Acoustic Emission on the Peteroa Volcano (2009-2011)

Isabel LÓPEZ PUMAREGA1, Cynthia HUCAILUK1, Giovanni P. GREGORI2, 3, 4, 5, Gabriele

PAPARO2, 6, José RUZZANTE1, 7, Nicolás NÚÑEZ1, Giuliano VENTRICE2, 5, 8, 9, Claudio RA-

FANELLI3.

1 ICES (“International Center for Earth Sciences”), National Atomic Energy Comission,

Av. General Paz 1499, Buenos Aires, Argentina Phone: +54 11 6772 7766; lopezpum@cnea.gov.ar; hucai-

luk@cnea.gov.ar, ruzzante@cnea.gov.ar; nnunez@cnea.gov.ar 2 ICES - International Center for Earth Sciences – Italy.

3IDASC - Istituto di Acustica e Sensoristica O. M. Corbino – CNR, Rome, Italy.

4 IEVPC – International Earthquake and Volcano Prediction Center, http://ievpc.org/index.html

5 SME srl - Security, Materials, Environment, Roma, info@sme-ae.it

6 Italy Embassy in Argentina, scient.buenosaires@esteri.it

7 UTN, Delta Regional Faculty, Buenos Aires, Argentina.

8 PME Engineering -Progettazione Macchine Elettroniche, giuliano.ventrice@sae-tec.it

9 SAE Technology, via Stazione Ottavia 21/C, 00135 Roma, Italy.

Abstract The first Acoustic Emission (AE) Station in the Andes Mountains was established at Peteroa volcano since 2003.

The volcano is located in Mendoza province, at the border between Argentina and Chile inside a region with 31

active volcanoes. This Andean segment shows an intense volcanic activity with 283 eruptions since the begin-

ning of the XIX century. The volcano is monitored by an Argentinian/Italian cooperation. The time variation of

the primary endogenous fluid supply is monitored including the consequent effects on the porous materials of the

volcanic edifice, and the role of Earth’s tides. The objective of this investigation is to analyse the AE signals and

their relation with the natural events during the period April 2009 - March 2011. Signals correlating changes in

Peteroa AE with big earthquakes, Chile (02-27-2010) and Japan (03-11-2011) are also studied.

Keywords: Acoustic Emission (AE), micro seismic, volcano, earthquakes, Andes Mountains.

1. Introduction

Peteroa Volcano is located on the middle Andes Mountains chain, 35°14’ S and 70º35’ W,

4135 m over sea level, 94 km to the West from Malargüe city, Mendoza province, Argentina

(see Figure 1). The region is only accessible from November through April (summer time)

because of the accumulated snow and the conditions on the access way. It is at the border be-

tween Argentina and Chile inside a region with 31 active volcanoes. This Andean segment

shows an intense volcanic activity with 283 eruptions since the beginning of the XIX century.

Figure 1. Peteroa volcano location, Mendoza, Argentina (Reference 1).

30th European Conference on Acoustic Emission Testing & 7th International Conference on Acoustic Emission University of Granada, 12-15 September 2012

www.ndt.net/EWGAE-ICAE2012/

The area is totally inhospitable. Because the volcano is on the international Argentina-Chile

limit, during summer time, the Argentine Squadron 29 of National Gendarmerie “Grupo Los

Azufres” works there. The Peteroa Volcano is part of the Planchón-Peteroa volcanic complex,

which has 20 historic eruptions. It is a polygenetic volcano, formed during a lot of eruptions

during the geological time [1]. Its “mean quiet period”, so called Recurrence Interval, calcu-

lated from the known eruptions, is 18 years, and its Rest Period is 9.1 years [2]. All around

there are sources of thermal baths.

