Automatic Classification of Seismic Signals at Mt. Vesuvius Volcano, Italy, Using Neural Networks

We present a new strategy for reliable automatic classification of local seismic signals and volcano-tectonic earthquakes (VT). The method is based on a supervised neural network in which a new approach for feature extraction from short period seismic signals is applied. To reduce the number of records required for the analysis we set up a specialized neural classifier, able to distinguish two classes of signals, for each of the selected stations. The neural network architecture is a multilayer perceptron (MLP) with a single hidden layer. Spectral features of the signals and the parameterized attributes of their waveform have been used as input for this network. Feature extraction is done by using both the linear predictor coding technique for computing the spectrograms, and a function of the amplitude for characterizing waveforms. Compared to strategies that use only spectral signatures, the inclusion of properly normalized amplitude features improves the performance of the classifiers, and allows the network to better generalize. To train the MLP network we compared the performance of the quasi-Newton algorithm with the scaled conjugate gradient method. We found that the scaled conjugate gradient approach is the faster of the two, with quite equally good performance. Our method was tested on a dataset recorded by four selected stations of the Mt. Vesuvius monitoring network, for the discrimination of low magnitude VT events and transient signals caused by either artificial (quarry blasts, underwater explosions) and natural (thunder) sources. In this test application we obtained 100% correct classification for one of the possible pairs of signal types (VT versus quarry blasts). Because this method was developed independently of this particular discrimination task, it can be applied to a broad range of other applications

Classifying seismic waveforms from scratch: a case study in the alpine environment

Nowadays, an increasing amount of seismic data is collected by daily observatory routines. The basic step for successfully analyzing those data is the correct detection of various event types. However, the visually scanning process is a time-consuming task. Applying standard techniques for detection like the STA/LTA trigger still requires the manual control for classification. Here, we present a useful alternative. The incoming data stream is scanned automatically for events of interest. A stochastic classifier, called hidden Markov model, is learned for each class of interest enabling the recognition of highly variable waveforms. In contrast to other automatic techniques as neural networks or support vector machines the algorithm allows to start the classification from scratch as soon as interesting events are identified. Neither the tedious process of collecting training samples nor a time-consuming configuration of the classifier is required. An approach originally introduced for the volcanic task force action allows to learn classifier properties from a single waveform example and some hours of background recording. Besides a reduction of required workload this also enables to detect very rare events. Especially the latter feature provides a milestone point for the use of seismic devices in alpine warning systems. Furthermore, the system offers the opportunity to flag new signal classes that have not been defined before. We demonstrate the application of the classification system using a data set from the Swiss Seismological Survey achieving very high recognition rates. In detail we document all refinements of the classifier providing a step-by-step guide for the fast set up of a well-working classification syste

Models for Identifying Structures in the Data: A Performance Comparison

This paper reports on the unsupervised analysis of seismic signals recorded in Italy, respectively on the Vesuvius volcano, located in Naples, and on the Stromboli volcano, located North of Eastern Sicily. The Vesuvius dataset is composed of earthquakes and false events like thunders, man-made quarry and undersea explosions. The Stromboli dataset consists of explosion-quakes, landslides and volcanic microtremor signals. The aim of this paper is to apply on these datasets three projection methods, the linear Principal Component Analysis (PCA), the Self-Organizing Map (SOM), and the Curvilinear Component Analysis (CCA), in order to compare their performance. Since these algorithms are well known to be able to exploit structures and organize data providing a clear framework for understanding and interpreting their relationships, this work examines the category of structural information that they can provide on our specific sets. Moreover, the paper suggests a breakthrough in the application area of the SOM, used here for clustering different seismic signals. The results show that, among the three above techniques, SOM better visualizes the complex set of high-dimensional data discovering their intrinsic structure and eventually appropriately clustering the different signal typologies under examination, discriminating the explosionquakes from the landslides and microtremor recorded at the Stromboli volcano, and the earthquakes from natural (thunders) and artificial (quarry blasts and undersea explosions) events recorded at the Vesuvius volcano

Event recognition in marine seismological data using Random Forest machine learning classifier

