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International Journal of EarthSciencesGR Geologische Rundschau ISSN 1437-3254 Int J Earth Sci (Geol Rundsch)DOI 10.1007/s00531-013-0964-7

“Failed” eruptions revealed by patternclassification analysis of gas emission andvolcanic tremor data at Mt. Etna, Italy

S. Falsaperla, B. Behncke, H. Langer,M. Neri, G. G. Salerno, S. Giammanco,E. Pecora & E. Biale

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ORIGINAL PAPER

‘‘Failed’’ eruptions revealed by pattern classification analysisof gas emission and volcanic tremor data at Mt. Etna, Italy

S. Falsaperla • B. Behncke • H. Langer •

M. Neri • G. G. Salerno • S. Giammanco •

E. Pecora • E. Biale

Received: 7 February 2013 / Accepted: 21 September 2013

� Springer-Verlag Berlin Heidelberg 2013

Abstract During the spring of 2007, paroxysmal activity

occurred at the Southeast Crater of Mt. Etna, always

associated with sharp rises in the amplitude of the volcanic

tremor. Activity ranged from strong Strombolian explo-

sions to lava fountains coupled with copious emission of

lava flows and tephra. During inter-eruptive periods,

recurrent seismic unrest episodes were observed in the

form of temporary enhancements of the volcanic tremor

amplitude, but they did not culminate in eruptive activity.

Here, we present the results of an analysis of these inter-

eruptive periods by integrating seismic volcanic tremor, in-

soil radon, plume SO2 flux, and thermal data. SO2 flux and

thermal radiation are envisaged as the ‘‘smoking gun,’’ and

certifying that changes in seismic or radon data can be

considered as volcanogenic. Short-term changes were

investigated by pattern classification based on Kohonen

maps and fuzzy clustering on volcanic tremor, radon, and

ambient parameters (pressure and temperature). Our results

unveil ‘‘failed’’ eruptions between February and April 2007

that are explained as ascending magma batches, which

triggered repeated episodes of gas pulses and rock frac-

turing, but that failed to reach the surface.

Keywords Volcano monitoring � Failed eruption �Mt. Etna � Volcanic tremor � Plume SO2 flux � In-soil

radon � Thermal data

Introduction

False alarms of imminent volcanic eruptions can lead to

significant economic losses and social hardship and

severely undermine the credibility of both scientists and

authorities. The evacuation of about 73,000 people from

the surroundings of La Soufriere volcano on the French

West Indies island of Guadeloupe in 1976, without the

volcano producing the announced catastrophic eruption, is

widely considered an emblematic failure in correctly

assessing the risk posed by the volcano (Fiske 1984). Even

though in hindsight, it might be oversimplified to state that

the 1976 La Soufriere evacuation was totally unnecessary,

as the volcano indeed seems to have been close to pro-

ducing a major magmatic eruption (Feuillard et al. 1983).

That crisis points to a dilemma that represents a continuing

challenge to modern volcanology: the phenomenon of

‘‘failed’’ (or ‘‘still-born’’) eruptions (Moran et al. 2011).

Trapped between the task of giving timely warning of an

imminent and potentially dangerous eruption and that of

avoiding false alarms, scientists have to tread a rather

narrow path that is expected to become safer with ever

increasing technological effort invested in volcano moni-

toring. But how much uncertainty is due to the possibility

that signals of volcanic unrest fail to be recorded and

recognized? How much is due to a simple lack of signals?

Mt. Etna in Sicily (Fig. 1) is among the most intensely

monitored and studied volcanoes on Earth (e.g., Bonac-

corso et al. 2004; D’Agostino et al. 2013), due to its very

frequent and highly varied activity, its location in a densely

populated area, and its easy accessibility. Through a

sophisticated monitoring system run by the Istituto Naz-

ionale di Geofisica e Vulcanologia-Osservatorio Etneo

(INGV-OE) (see http://www.ct.ingv.it), scientists are

gaining a real-time picture of the state of volcanic activity

Electronic supplementary material The online version of thisarticle (doi:10.1007/s00531-013-0964-7) contains supplementarymaterial, which is available to authorized users.

S. Falsaperla (&) � B. Behncke � H. Langer � M. Neri �G. G. Salerno � S. Giammanco � E. Pecora � E. Biale

Istituto Nazionale di Geofisica e Vulcanologia, Osservatorio

Etneo, Sezione di Catania, Piazza Roma 2, 95125 Catania, Italy

e-mail: susanna.falsaperla@ct.ingv.it

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DOI 10.1007/s00531-013-0964-7

Author's personal copy

of Etna that is also useful to evaluate potential risks posed

to the *800,000 people living in its surroundings. In

recent decades, Etna displayed a rapid switch from one

eruptive style to another as well as changes in its dynamics

(Allard et al. 2006; Neri et al. 2009). In the spring of 2007,

the Southeast Crater (SEC, Fig. 1)—the most recent of the

four summit craters of Etna (formed in 1971)—reawakened

to produce paroxysmal episodes characterized by intense

Strombolian activity, lava fountaining, tephra emission,

and lava flow effusion (Andronico et al. 2008; Neri et al.

2011a). Like other similar eruptive episodes at the SEC

(Alparone et al. 2005; Behncke et al. 2006; Merucci et al.

2011; Vicari et al. 2011), these events were short-lived,

with durations from *30 min to 10 h. The first of these

eruptive episodes occurred on March 29, 2007; during the

following 3 weeks, there were almost daily increments in

volcanic tremor amplitude, but only in one case (on 11

April) they were followed by a new paroxysmal eruption at

the SEC. The repeated episodes of increased volcanic tre-

mor during April 2007 often triggered the automatic alert

systems in the control room of the INGV-OE, leading the

staff on duty to notify the pertinent authorities, including

those at the International Airport of Catania, of possible

explosive eruptions with production of ash clouds.

Seismic data, in particular volcanic tremor, are contin-

uously recorded 24 h a day/7 days a week and have proven

to be a valuable tool for volcanic monitoring and early-

warning purposes. This signal can have different sources,

which entails the necessity to verify its significance as a

forerunner to enhanced volcano dynamics by reviewing

other parameters. The emission of radon is often claimed as

evidence for the opening of fissures in the ground, which

allow the ascent of this noble gas from the shallowest

levels of the crust toward the surface. Direct evidence of

enhanced volcano dynamics can be obtained by measuring

the SO2 flux and thermal radiation. Unfortunately, these

signals are not always available: measurement of SO2

during nighttime is not possible, and unfavorable meteo-

rological conditions can prevent the measurement of both

SO2 and thermal radiation.