During year 2003, on the base of Peteroa volcano (2300 m over sea level), the first Acoustic

Emission (AE) Laboratory on the Andes Mountains was set up [3]. This is part of a more am-

bitious research project for monitoring all this mountain chain with AE. The Second AE Sta-

tion is located on Cerro Blanco, at San Juan province, northern way from Mendoza [4-7]. The

third is being constructed at Cacheuta (Mendoza province) [8]; the fourth is planned at Tierra

del Fuego Island. The analysis of AE signals from Cerro Blanco is presented in an additional

paper at this Congress [9].

Figure 2. Actual complete installation at Peteroa volcano. Peteroa outcrop on right side.

Figure 3. Image captured on May 2, 2011, 9:55 a.m., by the video camera at Peteroa volcano, during

its last eruption. The rock on the left corner is part of the outcrop where the AE sensors are installed.

It is important to consider that the area has no electrical power, or mobile telephone service,

and only satellite communications are possible. The electricity is due to solar panels and re-

cently from a wind turbine generator. In figure 2 the complete installations can be seen. From

some years ago, Peteroa is just now a multi parametric natural laboratory. Recently a small

meteorological system was installed. CO2 and Radon concentrations and total water precipita-

tions are now measured. During 2010, a video camera pointing to the area of craters, so it is

now possible to take pictures of the continuous fumaroles on demand. At figure 3 an image of

Peteroa volcano last eruptions, captured by the video camera can be seen. An infrasound sys-

tem and other equipments to measure local variations of magnetic and electric fields will be

installed next summer. With ICES support several campaigns through the Peteroa craters

were made including thermographic capture of images [10]. Several flights around, especially

during the last eruptions were carried out. At figure 4, some aerial images of Peteroa craters

are shown.

Figure 4. Aerial and termographic images of Peteroa volcano (courtesy of Pablo Pennas).

Since more than a decade before, AE has proven that it can help understand what is happen-

ing inside the outcrop where the AE sensors are located, that is to say, to follow its stress

changes produced by thermal changes, tectonic strain, earth tidal, endogenous pressure by

fluids, earthquakes, volcano eruptions, etc. [11-22].

AE is effective for monitoring ground deformation and temporal variation of its porosity. It is

complementary to seismic information, related to the same area, though AE and earthquakes

focus on observational evidence concerned with substantially different space and time-scales.

AE information is pertinent (i) either for geodynamically stable areas, where it probes the

diurnal thermal and/or tidal deformation, (ii) or for seismic areas where it provides some as

yet unexploited precursors, (iii) or for volcanic areas, where it appears capable of recognizing

precursors originated by some hot fluid that penetrates by diffusion into rock pores, from

those associated with eventual plutonic magma intrusions, (iv) and also for monitoring peri-

ods of time during which a volcano is «inflated» by underground hot fluids compared to oth-

ers during which it «deflates». The AE results are a strictly local phenomenon detected only

whenever the transducer is put right on top of a suitable solid body. It results from the strain

of some very local solid feature, although being the consequence of some large-scale stress.

Seismic data are complementary, suitable for correlation with AE. They reflect a later stage of

the evolution of the system. Observations seem consistent with the hypothesis that phenome-

na always start by high frequency (HF) AE (~160-200 kHz) (originated when some very

small pores yield), followed by low frequency (LF) AE (~25 kHz) (when pores yield and coa-

lesce into larger microcavities), and finally at some later time by an earthquake (~1 Hz) when

the large scale mechanical structure of the system yields [19].

2. Acoustic Emission System

The Peteroa AE system was specially designed and constructed in Italy for micro-seismic

applications. In figure 5, an outline of the system is shown. The equipment includes the con-

nections to the video camera and satellite transmissions.

Figure 5. Outline of the two channels AE system installed on Peteroa volcano.

Two AE sensors are used, an LF resonant sensor (f= 25 kHz), and an HF resonant sensor

(f=150 kHz). Each sensor is coupled to a glass bar inserted and fixed by cement into a rock-

drill hole about 50 cm deep. The signal is driven to a preamplifier and then to an amplifier

and signal conditioner, which calculates the AE RMS values (Root Mean Square) from each

sensor. The system records one AE value every 30 sec. Every recorded value is the average of

all 5 msec records, which are measured during a time interval of 30 sec. By satellite commu-

nications the information is received in Buenos Aires city on demand. Every AE transducer

gives a signal in terms of an electrical potential. Every day the system records 2880 AE val-

ues in each channel. Figure 6 shows an AE sensor installed on Peteroa headland.

