Complementary Methods for Volcanic Seismic Source Discrimination

ABSTRACT FINAL ID: V53E-2673 TITLE: Complementary Methods for Volcanic Seismic Source Discrimination SESSION TYPE: Poster SESSION TITLE: V53E. Surveillance of Volcanic Unrest: New Developments in Multidisciplinary Monitoring Methods IV Posters AUTHORS (FIRST NAME, LAST NAME): Charlotte A Rowe1, Susanna M R Falsaperla2, Emily Morton3, Horst K Langer2, Boris Behncke2 INSTITUTIONS (ALL): 1. Los Alamos Natl Lab, Los Alamos, NM, United States. 2. Istituto Nazionale di Geofisica e Volcanologia, Catania, Italy. 3. Earth and Environmental Sciences, New Mexico Institute of Mining and Technology, Socorro, NM, United States. Title of Team: ABSTRACT BODY: We explore the success rates of detection and classification algorithms as applied to seismic signals from active volcanoes. The subspace detection method has shown some success in identifying repeating (but not identical) signals from seismic swarm sources, as well as pulling out nonvolcanic long period events within subduction zone tremor. We continue the exploration of this technique as applied to both discrete events and variations within volcanic tremor to determine optimal situations for its use. We will demonstrate both three-dimensional and subband applications both on raw waveforms and derived features such as skewness and kurtosis. The application can be used in both a supervised (select templates and compare) as well as unsupervised (cross-compare all samples and apply clustering to the matrix of comparisons). We compare the method to that of the KKAnalysis tool, which uses a self-organizing map approach to unsupervised clustering for feature vectors derived from the seismic waveforms. We will present a comparison of this method as applied to waveform features, spectral features and time-varying higher-order statistics as well as signal polarization, to elucidate the tools which show the best promise for problematic discrimination tasks

A Software Package for Unsupervised Pattern Recognition and Synoptic Representation of Results: Application to Volcanic Tremor Data of Mt Etna

Artificial Intelligence (AI) has found broad applications in volcano observatories worldwide with the aim of reducing volcanic hazard. The need to process larger and larger quantity of data makes indeed AI techniques appealing for monitoring purposes. Tools based on Artificial Neural Networks and Support Vector Machine have proved to be particularly successful in the classification of seismic events and volcanic tremor changes heralding eruptive activity, such as paroxysmal explosions and lava fountaining at Stromboli and Mt Etna, Italy (e.g., Falsaperla et al., 1996; Langer et al., 2009). Moving on from the excellent results obtained from these applications, we present KKAnalysis, a MATLAB based software which combines several unsupervised pattern classification methods, exploiting routines of the SOM Toolbox 2 for MATLAB (http://www.cis.hut.fi/projects/somtoolbox). KKAnalysis is based on Self Organizing Maps (SOM) and clustering methods consisting of K-Means, Fuzzy C-Means, and a scheme based on a metrics accounting for correlation between components of the feature vector. We show examples of applications of this tool to volcanic tremor data recorded at Mt Etna between 2007 and 2009. This time span - during which Strombolian explosions, 7 episodes of lava fountaining and effusive activity occurred - is particularly interesting, as it encompassed different states of volcanic activity (i.e., non-eruptive, eruptive according to different styles) for the unsupervised classifier to identify, highlighting their development in time. Even subtle changes in the signal characteristics allow the unsupervised classifier to recognize features belonging to the different classes and stages of volcanic activity. A convenient color-code representation shows up the temporal development of the different classes of signal, making this method extremely helpful for monitoring purposes and surveillance. Though being developed for volcanic tremor classification, KKAnalysis is generally applicable to any type of physical or chemical pattern, provided that feature vectors are given in numerical form

Identification of activity regimes by unsupervised pattern classification of volcanic tremor data. Case studies from Mt. Etna

The monitoring of the seismic background signal – commonly referred to as volcanic tremor - has become a key tool for volcanic surveillance, particularly when field surveys are unsafe and/or visual observations are hampered by bad weather conditions. Indeed, it could be demonstrated that changes in the state of activity of the volcano show up in the volcanic tremor signature, such as amplitude and frequency content. Hence, the analysis of the characteristics of volcanic tremor leads us to pass from a mere monoparametric vision of the data to a multivariate one, which can be tackled with modern concepts of multivariate statistics. For this aim we present a recently developed software package which combines various concepts of unsupervised classification, in particular cluster analysis and Kohonen maps. Unsupervised classification is based on a suitable definition of similarity between patterns rather than on a-priori knowledge of their class membership. It aims at the identification of heterogeneities within a multivariate data set, thus permitting to focalize critical periods where significant changes in signal characteristics are encountered. The application of the software is demonstrated on sample sets derived from Mt. Etna during eruptions in 2001, 2006 and 2007-8