Fig. 1 Shaded view of Mt. Etna

and location of the reference

sensors for the monitoring of:

thermal and visual images

(square), SO2 flux (circle),

radon and ambient parameters

(triangle), seismic signal (star)

used in this study. The top left

inset is a zoom of the summit

craters area and indicates the

location of NE Crater,

Voragine, Bocca Nuova, and SE

Crater. TdF, Torre del Filosofo;

ESPC, Monte Serra Pizzuta

Calvarina; ENT, Nicolosi

(thermal camera); ENV,

Nicolosi (visual camera)

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Here, we present the results of a detailed analysis of

thermal, geochemical (SO2 and radon), and volcanic tre-

mor data during the period from February to April 2007.

We investigate how continuously monitored seismic data

and radon emission can be linked to SO2 flux and thermal

radiation. We consider the latter two as ‘‘smoking guns,’’

supporting the link of observed anomalies to changes in the

volcano dynamics.

Fig. 2 a Thermal and b visual

images of the explosion

recorded at the Bocca Nuova

crater by INGV-OE video

cameras on March 19, 2007.

c Relative temperature

measured by NewSaraterm

software from March 14 to 20,

2007; the values are calculated

from the difference between the

maximum temperature in

window a (large rectangle in

Fig. 2a) minus the maximum

temperature in window b (small

rectangle). d Daily SO2 flux

values from 14 to 28 March

2007. The strong 1-min thermal

anomaly caused by the 19

March explosion and the

beginning of the increase in the

SO2 flux are marked by red

arrows

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Chronology of the eruptive events

The year 2007 started with a period of relative quiescence in

the eruptive activity of Etna, interrupted at 17:24 (all times

are UT) on 19 March by a short-lived episode of explosive

activity at the Bocca Nuova (BN, Figs. 1, 2), one of the four

summit craters of the volcano. The event produced a thick

ash plume (Fig. 2b) (Pecora and Biale 2007). Following this

episode, on the morning of 29 March, the SEC awakened

after 3.5 months of inactivity. Violent Strombolian activity

started and soon evolved to sustained lava fountaining with

voluminous tephra production and emission of lava flows

toward the south (1.2 km long) and southeast (2.3 km long).

The paroxysm lasted *30 min and produced approxi-

mately 0.7 9 106 m3 of lava and about 0.5 9 106 m3 of

tephra. Due to clouds covering most of the volcano, thermal

observation of this event was precluded. Paroxysmal

activity from the SEC resumed again around midnight of 10

April and continued during the early morning of the fol-

lowing day, with lava fountaining and emission of a huge

tephra plume, and a lava flow advancing 3.5 km to the

southeast. Furthermore, an eruptive fissure opened during

the climax of the activity between 01:00 and 02:00 on the

southern flank of SEC with the emission of a small lava flow

0.9 km long. The activity was over *6 h after its onset

(Table 1 in Online Resource), about 1 9 106 m3 of lava

and 0.3 9 106 m3 of tephra were erupted.

Data acquisition

Several parameters are monitored at Etna every day with

sampling frequencies depending both on the technological

features of the sensors and on the range of interest for each

parameter. Collected data are either transmitted in real time

to the INGV-OE or downloaded periodically on-site. Data

investigated in this study were thermal images, SO2 flux,

in-soil radon, and volcanic tremor. In the following, we

describe the data acquired from February 21 to April 20,

2007.

Thermal data

Thermal data were recorded in the long-wave infrared

(LWIR) by a FLIR Thermavision A40 M infrared camera

located in Nicolosi, 15 km south of the summit craters

(Fig. 1). The camera is provided with a detector of

320 9 240 pixel uncooled microbolometer with a spectral

range from 7.5 to 13 lm. This device measures and images

the emitted infrared radiation from an object by using the

power of incident electromagnetic radiation through the

heating of a material with a temperature-dependent elec-

trical resistance. The measured radiation depends not only

upon the surface temperature of the object, but also on

emissivity, which is a measure of how much radiation is

emitted from the object compared to a perfect blackbody at

the same temperature. Thermal images were processed by

the NewSaraterm software (Ando and Pecora 2005, 2006)

to detect ash-rich eruptive columns, and explosive and

effusive activity (Calvari et al. 2011). The software cal-

culated the difference between maximum temperatures

measured in two different windows (a and b in Fig. 2a) in

the thermal images, the first centered on the summit craters,

the second centered on an area not affected by volcanic

activity. The temperatures recorded were much lower than

the true temperatures of the target because of: (1) the dis-

tance between the location of the thermal camera and the

target; (2) environmental factors such as humidity, baro-

metric pressure, and air temperature; (3) the presence of an

additional protective lens on the camera housing (e.g., Ball

and Pinkerton 2006; Spampinato et al. 2011). The tem-

perature values discussed here are thus relative, not abso-

lute, as they were not corrected for the aforementioned

factors. The chosen temperature range was from -40 to

120 �C.

SO2 flux

Volcanic plume SO2 flux was measured by means of the

FLAME (FLux Automatic MEasurements) ultraviolet

scanning spectrometer network (e.g., Salerno et al. 2009a;

Bonaccorso et al. 2011a). The network consists of 9

autonomous stations placed on the flanks of Etna at an

altitude of *900 m a.s.l. and a distance of *14 km from

the volcano’s summit (white circles in Fig. 1). Each

instrument scanned the sky over 156� from horizon to

horizon every *5 min during daylight. Open-path ultra-

violet spectra are reduced in real time and on-site applying

the differential optical absorption spectroscopy method

(e.g., Platt and Stutz 2007), using a modeled rather than a

measured background spectrum (Salerno et al. 2009b;

Campion et al. 2010; Merucci et al. 2011). SO2 volcanic

plume-profiles are then sent via GSM modem to INGV-

OE, where SO2 flux is calculated and in real time available

for surveillance purposes. SO2 flux time series might be not

stationary throughout the day and/or week due to the vol-

canic plume drifting toward a sector of the volcano not

covered by the FLAME network. Uncertainty for computed

emission rates ranged between -22 and ?36 % (Salerno

et al. 2009a; Patane et al. 2013). SO2 flux in this work is

shown as averaged daily flux (Fig. 3b).