Figure 6. AE sensor installed on Peteroa outcrop.

3. Acoustic Emission Data

During the period December 17-2004 through March 9-2005, the first AE data was studied [3,

17]. During the next four years the system was out of order by technical problems and high

costs and the site was redesigned. During 2008 a prefabricated laboratory and a dwelling with

4 beds, a kitchenette and a bathroom, were transported, installed and equipped for all the re-

searchers who need it. All the systems were reassigned. Just now Peteroa volcano has a Multi

Parametric Station. Two years later, the AE system was replaced by a new one with some

small modifications. It was impossible to achieve the operability of the complete system dur-

ing the summer expeditions up to summer campaigns during 2009.

The cost of the satellite communications prevented the continuous transmission of the data, so

the AE information was saved in-situ on the memory of the system up to the first 2011 sum-

mer campaign. Even so, the first analysis of AE data from April 18, 2009 through March 3,

2011 are presented and studied at the present work.

3.1. Data Analysis Methods

The first step to analyze the AE data is to see the evolution of the raw HF and LF AE RMS

values during all the considered period and studying their statistical behavior. The second

stage is to scan them deeply and to correlate, to recognize the physical phenomena known by

other disciplines nor related (at a first sight) with AE. Up to the previous case history ana-

lyzed the intensity of AE can be fitted with a Log-normal distribution function [18, 19, 21].

3.1.1. AE temporal Evolution

The AE HF and LF RMS values were graphed versus time. In the “x” axis the values are

shown in number of register (one every 30 sec, which can be related with date) and in the “y”

axis in Volts. Figures 7 through 12 show the HF and LF AE evolution during the years 2009

(from April 17th), 2010 and 2011 respectively. At the same graphs more important facts as

earthquakes with 4 or higher magnitude in Richter scale (from the lighter earthquake up to the

greater ones), and Peteroa volcano eruptions are indicated by arrows.

Figure 7. AE HF time evolution during 2009, from April 17 through December 31. The earthquakes

occurred at Mendoza province are indicated by blue arrows; the corresponding date is included.

Figure 8. AE LF time evolution 2009, from April 17 through December 31. The earthquakes occurred

at Mendoza province, are indicated by blue arrows, including the corresponding date.

Figure 9. AE HF time evolution during 2010. The earthquakes occurred at Mendoza province, are in

blue arrows; the one at Chile and the period of the Peteroa volcano eruption are indicated by red and

green arrows respectively, including the corresponding date.

3.1.2. Methods of Analysis

It makes no sense to treat an observational database when the data are not uniform. Hence, a

search for outliers must be carried out on every given raw AE data series. The rejection of

outliers has been carried out by a suitable filter, upon selecting AE records that significantly

deviate compared to a Gaussian distribution of the AE records collected during a pre-chosen

and given time lag around every given instant of time.

Figure 10. AE LF time evolution during 2010. The earthquakes occurred at Mendoza province, are in

blue arrows; the one at Chile and the period of the Peteroa volcano eruption are indicated by red and

green arrows respectively, including the corresponding date.

Figure 11. AE HF time evolution during 2011, from January 1 through March 23. The earthquakes

occurred at Mendoza province and Japan are indicated by blue and violet arrows. Over the arrows the

magnitude and the date are indicated.

It was clearly realized, however, that even by avoiding to use much restrictive filters, a signif-

icant large number of outliers always persisted. Physically, this is a consequence of an intrin-

sic asymmetry of the AE signal. Indeed, the AE signal is intrinsically characterized by a

lognormal distribution, which displays an asymmetric tail. The physical reason is that the

probability of occurrence of a rupture of a crystalline bond is proportional to the number of

similar bonds that are being broken at that given instant of time. For instance, this is the typi-

cal situation e.g. of a public service (the probability that one person gets advantage of that

service is proportional to the number of users who already use it at that given instant of time).