Automatic detection of seismic events in ocean bottom seismometer (OBS) data is difficult due to elevated levels of noise compared to the recordings from land. Popular deep-learning approaches that work well with earthquakes recorded on land perform poorly in a marine setting. Their adaptation to OBS data requires catalogues containing hundreds of thousands of labelled event examples that currently do not exist, especially for signals different than earthquakes. Therefore, the usual routine involves standard amplitude-based detection methods and manual processing to obtain events of interest. We present here the first attempt to utilize a Random Forest supervised machine learning classifier on marine seismological data to automate catalogue screening and event recognition among different signals [i.e. earthquakes, short duration events (SDE) and marine noise sources]. The detection approach uses the short-term average/long-term average method, enhanced by a kurtosis-based picker for a more precise recognition of the onset of events. The subsequent machine learning method uses a previously published set of signal features (waveform-, frequency- and spectrum-based), applied successfully in recognition of different classes of events in land seismological data. Our workflow uses a small subset of manually selected signals for the initial training procedure and we then iteratively evaluate and refine the model using subsequent OBS stations within one single deployment in the eastern Fram Strait, between Greenland and Svalbard. We find that the used set of features is well suited for the discrimination of different classes of events during the training step. During the manual verification of the automatic detection results, we find that the produced catalogue of earthquakes contains a large number of noise examples, but almost all events of interest are properly captured. By providing increasingly larger sets of noise examples we see an improvement in the quality of the obtained catalogues. Our final model reaches an average accuracy of 87 per cent in recognition between the classes, comparable to classification results for data from land. We find that, from the used set of features, the most important in separating the different classes of events are related to the kurtosis of the envelope of the signal in different frequencies, the frequency with the highest energy and overall signal duration. We illustrate the implementation of the approach by using the temporal and spatial distribution of SDEs as a case study. We used recordings from six OBSs deployed between 2019 and 2020 off the west-Svalbard coast to investigate the potential link of SDEs to fluid dynamics and discuss the robustness of the approach by analysing SDE intensity, periodicity and distance to seepage sites in relation to other published studies on SDEs

Fluid injections in the subsurface: a multidisciplinary approach for better understanding their implications on induced seismicity and the environment.