Radon data

The dense 222Rn gas is a short-lived decay product derived

from 238U, with a half-life of only 3.8 days. By 2007,

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INGV-OE was acquiring continuous soil radon data from

an array of 4 detectors installed both in areas of active

faulting and close to the summit craters of Etna. Here, we

present the data collected from a station setup at Torre del

Filosofo (TdF) at an altitude of *2,950 m (Fig. 1),

*1 km south of the SEC. Radon data were collected

simultaneously with ambient parameters (soil temperature

and borehole barometric pressure) in the same borehole

Fig. 3 Temporal evolution of:

a thermal data, b daily SO2 flux,

c barometric pressure, d soil

temperature, e in-soil radon, and

f amplitude of volcanic tremor

from February 21 to April 20,

2007. Pink and light blue

vertical bands mark periods

when parametric changes were

associated with gas pulses or

with rock fracturing,

respectively (see ‘‘Stage I: 21

February–28 March’’ section for

more details). The white vertical

bands mark periods when

parametric patterns could not be

attributed clearly to gas pulses

or to rock-fracturing episodes.

The yellow arrow marks the 19

March explosion. The two

eruptive paroxysms (red

arrows) occurred on 29 March

and on 10–11 April

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every 20 min, and then downloaded onto a laptop about

every month. Given the characteristics of the borehole (see

Appendix 1), the barometric pressure measured at the

borehole bottom is assumed to be the same as the surface

atmospheric pressure; it is used as a parameter indicator of

possible environmental influences on the radon values and

of ‘‘false’’ (namely, not related to volcanic activity) vari-

ations. The radon station is located at the western margin of

a N–S trending, 50-m-wide thermal anomaly associated

with a fracture field characterized by fumarolic activity

(maximum outlet temperature in the area of about 83 �C)

(Aubert and Baubron 1988; Alparone et al. 2004). The

fumarole is due to boiling of a shallow water table and

intense diffuse degassing related to active gas release from

the central magma feeding system of the volcano, as pre-

viously reported by Aubert and Baubron (1988), Alparone

et al. (2005), Pecoraino and Giammanco (2005).

Volcanic tremor data

Continuous seismic monitoring was conducted with a

permanent network of sensors equipped with 40 s Nano-

metrics TrilliumTM seismometers. The signals were recor-

ded at a rate of 100 samples per second and transmitted to

the data acquisition center of INGV-OE in Catania. In the

time span of our case study, tectonic seismicity was low in

terms of both number of earthquakes and strain release; the

maximum magnitude (ML 2.7) was reached on April 19,

for an earthquake with origin 8 km below the sea level

under the WNW flank of the volcano (Gruppo Analisi Dati

Sismici 2013; http://www.ct.ingv.it/ufs/analisti/catalogolist.

php). Conversely, the background seismic radiation known

as volcanic tremor was characterized by repeated incre-

ments in amplitude and changes in its frequency content.

Volcanic tremor data analyzed in this study were recorded

by the Monte Serra Pizzuta Calvarina station (ESPC)

located at an altitude of 1,660 m, about 6 km S of the

summit craters (Fig. 1). ESPC provided high reliability and

continuity of data acquisition, as well as good signal

quality over the studied period. The amplitude of volcanic

tremor was calculated as root mean square (RMS) value of

the seismic signal over consecutive 20 min time spans

(Fig. 3f). The scheme of data processing followed the

method described in Langer et al. (2011), allowing to

exclude transient signals, such as spikes, wind gusts, and

also earthquakes, which are undesired in the context of

volcanic tremor analysis.

Synopsis of geochemical and volcanic tremor data

A synoptic view of thermal data, daily SO2 flux, borehole

barometric pressure and temperature, in-soil radon activity,

and amplitude of volcanic tremor is provided in Fig. 3.

Over the 59 days of observation, the SO2 emission rate was

characterized by a gradual declining trend. The mean

emission rate was 2,300 Megagram per day (Mg day-1,

standard deviation 1 r = 1,500), encompassed between the

minimum value of 700 Mg day-1 (1 April 2007) and the

maximum of 7,300 Mg day-1 (February 26, 2007). A clear

transition in the behavior of geochemical and volcanic

tremor data was observed soon after 29 March, namely

when the first paroxysmal event of 2007 occurred. The

transition marked the onset of a new cycle of eruptive

activity that lasted about 2 years. In our case study, the

period prior to the paroxysm (Stage I, from 21 February to

28 March) was characterized by substantial changes in SO2

flux and soil radon emission (Fig. 3b, e), while the fol-

lowing period (Stage II, from 29 March to 20 April)

showed a further increase in SO2 flux, and frequent

increments in the volcanic tremor amplitude and thermal

data, but no changes in the soil radon trend (Fig. 3f, b, a).

Stage I: 21 February–28 March

During this stage, the SO2 flux displayed three waxing and

waning phases between 24 February and 5 March, from 6

to 12 March, and from 19 to 27 March (Fig. 3b). The last

phase followed the brief episode of explosive activity at the

BN on 19 March. This explosion was recorded by visual

and thermal video cameras, which documented a 1-min-

long thermal anomaly (Fig. 2a–c) (Pecora and Biale 2007).

After the explosion, the SO2 flux continued to increase,

peaking at 6,200 Mg day-1 on 23 March (Fig. 3b). After-

ward, the emission rate declined till 29 March, when the

first episode of paroxysmal eruptive activity in 2007

occurred.

In this stage, many conspicuous (positive or negative)

variations of the radon activity (Fig. 3e) were associated

with soil temperature changes (Fig. 3d). Their occurrence

(highlighted by pink bands in Fig. 3) alternated with

periods of increased radon emission with no change in soil

temperature (highlighted by light blue bands in Fig. 3). The

apparent relation between changes in radon emission and in

soil temperature is as intriguing as the absolute values of

radon variations themselves. The role of soil temperature as

a proxy of steam/heat flux and more generally of the bulk

gas flux is critical in identifying the source responsible for

the variations observed in radon emission. This source can

be ascribed to gas pulses, when increased flux of deep and

hot magmatic gas produces increases in soil temperature

associated with changes in radon emission, as earlier

described in the same area by Pinault and Baubron (1996).