The lognormal distribution is just one case history of the so-called Kapteyn class distributions

[19].

The outlier data series was thus selected, and the remaining data series (with no outliers) was

used for evaluating the moving running mean as specified in the following. The outlier data

series was analyzed independently.

Figure 12. AE LF time evolution during 2011, from January 1 through March 23. The earthquakes

occurred at Mendoza province and Japan are indicated by blue and violet arrows. Over the arrows the

magnitude and the date are indicated.

The timing of AE events, neglecting their intensity, may be studied using fractal analysis re-

lated with SOC (Self Organized Criticality) phenomena [13]. Consider the raw AE data series

(of a given frequency). Reject the outliers. Compute a weighted running mean on the data

series where the outliers have been rejected (e.g. use a triangular weight function). Subtract

from the original raw data series (including the outliers) the weighted running mean, and thus

compute the residual. Evaluate the RMS σ of all values of the residual data series. Choose an

arbitrary threshold that, upon a trial-and-check procedure, was found to be optimum for a val-

ue 0.4 σ. Define a point-like process, which is identified with the time instants of all relative

maxima of the residual data series, when the peak value of a relative maximum is >0.4 σ. This

is the point-like process, by which the fractal dimension D is computed [21]. The simplest

Box Counting Method can be used to study it. Consider the AE phenomena by the occurrence

of AE values over the analysis threshold level as “1” and as “0” for lower levels. Select a

small sampling interval μ, moving it from the beginning through the end of the time series,

taking into account not overlapping them, and sum the quantity of “1” found in all of them.

Enlarge μ and repeat the counting process and so on. Then, graph the logarithm of the number

of counts, N, inside the sampling interval, μ, versus the logarithm of the sampling interval μ,

that is to say, Log (N) versus Log (μ), which is the Richardson Plot. The slope can be calcu-

lated by least-square fitting on the plotted points. Owing to its definition, the result shall dis-

play a decreasing trend. For some limited interval μ1 ≤ μ ≤ μ2 such trend generally results

linear. The slope of this line is the negative value of the fractal dimension (D). The fractal

behavior is missed whenever μ1 is related with the minimum time interval that can be moni-

tored by the sensor. On the other side, μ2 is related with the insufficient extension of the data-

base. It is worth noting that here only the fractal dimension of temporal AE signals is consid-

ered. Up to now, considering the AE cases analyzed, when the D values goes to 0, some orga-

nized evolution of the system is likely to be in progress, the three dimensional distribution of

AE sources tends to become a bi-dimensional one, so a plane of rupture is organizing [18, 19,

21].

The next step is the analysis of the outlier data series, which is very useful for the investiga-

tion of the intrinsic periodicities of the system. The Automatic Research of Periodicities

(ARP) method is then used to find different frequency components. The computation of ARP

is carried out automatically in steps Δt in order to get a significant ARP histogram. The ARP

computation proceeds by a formal “loop” inside the program. The periodical structure of ARP

is repeated always identically. Thus, it was possible to relate them with the earth tidal fre-

quencies. Up to now this analysis was realized over the data of year 2005, and 7 different pe-

riods (around 23 h) of earth tidal spectral lines were found; 4 of them coincide with frequen-

cies formerly known [17, 18]. New sophisticated software to analyze the last data is being

developed and the results are not showed here. Figure 13 shows for HF AE the number of

“outliers” versus time for a selected period of near 80 days of the three years analyzed. The

partial time intervals (Δt) are of approximate 51 sec. Figure 14 shows a blow up of the origin

of the plot of figure 13. Figures 15 and 16 are the equivalent for LF AE with partial Δt of near

97 sec. The elementary interval on abscissas, chosen for the construction of the ARP histo-

gram, is automatically determined by the software. It is defined as 1/10 of the average time

lag between any two subsequent outliers of the time series, which is analyzed.