Fluid injections in the subsurface are common operations in underground industrial activities such as oil and gas exploitation, geothermal energy development, and carbon capture and storage (CCS). In recent years, it became a focal point as new drilling technologies (e.g., hydraulic fracturing) enable the extraction of oil and gas in unconventional reservoirs and the development of CCS injection techniques became a key research topic in the context of the low-carbon energy transition. Fluid injections have drawn the attention also in the general public because of their main potential implications such as the induced seismicity phenomenon (Rubinstein and Mahani, 2015) and the environmental pollution (Burton et al., 2016, Pitchel et al., 2016). Considering the strong socioeconomic impact of fluid injection operations (National Research Council, 2013; Ellsworth, 2013; Grigoli et al., 2017) the current research in this field needs the integration of multidisciplinary studies, involving knowledge on geology, seismology, source physics, hydrogeology, fluid geochemistry, rocks geomechanics for a complete understanding of the phenomenon and to set-up the most effective and “best practice” protocols for the monitoring of areas where injection operation are performed. On this basis, this work applied a multidisciplinary approach integrating seismological methods, geochemical studies, and machine learning techniques. Two key-study areas characterized by high fluid-rock interaction and fluid-injection in the subsurface were analyzed: i) the High Agri Valley (hereinafter HAV), hosting the largest onshore oil field in West Europe, in which wastewater disposal operations have been carried out since 2006 at the Costa Molina 2 injection well and where both natural and induced seismicity clusters were recognized; ii) the Mefite d’Ansanto, the largest natural emission of CO2-rich gases with mantle-derived fluids (from non‐volcanic environment) ever measured on the Earth (Carcausi et al., 2013; Caracausi and Paternoster, 2015; Chiodini et al, 2010). Regarding the HAV study area, we reconstructed the preliminary catalogue of seismicity through accurate absolute locations in a 3D-velocity model (Serlenga and Stabile, 2019) of earthquakes detected from the local seismic INSIEME network managed by the CNR-IMAA. A total of 852 between local tectonic and induced earthquakes occurred in the HAV between September 2016 and March 2019. We tested the potential of the unsupervised machine-learning approach as an automated tool to make faster dataset exploratory analysis, founding the density-based approach (DBSCAN algorithm-Density-Based Spatial Clustering of Applications with Noise, Ester et al., 1996) particularly suitable for the fast identification of clusters in the catalogue resulting from both injection-induced events and tectonic local earthquake swarms. Moreover, we proposed a semi-automated workflow for earthquake detection and location with the aim to improve the current standard procedures, quite time-consuming and strictly related to human operators. The workflow, integrating manual, semi-automatic and automatic detection and location methods enabled us to characterize a low magnitude natural seismic sequence occurred in August 2020 in the southwestern area of the HAV (Castelsaraceno sequence) in a relatively short time with respect to the application of standard techniques, thus representing a starting point for the improvement of the efficiency of seismic monitoring techniques of both anthropogenic and natural seismicity in the HAV. Our multidisciplinary approach involved the geochemical study of the HAV groundwaters with the aim to: (1) determine the geochemical processes controlling the chemical composition; (2) define a geochemical conceptual model regarding fluid origin (deep vs shallow) and mixing processes by means isotopic data; (3) establish a geochemical baseline for the long-term environmental monitoring of the area. A total of 39 water samples were collected from springs and wells located at the main hydro-structures bordering the valley to determine chemical (major, minor and trace elements) and isotopic composition (e.g., dD, d18O, d13C-TDIC and noble gas). All investigated water samples have a meteoric origin, although some springs show long and deep flow than the other ones, and a bicarbonate alkaline-earth composition, thus suggesting the carbonate hydrolysis as the main water-rock interaction process. Our results demonstrated that HAV groundwater is chemically suitable for drinking use showing no criticalities for potentially toxic metals reported by the Italian and European legislation guidelines. Particular attention was given on thermal water of Tramutola well, built by Agip S.p.a. for oil & gas exploration, with the occurrence of bubbling gases. The geochemical study highlighted a substantial difference of these CH4-dominated thermal fluids with the rest of the dataset. Helium isotope (3He/4He) indicate a prevalent radiogenic component with a contribution of mantle-derived helium (~20%) and the average δ13C-CO2 value is of – 4.6 ‰ VPDB, consistent with a mantle origin. Methane isotope composition indicates a likely microbial isotopic signature (δ13C-CH4 =−63.1‰, −62.4‰, δD-CH4=−196‰, −212‰), probably due to biodegradation processes of thermogenic hydrocarbons. The methane output at the well, evaluated by means of anemometric measurement of the volume flow (m3/h) is of ~156 t/y, that represent about 1.5% of total national anthropogenic sources related to fossil fuel industry (Etiope et al., 2007). Our work highlighted that Tramutola well may represent a key natural laboratory to better understand the complex coupling effects between mechanical and fluid-dynamic processes in earthquake generation. Moreover, the integration of seismic and geochemical data in this work allowed us to identify the most suitable locations for the future installation of multiparametric stations for the long-term monitoring of the area and development of integrated research in the HAV. Regarding the Mefite d’Ansanto, we analyzed the background seismicity in the emission area recorded by a dense temporary seismic network deployed at the site between 30-10-2019 and 02-11-2019. First, we implemented and tested an automated detection algorithm based on non-parametric statistics of the recorded amplitudes at each station, collecting a total dataset of 8561 events. Then, both unsupervised (DBSCAN) and supervised (KNN-k-nearest neighbors classification, Fix & Hodges, 1951) machine learning techniques were applied, based on specific parameters (duration, RMS-amplitude and arrival slope) of the detected events. DBSCAN algorithm allowed to determine characteristic bivariate correlations among tremors parameters: a high linear correlation (r~0.6-0.7) between duration and RMS-amplitude and a lower one (r~0.5-0.6) between amplitude and arrival slope (first arrival parametrization). These relationships let us to define training samples for the KNN algorithm, which allowed to classify tremor signals at each station and to automatically discriminate between tremors and accidentally detected anthropogenic noise. Results allowed to extract new information on seismic tremor at Mefite d’Ansanto, previously poorly quantitively analyzed, and its discrimination, thus providing a starting workflow for monitoring the non-volcanic emission. Isotopic geochemistry (3He/4He, 4 He/20Ne, δ13CCO2) indicated a mixing of mantle (30%-40%) and crust-derived fluids. The source location of the emission related tremor would represent a step forward in its characterization, and for setting up more advanced automated detection and machine learning classification techniques to exploit the information provided by seismic tremor for an improved automatic monitoring of non-volcanic, CO2 -gas emissions

