020 research and innovation programme under the ChEESE project, grant agreement No. 823844. This project has also received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 777778 MATHROCKS.Peer ReviewedPostprint (author's final draft

Privacy preserving deep learning framework in fog computing

Nowadays, the widespread use of mobile devices has raised serious cybersecurity challenges. Mobile services and applications use deep learning (DL) models for the modelling, classification and recognition of complex data, such as images, audio, video or text. Users benefit from the wide range of ser-vices and applications offered by these devices but pay an enormous price, the privacy of their personal data. Mobile services collect all different types of us-ers’ data, including sensitive personal data, photos, videos, clinical data, bank-ing data, etc. All this data is pooled to the Cloud to train global DL models, and big companies benefit from all the collected users’ data, posing obvious serious privacy issues. This paper proposes a privacy preserving framework for Fog computing envi-ronments, which adopts a distributed deep learning approach. Internet of Things (IoT) end nodes never reveals their sensitivity to the Cloud server; instead, they share a fraction of the users’ data, blurred with Gaussian noise, with a nearby Fog node. The DL methods considered in this work are Multilayer Perceptron (MLP) and Convolutional Neural Networks (CNN), and for both cases the ac-curacy is similar to the centralized and privacy violating approach, obtaining the best results for the CNN model.This work is partially supported by Generalitat de Catalunya under the SGR program (2017-SGR-962) and the RIS3CAT DRAC project (001-P-001723).Peer ReviewedPostprint (author's final draft

Epicentral region estimation using convolutional neural networks

Recent works have assessed the capability of deep neural networks of estimating the epicentral source region of a seismic event from a single-station three-channel signal. In all the cases, the geographical partitioning is performed by automatic tessellation algorithms such as the Voronoi decomposition. This paper evaluates the hypothesis that the source region estimation accuracy is significantly increased if the geographical partitioning is performed considering the regional geological characteristics such as the tectonic plate boundaries. Also, it raises the transformation of the training data to increase the accuracy of the predictive model based on a Projected Coordinate Reference (PCR) System. A deep convolutional neural network (CNN) is applied over the data recorded by the broadband stations of the Venezuelan Foundation of Seismological Research (FUNVISIS) in the region of 9.5 to 11.5ºN and 67.0 to 69.0ºW between April 2018 and April 2019. In order to estimate the epicentral source region of a detected event, several geographical tessellations provided by seismologists from the area are employed. These tessellations, with different number of partitions, consider the fault systems of the study region (San Sebastián, La Victoria and Morón fault systems). The results are compared to the ones obtained with automatic partitioning performed by the k-means algorithm.This work is partially supported by the Spanish Ministry of Economy and Competitivity under contract TIN2015-65316-P, by the Spanish Ministry of Science and Innovation under contract PID2019-107255GB-C22, and by the SGR programmes (2014-SGR-1051 and 2017-SGR-962) of the Catalan Government and has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 777778 (MATHROCKS).Peer ReviewedPostprint (author's final draft