Figure 13. AE HF “ARP” for Peteroa based on data recorded in 2009-2011. See text.

Figure 14. Blow up of figure 13, aimed to show the fine structure of the periodicities that affect

Peteroa.

Figure 15. AE LF “ARP” for Peteroa based on data recorded in 2009-2011.

Figure 16. Blow up of figure 15, aimed to show the fine structure of the periodicities that affect

Peteroa.

4. Discussion

Planchón-Peteroa volcanic complex is characterized by various craters, with a practically

permanent activity displayed by large continuous fumaroles, being encircled by large glacier

walls. All such features denote that Peteroa is supplied by a steady source of endogenous heat

flow. It displays a variety of different manifestations of natural phenomena, so AE behavior

appears different from other seismically active areas and inclusive from other volcanoes.

Considering the HF and LF AE, during 2009 (figures 7 and 8), the original raw AE records

were higher during or prior to 4 of the 5 small earthquakes occurred at Mendoza province

showing higher LF values. The study threshold was selected as 0.01 V. It must be considered

that the high AE values began on April 17 and no AE information was record before.

In figure 9 (2010, HF), the AE was relatively low during the 10 earthquakes at Mendoza prov-

ince, but the very higher values shown at the end of the year stayed, with some variations dur-

ing all 2011. The scale used to show the last higher AE values do not show clearly the small

previous variations. The big Chile earthquake (magnitude = 8.8), occurred on February 27,

2010, did not show great AE increments nor the Peteroa eruption (September 4 through Octo-

ber 18). The LF AE in figure 10 shows higher values during the first four months of 2010 (8

earthquakes), and is over the study threshold almost all the time, inclusive during Peteroa

eruptions period.

During year 2011, the HF AE remained high all the year, prior to and during a small Mendoza

earthquake (magnitude 4.7), and the high Japan earthquakes (magnitudes 7.2 and 9.0 respec-

tively). The LF during 2011 (figure 12) shows relatively low values and much higher ones,

some days previous to Japan earthquakes. All the previous comments are only observational

facts with no clear explanations yet.

As it was already clearly shown in the aforementioned preliminary analysis of Peteroa AE

records, Peteroa responds to Earth’s tides with a precision comparable to highly reliable stop-

watch [17, 18]. It was also found that Peteroa responds to tidal periods close to ~ 24 hours,

not to ~ 12 hours, thus envisaging that the weight of the volcanic edifice is lifted by the direct

tidal attraction by the Moon, while it is not sufficiently uplifted by the centrifugal force when

the volcano is located in the anti-lunar direction. These ARP plots of figures 13 through 16

reveal, however, a definitely much complicated spectral composition of the tidal phenome-

non. These plots require therefore to be analyzed by a specific interactive and semi-automatic

software (in preparation) aimed to single out the competing roles of the several different spec-

tral components.

5. Conclusions

The Peteroa AE station has now been converted into a multi-parametric natural Laboratory

with some accommodations for researchers.

A first preliminary analysis is here reported, which was carried out on AE records collected

during a long period, form April 17th 2009 through March 23rd 2011, i.e. almost three com-

plete years of records. The database dealt with HF AE and LF AE measured every 30 sec

(every record is the average of the RMS AE signal computed every 5 msec).

First deductions from the AE analysis carried out during 2004-2005 (December 17th 2004

through March 9th 2005), related with the Earth tidal frequency components, are very likely

to be repeated in this new and comparably longer data series, and with greater detail due to

the longer available observations. New software suited to process this new and much wealthi-

er information is in development.

Acknowledgements

To National Agency for Scientific and Technological Promotion, Argentina, ANPCyT, PICT

2007-001769: “Emisión Acústica y Precursors Símicos”.

To Municipality of Malargüe City, Mendoza, Argentina.

To National Direction of Civil Protection, Argentina (DNPC), Argentina.