Regimes of Volcanic Activity at Mt. Etna in 2007-2009 inferred from Unsupervised Pattern Recognition on Volcanic Tremor Data

Mt Etna is a well monitored basaltic volcano for which high-quality, multidisciplinary data set are continuously available for around-the-clock surveillance. Particularly, volcano-seismic data sets cover decades long local recordings, temporally encompassing different styles of eruptive activity, from Strombolian eruptions to lava fountains and lava flows. Intense earthquakes swarms have often heralded effusive activity. However, from the seismic point of view, volcanic tremor has proved to be one of the most reliable indicators of impending eruptive activity. Indeed, changes in the volcano feeder show up in the signature of tremor, its spectral characteristics and source location. Some of us (Langer and Messina) have recently developed a new software for the classification of volcanic tremor data, combining Self Organizing Maps (also known as Kohonen Maps) along with Cluster and Fuzzy Analysis. This software allows us to analyse the background seismic radiation at permanent broadband stations located at various distance from the summit craters to identify transitions from pre-eruptive to eruptive activity. Throughout the analysis of the data flow, the software provides an unsupervised classification of the spectral characteristics (i.e., amplitude and frequency content) of the signal. The information embedded in the spectrum is interpreted to assign a specific state of the volcano. An application of this new software is proposed here on the eruptive events at Etna of 2007-2009, which consisted of 7 episodes of lava fountaining, periodic Strombolian activity at the summit craters, followed by lava emissions on the upper east flank of the volcano, with start on 13 May 2008 and end on 6 July 2009. In the study period the source of volcanic tremor was always shallow (less than 3 km) and within the volcano edifice. The upraise of magma to the surface was fast and associated with changes of volcanic tremor features, which covered time windows of variable duration from several hours to a few minutes. We discuss the possible reasons of such variability in the light of the characteristics of the overall seismicity preceding the eruptions in the study period, taking into account field observations and rheology of the ascending magma as well

Complementary Methods for Volcanic Seismic Source Discrimination

ABSTRACT FINAL ID: V53E-2673 TITLE: Complementary Methods for Volcanic Seismic Source Discrimination SESSION TYPE: Poster SESSION TITLE: V53E. Surveillance of Volcanic Unrest: New Developments in Multidisciplinary Monitoring Methods IV Posters AUTHORS (FIRST NAME, LAST NAME): Charlotte A Rowe1, Susanna M R Falsaperla2, Emily Morton3, Horst K Langer2, Boris Behncke2 INSTITUTIONS (ALL): 1. Los Alamos Natl Lab, Los Alamos, NM, United States. 2. Istituto Nazionale di Geofisica e Volcanologia, Catania, Italy. 3. Earth and Environmental Sciences, New Mexico Institute of Mining and Technology, Socorro, NM, United States. Title of Team: ABSTRACT BODY: We explore the success rates of detection and classification algorithms as applied to seismic signals from active volcanoes. The subspace detection method has shown some success in identifying repeating (but not identical) signals from seismic swarm sources, as well as pulling out nonvolcanic long period events within subduction zone tremor. We continue the exploration of this technique as applied to both discrete events and variations within volcanic tremor to determine optimal situations for its use. We will demonstrate both three-dimensional and subband applications both on raw waveforms and derived features such as skewness and kurtosis. The application can be used in both a supervised (select templates and compare) as well as unsupervised (cross-compare all samples and apply clustering to the matrix of comparisons). We compare the method to that of the KKAnalysis tool, which uses a self-organizing map approach to unsupervised clustering for feature vectors derived from the seismic waveforms. We will present a comparison of this method as applied to waveform features, spectral features and time-varying higher-order statistics as well as signal polarization, to elucidate the tools which show the best promise for problematic discrimination tasks

Negaton and Positon Solutions of the KDV Equation

We give a systematic classification and a detailed discussion of the structure, motion and scattering of the recently discovered negaton and positon solutions of the Korteweg-de Vries equation. There are two distinct types of negaton solutions which we label $[S^{n}]$ and $[C^{n}]$, where $(n+1)$ is the order of the Wronskian used in the derivation. For negatons, the number of singularities and zeros is finite and they show very interesting time dependence. The general motion is in the positive $x$ direction, except for certain negatons which exhibit one oscillation around the origin. In contrast, there is just one type of positon solution, which we label $[\tilde C^n]$. For positons, one gets a finite number of singularities for $n$ odd, but an infinite number for even values of $n$. The general motion of positons is in the negative $x$ direction with periodic oscillations. Negatons and positons retain their identities in a scattering process and their phase shifts are discussed. We obtain a simple explanation of all phase shifts by generalizing the notions of ``mass" and ``center of mass" to singular solutions. Finally, it is shown that negaton and positon solutions of the KdV equation can be used to obtain corresponding new solutions of the modified KdV equation.Comment: 20 pages plus 12 figures(available from authors on request),Latex fil