Alternatively, it can result from rock-fracturing episodes,

shallow processes that produce an increase in radon

emission from newly formed rock surface in contact with

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the carrier gases (Thomas 1988), without an increase of

deeper gases that carry heat, and hence without associated

temperature changes. Before the 29 March paroxysm, we

identify three time spans with an increase in radon and no

significant change in soil temperature and three time spans

characterized by high soil temperature associated with

either a concurrent increase or with a decrease in radon

(Fig. 3c–e). Increases both of temperature and of radon in

soil are due to a carrier effect of increased flux of water

vapor, as mentioned above for gas pulses. However,

increases in temperature associated with radon decreases

could be the result of a radon dilution effect, when the flux

of water vapor/bulk gas is so high as to overwhelm the

shallow source of radon isotopes, similar to the effect

produced by high CO2 fluxes (Giammanco et al. 2007). An

alternative explanation would take into account a physical

mechanism that removes soil radon while causing an

increase in ground temperature at the same time. A pos-

sible explanation for this would be near-surface conden-

sation in the ground of an upward moving gas phase, of

which water vapor is a major constituent and CO2 (or

another non-condensable gas) makes the remaining part

(see also Appendix 1).

Only two short (\2-day long) time intervals in February

and March had no change in temperature or in radon (white

bands in Fig. 3), highlighting temporary low values of the

radon activity. Overall in Stage I, both volcanic tremor and

thermal data remained relatively steady and no apparent

direct link was observed with SO2 flux and radon emission.

The only exception was a short-lived, moderate increase in

amplitude of volcanic tremor and thermal measurements on

19 March.

Stage II: 29 March–20 April

After 29 March until 20 April, the SO2 emission rate lev-

eled out at mean value of *1,800 Mg day-1 with the

exception of the period between 8 and 11 April in which

the geochemical signal peaked at 4,700 Mg day-1. This

degassing phase, likely associated with the 10–11 April

paroxysmal episode, displayed a maximum value about a

half of that measured between 24 February and 5 March in

Stage I (Fig. 3b). Unlike the SO2 flux, radon was rather

insensitive to the occurrence of both the 29 March and

the 10–11 April paroxysms, having values below

5,000 Bq m-3 on average throughout the whole Stage II

(Fig. 3). Conversely, after the relatively low values of

volcanic tremor and thermal data until 29 March, both

parameters showed major changes. Volcanic tremor peaked

at *2.3 9 104 nm s-1 during the paroxysmal activity of

29 March (Fig. 3f). With the cessation of the eruptive

activity, the amplitude dropped again reaching values of

*0.08 9 104 nm s-1. However, the next day, RMS values

rose very much in the same manner as on the previous day,

but they were not associated with any visible eruptive

activity at the surface. Similar temporary increases in the

volcanic tremor amplitude not associated with any volcanic

activity were observed at almost regular daily intervals

over the next 12 days. By late 10 April, a new increase of

the volcanic tremor amplitude to *1.6 9 104 nm s-1

finally culminated in the second episode of lava fountain-

ing (red arrow in Fig. 3). Again, during the following days,

temporary increments in the RMS volcanic tremor ampli-

tude triggered the automatic alert systems in the control

room of the INGV-OE. However, none of these was

accompanied by any eruptive phenomena. On average, the

20 major peaks (paroxysms excluded) of volcanic tremor in

Stage II had RMS amplitudes of 0.47 9 104 nm s-1,

namely *5 times smaller than the maximum value

achieved on 29 March (Fig. 3f).

Thermal data had a few gaps in the time series due to

cloudy skies, which hindered the visibility of the summit

craters. Such problems unfortunately occurred also during

the two aforementioned paroxysms. Nevertheless, thermal

measures highlighted numerous peaks, the largest of which

followed the end of the 10–11 April paroxysm (Fig. 3a). To

accomplish a comparative seismic record and video image

analysis, we examined the footage recorded during the 20

episodes in which volcanic tremor peaked. The analysis of

these visual records disclosed three cases in which the

visibility was poor due to clouds or to bad weather con-

ditions. Only in one case (on 19 April) out of the remaining

17 episodes, fair visibility conditions revealed an appre-

ciably warmer degassing than usual from the SEC (Table 1

in Online Resource).

KKAnalysis for pattern classification

Multidisciplinary analyses have pros and cons. An

appealing pro is that a multiparametric approach may

unveil subtle, interconnected changes in a huge amount of

information, enhancing the potential detection of signifi-

cant links between parameters and variations related to the

monitored natural phenomena. On the other hand, multi-

disciplinary studies come along with puzzling data sets,

thus making the application of specific analytical tools

necessary. For these reasons, we took advantage of a free

software package named KKAnalysis (Messina and Langer

2011), which proved to be quite versatile in the multipa-

rameter analysis of both geochemical (Corsaro et al. 2013)

and geophysical (Langer et al. 2011) data. KKAnalysis

exploits unsupervised pattern classification techniques,

combining the application of Kohonen maps (Kohonen

1984, 2001) or self-organizing maps (SOM) and cluster

analysis, in particular fuzzy clustering (Zadeh 1965).

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Langer et al. (2011) applied KKAnalysis to volcanic tremor

data recorded on Etna during eight eruptive events in 2007

and 2008. In the multivariate problem they dealt with,

Langer et al. (2011) applied KKAnalysis to patterns con-

taining the spectral content of the seismic signal. The

feature vectors that formed each pattern were made up of

spectral amplitudes measured in specific frequency bins.

Based on the results of KKAnalysis, the authors were able

to identify significant changes in the characteristics of

volcanic tremor data immediately preceding (from 1 h to

tens of hours) episodes of lava fountaining at the SEC in

2007–2008, and the onset of the flank effusive activity in

May 2008. In 2010, the implementation at the control room

of INGV-OE of a real-time pattern classification tool based

on KKAnalysis for the analysis of volcanic tremor fol-

lowed in the wake of these results. In the on-line pattern

classification now routinely running at INGV-OE, the most

recent patterns (that is the ones related to the last 24 h) are

pooled together with a reference data set, which encom-

passes past eruptive episodes, in particular the lava foun-

tains in 2007–2008 and the flank eruption initiated on May

13, 2008. The use of the reference data set allows us to

directly compare the signal characteristics of the new data

to those found during eruptive events, providing efficient

automatic warning when a critical status of the volcano is

recognized.