To Argentine Squadron 29 of National Gendarmerie: “Grupo Los Azufres”.

To de Embassy of Italy in Argentina.

To the Istituto di Acustica y Sensoristica O. M. Corbino, Roma, Italy.

To Dr. Miguel Haller, for his rich discussions.

References

1. M. Haller, C. Risso, A. Ramires, “Volcán Peteroa: Geología, Actividad Eruptiva 2010-

2100 y Vulnerabilidad de la Población”, Cuadernos ICES 4, ISBN 978-987-1323-25-8,

October 2011.

2. M. J. Haller, C. Risso, “La Erupción del Volcán Peteroa (35º15’S, 70º18’O) del 4 de Sep-

tiembre de 2010”, Revista de la Asociación Geológica Argentina, 68 (2), pp. 295–305,

2011.

3. J. Ruzzante, G. Paparo, R. Piotrkowski, M. Armeite, G. Gregori, M.I. López Pumarega,

“Proyecto Peteroa, primera estación de emisión acústica en un volcán de los Andes”, Re-

vista Española de Física, Vol. 1 (19), No. 1, pág. 12-18, www.ucm.es/info/rsef/revis-

taibfisica/peteroa.pdf, 2005.

4. C. Hucailuk, M. Armeite, D. Filipussi, M. I. López Pumarega, J. E. Ruzzante, M. A. Sabio

Montero, B. Veca, “Relación Entre Sismos y Emisión Acústica en Cerro Blanco, Argenti-

na” Malargüe, Mendoza, Argentina, accepted to be published at “Actas 7o Encuentro del

International Centre for Earth Sciences, E-ICES 7”, October 31-November 3, 2011.

5. C. Hucailuk, M. Armeite, D. Filipussi, M. I. López Pumarega, J. E. Ruzzante, M. A. Sabio

Montero, B. Veca, “Avances en el Estudio de la Emisión Acústica en el Cerro Blanco”,

“7to Encuentro del Grupo Latinoamericano de Emisión Acústica, E-GLEA 7”, E-Book,

ISBN 978-950-42-0137-3, 1ª. Ed., Mendoza, Argentina, August 25-27, 2011.

6. M. A. Sabio Montero, S. Isaacson, M. I. López Pumarega, M. Armeite, G. Paparo, G.

Gregori, J. E. Ruzzante, M. P. Gómez, “Second Acoustic Emission Station at the Andes

Mountains, San Juan, Argentina”, “EGU Topical Conference Series, 4th Alexander von

Humboldt International Conference, The Andes: Challenge for Geosciences”, Santiago,

Chile, 24–28 November 2008.

7. S. I. Isaacson, M. A. Sabio Montero, M. I. López Pumarega, M. Armeite, G. Paparo, J. E.

Ruzzante, M. P. Gómez, “Análisis de Autosimilaridad de los Tiempos de Arribo de la

Emisión Acústica en el Cerro Blanco, San Juan, Argentina”, Actas ”XXIV Reunión Cien-

tífica de la Asociación Argentina de Geofísicos y Geodestas AAGG, Primer Taller de

Trabajo de Estaciones Continuas GNSS de América y el Caribe”, ISBN: 978-987-25291-

1-6, pág. 241-248, Mendoza, Argentina, April 14-17, 2009.

8. M. E. Tornello, C. D. Frau, A. R. Gallucci, J. Ruzzante, M. I. López Pumarega, “Estación

de Emisión Acústica para el Monitoreo Sísmico en la Localidad de Cacheuta, Mendoza”,

“Acta de Resúmenes, 7o Encuentro del International Centre for Earth Sciences, E-ICES

7”, Malargüe, Mendoza, Argentina, ISBN: 978-987-1323-24-1, October 31-November 3,

2011.

9. C. Hucailuk, M. Armeite, D. Filipussi, M. I. López Pumarega, J. E. Ruzzante, B. Veca, M.

A. Sabio Montero, “Acoustic Emission in Cerro Blanco, Argentina”, Abstract accepted to

be presented at “30th European Conference on Acoustic Emission Testing / 7th Interna-

tional Conference on Acoustic Emission”, Granada, España, 12-15 de Septiembre, 2012.