Speech Recognition based Automatic Earthquake Detection and Classification

Die moderne Seismologie zeichnet die Bodenbewegungen mit einem weltweit verteilten Stationsnetz kontinuierlich auf und gilt damit als datenreiche Wissenschaft. Die Extraktion der im Moment interessierenden Daten aus diesen kontinuierlichen Aufzeichnungen, seien es Erdbebensignale oder Nuklearsprengungen oder ä.m., ist eine Herausforderung an die bisher verwendeten Detektions- und Klassifizierungsalgorithmen

Advanced Methods for Real-time Identification and Determination of Seismic Events

Natural disasters pose an indistinguishable threat to populations all around the world, affecting ~200 hundred million every year, with earthquakes being the most deadly. Global seismic monitoring allows for robust real-time analysis to provide useful information about an event to assist in earthquake emergency response. Additionally it is an essential tool for monitoring anthropogenic seismic sources like nuclear weapons tests, the use of which can have disastrous effects on human life, ecological environments and public health, ramifications that can last for generations. The focus of this thesis is on characterizing and identifying unique seismic events in near-real-time using the waveforms of initial seismic phase arrivals from teleseismic stations, their derivative products like radiated earthquake energy and rupture duration, and machine learning (ML). This thesis is a compilation of several works addressing novel methods for seismic event identification of: global tsunamigenic earthquakes, uncharacteristically high-energy tsunami earthquakes, deep earthquakes, and underground nuclear explosions (UNE). First, I present the current Real-Time Earthquake Energy and Rupture Duration Determinations (RTerg) products and methodology applied to a case study of fast-rupturing tsunami earthquakes in the Solomon Islands, testing the robustness of the RTerg derivative waveform products and Tsunami Earthquake (TsE) discriminant threshold used for real-time analysis. Second, I show how peaks in RTerg energy flux curves from teleseismic stations and their differences in broadband and high frequency bandwidths can be associated with depth phase arrivals (P, pP, sP) to identify deep earthquakes, highlighting the potential for real-time depth determinations using first derivative waveform products without additional processing of waveforms. Next, I introduce nuclear explosion monitoring from a global network of stations, starting with the compilation of the first openly available and comprehensive UNE seismic waveform and event catalog termed GTUNE (Georgia Tech Underground Nuclear Explosions). GTUNE seismic records are sourced from declassified nuclear tests, previously published datasets and openly available waveforms and were assembled into a user‐friendly format compatible with most python‐based ML packages. The next contribution to this thesis is the development of a global UNE classifier using labeled P-wave seismograms from GTUNE. I trained a Convolutional Neural Network (CNN) to identify three classes: earthquake P-wave, nuclear P-wave, and noise. I found that the model can accurately characterize most events, finding over 90% of the signals in the validation set, even with limited training data. Lastly, I combine the thesis works described thus far and applied similar ML methodology to classify/predict deep earthquakes, using both a CNN and a Deep Neural Net (DNN), trained on both physical features of the energy flux time series (prominence and peak density) as well as the original waveforms. Results show better single station predictions using the original waveforms. By contrast, for full network determinations, the energy flux products perform the best, despite the smaller training dataset. We anticipate that ML models like our UNE and deep earthquake classifiers can have broad application for other “small data” seismic signals including volcanic and non-volcanic tremor, anomalous earthquakes, tsunami earthquakes, ice-quakes or landslide-quakes.Ph.D