In the present study, we applied KKAnalysis following a

multiparametric strategy of analysis similar to that of

Langer et al. (2011). In particular, we focused on volcanic

tremor, radon, and ambient parameters as: (1) they pro-

vided information around-the-clock, and (2) the data con-

tain no gaps during the study period. Conversely, we

discarded from this application the SO2 flux data, for the

lack of nighttime measurements that precludes a round-the-

clock coverage, and thermal data, for their discontinuity

due to repeated technical failures and frequent presence of

weather clouds. In the Appendix 2, we briefly outline basic

concepts on SOM and fuzzy clustering. More details can be

found in Messina and Langer (2011), Langer et al. (2011),

and in the ‘‘Extended Documentation,’’ which can be freely

downloaded together with KKAnalysis from http://earthref.

org/ERDA/erda, searching for ‘‘KKAnalysis.’’

Results from pattern classification

We applied a day-by-day pattern classification based on the

spectral characteristics of volcanic tremor (see Appendix

3), pooling the aforementioned 2007–2008 reference data

set together with each of the 59 days from 21 February to

20 April, following the same scheme as in the real-time

implementation mentioned above. The results of the clas-

sification were depicted in the form of colored triangles,

which combine the output of SOM and fuzzy clustering

(Figs. 4, 5). The color code offers a convenient represen-

tation, as it highlights the temporal development of the

different clusters of signal during the study period. SOM

colors have deep blue tones during stages of relative vol-

canic quiescence. As volcanic unrest starts, red tones

become visible and grow progressively more intense. For

instance, in Fig. 4a, we notice deep purple and red triangles

with cluster #2 and #3 during the paroxysm on 29 March.

The spectrograms representing the original feature vectors

for the time span 29–31 March allow us to get an in-depth

view of the spectral characteristics during the paroxysmal

episode (the lava fountain) and three episodes of seismic

unrest immediately following that paroxysm (see also

Fig. 3f). Blue SOM colors on the early morning of 29

March correspond to rather low levels of all spectral

amplitudes of the seismic radiation. Red tones increase

together with the spectral amplitude (volcanic activity

reached a climax at 05:25, see Table 1 in Online

Resource), at the same time as the spectral content

becomes narrower and peaked at *1.5 Hz (Fig. 4a). Note

that by 29 March, the prevailing cluster switches progres-

sively from #1 to #2 and to #3, and then back again to #1 at

the end of the eruptive episode. However, the return to

background conditions was far from being completed. The

spectrograms of 30 and 31 March are again characterized

by increasing amplitudes and narrow spectral content

(Fig. 4b, c). These changes are also mirrored in the clas-

sifier (FC), where we find patterns marked by increasing

red tones and prevailing cluster #2 and #3 (Fig. 4b, c).

Similar variations were repetitively observed in the output

of the classifier until 20 April and are described in Table 1

(Online Resource), which lists all the episodes where the

same cluster #3 appeared. These episodes temporally cor-

respond to the major peaks in the diagram in Fig. 3f, which

were defined as ‘‘failed paroxysms’’ by Langer et al.

(2011), who were the first to observe their missing link to

any eruptive activity.

With the aim of comparing the output of KKAnalysis for

volcanic tremor with radon and ambient parameters (soil

temperature and barometric pressure), we repeated the

application of the software defining the patterns in various

configurations. The reference data set containing the clas-

sification of 2007–2008 episodes for volcanic tremor was

not considered in these new applications, as no similar a

priori knowledge, namely previous pattern classification,

was available for radon and ambient parameters. In Fig. 5,

we present the results of pattern classification during the

week from 14 to 20 March. This time interval was chosen

for two reasons. Firstly, it allows us a comparison among

the outputs of the various configurations of patterns; sec-

ondly, it highlights an internal change shortly preceding the

episode of explosive activity at the BN on 19 March

(Fig. 2a, b). Figure 5c shows the application of pattern

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Fig. 4 Results of KKAnalysis and spectrograms of volcanic tremor

from March 29 to 31, 2007 [the part pertaining 29 March was partially

presented in (Langer et al. 2011)]. Colored triangles combine the

results of SOM and fuzzy clustering; the results of fuzzy clustering

(FC) with the partition in three clusters (distinguished by red, green,

and blue color) are shown below each diagram. The vertical position

of the colored triangles in the upper panels depends on the cluster to

which a pattern prevalently belongs. The fuzziness can be immedi-

ately inferred from the dark gray bars, which cover the underlying

light gray area according to the value of the prevalent cluster

membership. In the spectrograms, frequency changes in warm colors

are associated with the paroxysmal activity on March 29, 2007, but

also with temporarily unstable internal conditions of the volcano-

feeding system (see spectrograms of 30 and 31 March 2007). The

arrow marks the climax of volcanic activity at 05:25 during the lava

fountain on 29 March; thick black bars mark the climax of the three

following episodes of seismic unrest (SU), which did not lead to any

eruptive event. PSD stands for power spectral density

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classification to tremor data only. Cluster #1 and green

triangles prevail during nighttime. During daytime, some

patterns belonging to cluster #2 and #3 show up, and the

colors of the triangles turn to violet. These short-lived

daytime changes are often noticed at the station ESPC and

can be attributed to manmade noise (see Langer et al. 2011,

in particular their Fig. 13c). Between 19 and 20 March,

however, violet triangles belonging to cluster #3 form a

temporary stable regime.

The results of the application to radon and ambient

parameters are depicted in Fig. 5f. Violet to purple trian-

gles and cluster #3 characterize the first 5 days (14–18

March). From Figs. 3e and 5e, it is evident that in these

days, the rate of radon emission was high. Between 19 and

20 March, the radon emission sharply decreased (Fig. 5e);

accordingly, the corresponding patterns were assigned to

green colors and the cluster became #1 (Fig. 5f). Overall,

in the time span examined, we observe a neat negative

correlation between the patterns of volcanic tremor and

those made up of radon, pressure, and temperature (Fig. 5c,

f).