10. D. Trombotto Liaudat, P. Penas, J. H. Blöthe, J. Hernández, “Monitoreo termo-

geomorfológico de la cumbre del Complejo Volcánico Peteroa, Mendoza, Argentina”,

“Cuadernos ICES 5, International Centre for Earth Sciences”, to be published, 2012.

11. Edited by Alberto Carpinteri & Giuseppe Lacidogna, “Earthquakes and Acoustic Emis-

sion”, Taylor & Francis e-Library, ISBN 978-0-415-44402-6, Italy, 2007.

12. H. Reginald Hardy, “Acoustic Emission / Microseiemic Activity”, Vol 1, Principles,

Techniques and Geotechnical Applications”, A. A. Balkema Publishers / Lisse / Abingdon

/ Extono (PA)/Tokyo, 2003.

13. P. Diodati, Per Bak, F. Marchesoni, “Acoustic emission at the Stromboli volcano: scal-

ing laws and seismic activity”, Earth and Planetary Science Letters, 182 , 253-258, 2000.

14. V. A. Gordienko, T. V. Gordienko, N. V. Krasnopistsev, A. V. Kuptsov, I. A. Larionov.

Yu. V. Marapulets, A. N. Rutenko, B. M. Shevtsov, “Anomaly in High-Frequency Geo-

acoustic Emission as a Close Earthquake Precursor”, Acoustical Physics, Vol. 54, No. 1,

pp. 82-93, 2008.

15. J. Ruzzante, M. I. López Pumarega, “Algunas Aplicaciones de las Ondas Elásticas en

Geofísica”, Sexto Encuentro del Grupo Latinoamericano de Emisión Acústica, E-GLEA

6, Porto Alegre, Brasil, 16 de septiembre de 2009.

16. M. Armeite, D. Filipussi, M. P. Gómez, S. Isaacson, M. I. López Pumarega, N. Núñez,

R. Piotrkowski, J. E. Ruzzante, D. Torres, “Emisión Acústica: un proceso vigente a distin-

tas escalas y con muchas aplicaciones”, Revista Abendi, Año VI, No 35, pág. 58-59, Bra-

sil, Diciembre 2009.

17. J. Ruzzante, M. I. López Pumarega, Maria Armeite, R. Piotrkowski, G. P. Gregori, I.

Marson, G. Paparo, M. Poscolieri , A. C. Catellani, “Análisis sobre el Comportamiento

del Volcán Peteroa (Argentina), por Métodos de Emisión Acústica (EA)”, “Actas XXIV

Reunión Científica de la Asociación Argentina de Geofísicos y Geodestas AAGG, Primer

Taller de Trabajo de Estaciones Continuas GNSS de América y el Caribe”, ISBN: 978-

987-25291-1-6, pág. 221-227, Mendoza, Argentina, April 14-17, 2009.

18. J. E. Ruzzante, M.I. López Pumarega, Editors, “Acoustic Emission, Vol. 1, Microseis-

mic, Learning how to listen to the Earth...”, ISBN 978-987-05-4116-5, CNEA, March,

2008.

19. G. P. Gregori, G. Paparo, U. Coppa, I. Marson, “Acoustic Emission (AE) in Geop-

hysics”, Actas E-GLEA 2, Segundo Encuentro del Grupo Latinoamericano de Emisión

Acústica, pp. 57-78, 2001.

20. P. Diodati, Per Bak, F Marchesoni, “Acoustic Emission at the Stromboli volcano: scaling

laws and seismic activity”, Earth and Planetary Science Letters, 182, pp. 253-258, 2000.

21. G. P. Gregori, “Natural Catastrophes and Point-like Process. Data Handling and Previ-

sion”, Annali di Geofisica, Vol. 41, (5/6), pp. 767-786, 1998.