Aborted eruptions at Mt. Etna (Italy) in spring 2007 unveiled by an integrated study of gas emission and volcanic tremor

In spring 2007, a sequence of paroxysmal episodes took place at the Southeast Crater of Mt. Etna, Italy. Eruptive activity, characterised by Strombolian explosions, lava fountains, emission of lava flows and tephra, were all associated with an outstanding increase in the amplitude of volcanic tremor. In periods of quiescence between the eruptive episodes, recurring phases of seismic unrest were observed in forms of small temporary enhancements of the volcanic tremor amplitude, even though none of them culminated in eruptive activity. Here, we present the results of an integrated geophysical and geochemical data analysis encompassing records of volcanic tremor, thermal data, plume SO2 flux and radon over two months.We conclude that between February and April 2007, magma triggered repeated episodes of gas pulses and rock fracturing, but failed to reach the surface. Our multidisciplinary study allowed us to unveil these ‘aborted’ eruptions by investigating the long-temporal evolution of gas measurements along with seismic radiation. Short-term changes were additionally highlighted using a method of pattern classification based on Kohonen Maps and Fuzzy Clustering applied to volcanic tremor and radon data

The July 2006 eruption of Mount Etna (Italy) monitored through continuous soil radon measurements

Radon (222Rn) is a short-lived decay product derived from 238U, with a half-life of only 3.8 days. This gas ascends towards the earth’s surface mainly through cracks or faults. In recent decades radon has been used as a tool for predicting earthquakes and volcanic eruptions, because anomalous variations of its activity have often been reported before the occurrence of such geodynamic events. The recent eruptive activity of Mount Etna in Sicily (Italy) has been documented by multidisciplinary visual, geochemical, and instrumental observations. Here we describe the results obtained during the 10-day July 2006 Strombolian-effusive eruption of Mount Etna by using a radon probe installed near Torre del Filosofo (˜2950 m above sea level). This site is located ˜1 km south of the Southeast Crater, the youngest and most active of the four summit craters of the volcano, and the site of the July 2006 eruption. In order to better interpret the soil radon data we have compared them with simultaneously acquired volcanic tremor signals and a relative measurement of the thermal radiance emitted from the eruption area, derived from thermal camera measurements. During the month prior to the onset of the 2006 eruption, soil radon activity remained at low levels (˜1 x 103 Bq/m3); similar values persisted even when effusive activity started late on 14 July 2006. Only at ˜02:50 on the 15th July, radon activity showed a sharp increase (up to ˜50 x 103 Bq/m3) in a 20 minute interval, and a further increase to ˜20 x 106 Bq/m3 during the following hours. Explosive activity started at 04:30, 100 minutes after the initial rise in soil radon activity. High values in radon activity with numerous peaks persisted through the following four days, and were then followed by a marked decline until early on 20th July, when an extremely sharp rise brought the levels of radon activity to unprecedented values of nearly 1.7 x 108 Bq/m3, and remained very high for the next ˜24 hours. The episode of lava fountaining of 20th July occurred during this interval, starting 10 hours after the maximum in radon activity. From then on through the end of the eruptive phase, the levels of radon activity fluctuated with values rarely exceeding 106 Bq/m3 and then gradually declined starting from around noon on 22nd July. At the end of the eruption, radon levels remained higher (10-100 x 103 Bq/m3) than those recorded before the eruption. In conclusion, the onset of the Strombolian activity (15 July) and the lava fountaining (20 July) were related to significant changes in the magma pressure within the conduit. These two events were preceded by some hours with increases in radon soil emission by 4-5 orders of magnitude. For this reason we can imagine in the future the use of this signal as a potential precursor of this type of volcanic activity. Minor changes in eruptive behaviour did not produce significant variations in the monitored parameters. We interpret peaks in radon activity as due primarily to microfracturing of uranium-bearing rock. These observations suggest that radon measurements in the summit area of Etna are strongly controlled by the state of stress within the volcano and demonstrate the usefulness of radon data acquisition before and during eruptions

Contributions from an integrated analysis of geochemical and geophysical parameters to the study of failed eruptions at Mt. Etna

Continuous monitoring at Mt. Etna volcano usually unveils remarkable changes in geophisycal and geochemical parameters before the onset of volcanic activity. However, signals of apparent impending volcanic unrest are sometimes recorded without being followed by any eruption. Based on data acquired by the permanent monitoring networks run by INGV, we present cases of "failed eruption" at Mt. Etna from februery to April 2007