The results obtained considering volcanic tremor—here

with 62 spectral components—radon, and ambient param-

eters depict triangles mainly from pink to violet during the

first 5 days (Fig. 5g). Green colors with cluster #3 prevail

in the last 2 days. Despite these variations, the results

remain similar to those shown in Fig. 5c. Having 62

spectral components related to volcanic tremor and only

three related to radon and ambient parameters, there was

the risk of overweighting the tremor components. For this

reason, a feature reduction was obtained carrying out the

principal component analysis on the 62 original spectral

components of tremor data and then extracting the largest

three principal components. In the fourth configuration

(Fig. 5h), using the feature reduction (from 62 to 3) of

volcanic tremor data and merging the patterns with radon

and ambient parameters, the change from cluster #1 to #3

becomes sharper and more anticipated (start at about 07:00

on 19 March) than in Fig. 5c, f, g (start at 17:00). In

addition, all the small fluctuations visible in Fig. 5c, g fade

away. As the onset of explosive activity at the BN was at

17:24 on 19 March (Fig. 2a, b), the change unveiled by

pattern classification in Fig. 5h occurred 10 h in advance.

Discussion

Earthquakes and high gas emission rates usually herald and

accompany volcanic unrest under the action of tectonic

stresses (e.g., Daag et al. 1996; Werner et al. 2011). In the

case of the eruptive activity at Etna, however, summit

eruptions are associated with strong changes in volcanic

tremor without any tectonic seismicity (e.g., Cardaci et al.

1993; Patane et al. 2004; Behncke et al. 2009). A new

phase of paroxysmal summit activity started in spring 2007

with a lava fountaining episode on 29 March. This event

had no tectonic precursors, similar to the numerous erup-

tive episodes reported by Alparone et al. (2003) at the SEC

in early 2000. Accordingly, the main engine in the trigger

of this kind of activity has to be found in the dynamics of

magma, of magmatic fluids, or in their combined action.

Distance variations along GPS baselines lengthened almost

continuously during our study period, thus revealing a mild

inflation of the volcano (Aiuppa et al. 2010). However, this

evidence offered no clues on the type of source producing

the inflation, this being the result of the ascent of magma,

of gas, or both toward the surface. Corsaro and Miraglia

(2009) carried out petrological analyses of the 2007–2008

volcanic products from erupted pyroclasts and lavas. Their

results show a clear chemical evolution of the magma,

from a more primitive composition during the first parox-

ysm on 29 March toward increasingly differentiated com-

positions in the products of the following eruptive events.

This chemical evolution is indicative of different fluid

contents (i.e., differential loss of volatiles from an

ascending magma batch which caused accumulation of the

most soluble gas species in the residual melt) and depths of

the magmas erupted. Petrological indication of a primitive

and volatile-rich magma erupted in spring 2007 supports

our interpretation that the high values of SO2 flux and

radon emission highlighted in Fig. 3 throughout Stage I

(between 21 February and 28 March) are related to a new

recharging phase of the volcanic system after the 14 July–

14 December 2006 eruption, mostly promoted by gas

overpressure. As radon measurements are strongly con-

trolled by the local state of stress within relatively shallow

rocks, the remarkably high radon concentrations observed

in Fig. 3e during Stage I are likely the results of micro-

fracturing processes acting in the summit of the volcano.

The TdF station is indeed sensitive to the pressure induced

by fluid/magma dynamics, as documented by the compa-

rably high emission values measured about 10 h before the

paroxysmal activity on July 20, 2006 (Neri et al. 2006). It

Fig. 5 Data, spectrograms, and results of KKAnalysis from March 14

to 20, 2007. a Amplitude of volcanic tremor, b spectrograms of

volcanic tremor, d ambient parameters (pressure and soil tempera-

ture), e radon. Results of the application of KKAnalysis refer to:

c volcanic tremor, f radon and ambient parameters, g volcanic tremor

(62 features), radon, and ambient parameters, h volcanic tremor (with

feature reduction from 62 to 3), radon and ambient parameters.

Colored triangles combine the results of SOM and fuzzy clustering;

the results of fuzzy clustering (FC) with the partition in three clusters

(distinguished by red, green, and blue color) are shown below each

diagram. The brown and orange arrows in (h) mark, respectively: the

change from cluster #1 to #3 at *07:00 on 19 March, highlighting a

strong modification in FC, and the start of explosive activity at the BN

at 17:24 on the same day. PSD stands for power spectral density

b

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is noteworthy that the radon emission started to decrease

abruptly 34 h before the explosive activity on March 19,

2007 (see Fig. 5e) and that afterward, apart from a new

intense relative maximum on 23 March (Fig. 3e), radon

activity remained at low levels until the end of the study

period.

Mt. Etna is well known for having a continuous

degassing regime from its summit craters, which makes

this volcano one of the largest contributors of magmatic

gases and in particular of SO2 worldwide (e.g., Allard

1997; Allard et al. 2006; Caltabiano et al. 2004; Aiuppa

et al. 2008). This is a sign of persistent and lively dynamics

of magmatic fluids inside Etna’s feeding conduits. Inde-

pendent evidence of the persistent internal dynamics of

magmatic fluids is also given by volcanic tremor, as doc-

umented from the pioneering study by Caloi et al. (1948).

The common link with the magmatic source makes both

volcanic fluids and tremor key parameters in case of vol-

cano unrest (e.g., Leonardi et al. 2000; Watson et al. 2000;

Young et al. 2003; Palma et al. 2008). This is the reason

why their temporal relationship at Etna between 2007 and

2008 was investigated in various multidisciplinary studies

aimed at combining the high-quality data acquired by

means of the permanent monitoring system run by INGV-

OE (Bonaccorso et al. 2011a, b; Aiuppa et al. 2010).

Imaging of magma conduits using tremor sources and long-

period events highlighted a shallow plumbing system

active from October 2006 to December 2007. It consisted

of two connected NNW–SSE- and NW–SE-oriented dike-

like bodies, extending from the sea level to an elevation of

*3,000 m a.s.l. almost exactly below the SEC (Patane

et al. 2008). We surmise that this part of the plumbing

system played a fundamental role in the upward transport

of gas-rich magma without earthquake seismicity. This

hypothesis is compatible with: (1) the relative quietness of

volcanic tremor during Stage I (tremor sources located

between sea level and an absolute altitude of 2.5 km above

sea level, according to Patane et al. 2008); (2) the occur-

rence of peaks in the RMS values of volcanic tremor during

Stage II (Fig. 3f), due to activation of the shallowest por-

tion of this plumbing system (absolute altitude of tremor

sources between 2.5 and 3 km above sea level, that is less

than a few hundred meters below the summit, with tremor

sources closer to ESPC). In their medium–long-term ana-

lysis of crater plume CO2/SO2 ratios during 2007–2008,

Aiuppa et al. (2010) found decreasing values concurrent

with the March 29, 2007 paroxysm and the following

eruptive episodes, and they concluded that pressurization

of magma within the central conduit below the summit

craters might be a trigger for the eruptive episodes at the

SEC. Our investigation provides further support to this

hypothesis, shedding light on the subtle changes in gas and

volcanic tremor data observed in the short term during the

study period. Pulses of SO2 flux unveil degassing cycles

with sustained gas segregation, which along with the radon

measurements document vigorous dynamics of the vol-

cano-feeding system despite the absence of surface activity

(Fig. 3b, e). An emblematic example of such intense

internal dynamics in a period of apparent quiescence is

given by the results of pattern classification from 14 to 20

March (Fig. 5). The separate classification of the parame-

ters reveals different time scales of evolution (Fig. 5c, f).

However, looking for changes heralding or accompanying

the explosive episode on 19 March, we found that not only

patterns of volcanic tremor and combined radon/ambient

parameters had anti-correlated variations (Figs. 5c, f), but

also the SO2 flux underwent a concurrent increasing trend

starting from 19 March (Fig. 2d). Therefore, following the

above discussions, it is reasonable to infer that the anti-

correlated variations—as highlighted from the results from

pattern classification—and the SO2 trend reflect a common

source process.

Application of pattern classification to volcanic tremor

data confirms that its spectral characteristics during epi-

sodes of RMS peaks in Stage II were similar to those

observed during paroxysms, even though energetically

minor (see spectrograms in Fig. 4). In seismic records, the

sequence of RMS peaks of volcanic tremor takes the form

of the so-called banded tremor, that is repeated temporary

increments in the energy of seismic radiation typical of

dynamic geothermal systems, such as geysers (e.g., Kieffer

1984). We surmise that the vigorous gas exsolution

recorded in Stage I recharged the system and led to the

accumulation of gas bubbles in a foam layer that periodi-

cally collapsed. The rather periodical behavior might be

regulated by the size of the roof in a shallow reservoir as

for the model proposed by Vergniolle and Jaupart (1990),

or by the geometry of the plumbing system articulated in

dikes as proposed by Menand and Phillips (2007). Seismic

unrest occurred almost daily (see insert in Fig. 3) and with

rather comparable energy. This can be explained as the

result of a cyclic pressure build up and release within a

non-destructive source, which eventually overcame the

overpressure threshold only with the arrival of new gas-rich

magma, thus producing the paroxysms on 29 March and

10–11 April.

Conclusions

After the end of the July–December 2006 eruption, periods

of non-eruptive seismic unrest were recorded at Mt. Etna in

the form of enhancements in volcanic tremor amplitude.

We found that they were particularly numerous from the

end of the paroxysmal activity of March 29, 2007. Based

on a multidisciplinary data set, which integrated the long-

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term temporal evolution of gas measurements, thermal

data, and seismic radiation, we conclude that between

February 21 and April 20, 2007 there were repeated epi-

sodes of gas pulses and rock fracturing induced by fluid

pressure build up, which led to aborted eruptions.

Analyses on geochemical and seismic data by the

application of pattern classification revealed intriguing

aspects, such as the possibility of detecting forerunning

signals of explosive activity, as observed 10 h before the

19 March event at the Bocca Nuova crater. The outcomes

from the merging of data from various parameters pro-

vide indeed more robust information than the analysis of

a single parameter (Fig. 5). On the other hand, changes

similar to those observed in the volcano feeding system

in early 2007 would additionally require a separate

classification, not only for the different time scales of

the processes involved, but also for the complex rela-

tionship between some parameters (e.g., radon and soil

temperature).

We consider the application of unsupervised classifica-

tion techniques to volcanic tremor and radon data a

promising tool for monitoring purposes and specifically for

the surveillance of active volcanoes.

Acknowledgments T. Caltabiano and F. Mure are acknowledged

for their technical assistance in the FLAME Network. We gratefully

thank D. Tomas for his comments on radon measurements and C.

Corazzato for her careful reading of the manuscript and constructive

criticism.

Appendix 1: Notes on in-soil radon measurements

Radon is a radioactive, noble gas emitted from soil and

soluble in water. Especially in volcanic/geothermal areas

with vigorous circulation of magmatic or hydrothermal

fluids at shallow depth, in-soil radon emissions are the

surface expression of convective flow of gases along

fractures. Convection facilitates the transport of radon

from depth to the surface, and the abundance of the radon

transported depends also on the abundance of uranium-

bearing rocks. In recent years, promising results were

obtained by applying soil gas radon prospecting to the

detection of hidden (but seismically active) and outcrop-

ping faults (Burton et al. 2004; Cigolini et al. 2007; Neri

et al. 2007, 2011b; Giammanco et al. 2009; Siniscalchi

et al. 2010). In active volcanic environments, increments

in in-soil radon emission that are not related to environ-

mental influences (mostly earth tides and/or changes in

atmospheric parameters, such as rainfall, barometric

pressure and air temperature) can be the expression of

changes in the mass flux within hydrothermal systems

(Durrance and Gregory 1990). These changes can be

related to increased output of magmatic gas and/or to

increased permeability of the rock due to newly formed

stress-induced fractures. On Mt. Etna, continuous soil

radon monitoring was also used to recognize precursory

signals to paroxysmal eruptive events (e.g., Cox et al.

1980; Alparone et al. 2005; Cigolini et al. 2005; Neri

et al. 2006; Giammanco et al. 2007).

Our station TdF was equipped with a BarasolTM probe

(Algade, France) placed into a borehole at a depth of 1.6 m

in pyroclastic deposits produced by the 2002–2003 erup-

tion (e.g., Allard et al. 2006). According to the specifics

given by the manufacturer, the borehole where the Bara-

solTM probe was installed was not isolated from the surface

(the borehole is capped with a small semipermeable

Mylar� sheet in order to make gas flow out of the hole

while preventing rain or snow from entering it). The probe

is held some 40 cm above the borehole bottom, as in a

fumarolic environment with a lot of water vapor, this setup

lets the gas flow freely up along the borehole (and thence

out of it through the semipermeable cap), preventing water

vapor from condensing on the sensor. The probe at the TdF

site was also modified by adding a robust filter close to the

sensor, in order to prevent any condensation of water vapor

on its surface.

Under steady flow conditions, water condensation would

cause both an increase in soil temperature due to heat loss

and an apparent increase in radon activity in the condens-

ing zone, due to the lower proportion of residual water

vapor and the higher proportion of non-condensable gases

(particularly CO2) after water condensation. However, as

already proposed by Aubert and Baubron (1988), conden-

sation of CO2-rich hydrothermal fluids rising just south of

TdF through a N–S-trending fault (namely, very close to

the location of our radon probe) produces a soil tempera-

ture anomaly right on the fault plane and an eastward lat-

eral flow of condensed water. This process, favored by the

high soil permeability in the area, was traced at the surface

by Aubert and Baubron (1988), measuring a concurrent and

decreasing anomalous emission of CO2 and radon along the

presumed direction of water flow. Both CO2 and radon

were evidently released by the flowing water after being

trapped in it, and hence were removed by water from the

residual soil gas after condensation. If the latter mechanism

is true, then the increased condensation of water vapor at

shallow depth, coupled with increased soil temperature and

decreased soil radon activity, would be caused by a

decrease in the input of high-enthalpy magmatic fluids due

to changes in volcanic activity. However, this would be in

contrast with the overall increase in volcanic activity

observed after the onset of the 2007 sequence of parox-

ysmal events. Therefore, we would be inclined toward the

mechanism of radon dilution to explain the observed

variations in temperature and radon activity. Further stud-

ies that will include monitoring of soil CO2 will possibly

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clarify the interaction of temperature, condensation pro-

cess, and radon activity.

Appendix 2: SOM and fuzzy clustering in KKAnalysis

Kohonen maps or SOM are unsupervised pattern classifi-

ers, which ‘‘learn’’ from data of whatever nature provided

in numerical format. SOM are made up of a number of

nodes, each one representing a feature vector, and being

prototypes of a number of patterns. During the so-called

training phase, the node weights (feature vector elements)

are adjusted minimizing the differences between the nodes

and the original pattern. A pattern is assigned to the node

for which the smallest difference is encountered; this node

is referred to as the Best Matching Unit (BMU) for that

pattern. Once KKAnalysis concludes the training phase of

the SOM, the nodes are mapped into a 2D representation

space applying the principal component analysis (Fig. 6a).

KKAnalysis exploits an appealing property of SOM, often

referred to as topological fidelity, which means that pat-

terns represented by neighboring nodes in the SOM are

close to each other also in the original data space.

KKAnalysis applies a color coding to the nodes depending

on their position on the map and assigns to each pattern the

color of the BMU to which it belongs. Given the topo-

logical fidelity, similar patterns show similar colors.

Accordingly, changes of pattern characteristics in time are

visualized as output of KKAnalysis by creating a sequence

of colored symbols.

Along with SOM, KKAnalysis exploits the so called

fuzzy clustering algorithm. This algorithm allows the

potential allocation of a pattern into several clusters (or

classes) according to a vector, which identifies its mem-

bership (Fig. 6b). A pattern with membership 1 belongs

exclusively to one cluster. In case of membership \1, the

vector components indicate to which degree a pattern

belongs to various clusters. A membership \0.5 means

rather fuzzy. Combined with the colors of the nodes of the

SOM, fuzzy clustering allows the identification of transi-

tional regimes between clusters, highlighting the develop-

ment of the characteristics of a multidimensional feature

vector.

Appendix 3: Application of KKAnalysis to seismic

and radon data

Before using KKAnalysis, we preprocessed our seismic

data recorded at ESPC (Fig. 1), calculating the spectral

content of volcanic tremor from February 21 to April 20,

2007. The spectral analysis was carried out applying the

Short Time Fourier Transform. We followed Langer et al.

(2011), preparing feature vectors with 62 components, each

of which corresponded to power spectral densities in fre-

quency bins of *0.29 Hz. The total frequency range

covered was from 0.15 to 18 Hz. Each pattern represented

5 min of tremor data, with a total of 288 patterns per day.

The whole amount of patterns obtained was 16,992, rep-

resentative of 59 consecutive days. For the setting of

KKAnalysis, we fixed the size of the SOM at 40 9 9

nodes, the same size chosen by Langer et al. (2011), and

similarly we considered three clusters for the fuzzy clus-

tering. Patterns representative for the 2007–2008 eruptive

episodes analyzed by Langer et al. (2011) were considered

as a reference data set, as they represented spectral char-

acteristics of volcanic tremor shortly before, during, and

immediately after climactic volcanic activity.

Exploiting our experience on volcanic tremor classifi-

cation, we applied KKAnalysis to radon data along with

barometric pressure and soil temperature measurements

acquired at the same site with the same sampling rate, as

the latter two parameters affect radon emission (e.g., Au-

bert and Baubron 1988). Also in this case, we preprocessed

the data set for numerical purposes, applying a logistic

normalization function to the raw data. Overall, we

Fig. 6 Principles of functioning of KKAnalysis: a Kohonen maps,

b fuzzy clustering. The input data in a can be of whatever nature

though expressed in a numerical form. In our application, data were

given by the spectral content of volcanic tremor, radon values,

temperature, and pressure. The vector components (red, green, and

blue color) in b indicate to which degree, expressed by the results of

fuzzy clustering (FC, ranging 0–1), a pattern belongs to various

clusters (three in the example)

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obtained some 16,000 patterns sampled with a time interval

of 5 min, covering a total of 59 days.

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