Tratamiento estadístico de la deuda y flujos derivados de operaciones de rescate financiero (continuación)

There have been numerous studies that have utilised seismic recordings to recognise, locate, and/or monitor mass flows (e.g., Tungol and Regalado (1996);Arattano (1999);

Jolly et al. (2002); Caplan-Auerbach et al. (2004)). The aims of these studies, with regards to their seismic component, varies. Often, they have merely been used to either inform of the passage of a flow and send alert warnings (e.g., Bessason et al.(2007)), or as a trigger to increase the sampling record of other geophysical instrumentation (e.g.,

Lavigne et al. (2000b)). There have, however, been a number of studies where seismic monitoring has been used to derive information about a flow itself (e.g., Surinach et al.

(2005)).

2.4.3.1 Warning systems

Monitoring and warning of mass flows using seismic studies include work developed in the European Alps (e.g., Leprettre et al. (1996)), Norway, and Iceland (e.g., Bessason

et al. (2007)). The system provided in Norway to alert traffic on roads is based upon the use of geophones, located above the roads in the paths of avalanches. Minimum thresholds were given, above which avalanches would produce signals, and the traffic alerted. The system did not always work well; the threshold level had to be set high enough to avoid false triggers but low enough to not miss events. Despite the appli- cation of these threshold levels, false alarms were triggered and events were missed (Bessason et al. (2007)).

In 1992, Leprettre et al.(1996) investigated a method of real-time seismic detection of avalanches in the Alps. Approximately 300 events were recorded over a 3-year period, with additional information providing confirmation of avalanche trigger mechanism for c. 15% of the events. The rest were triggered by a combination of earthquakes, animals, storms, and traffic. The study highlighted the need for more than just instrumentation at a site; Leprettre et al. (1996) introduced new methods for automatic recognition of an avalanche based upon signal analysis.

More recently, Bessason et al. (2007) have developed a more complex monitoring system that automatically detects and analyses flows in the West Fjords Peninsula, Iceland. This system, initially installed in 1996 (Bessason et al.(1999, 2000)), is based on comparison of known events to detect and identify new events. Ten characteris- tic parameters of the signals are evaluated: peak value, power, power duration, total duration, impact factor, characteristic frequencies, half-power bandwidth, lower spec- tral limit, minimum and maximum amplitudes, and the frequency of the maximum in the power density spectrum. The results of these analyses are not as satisfactory as could be hoped; many events are incorrectly classified, although it is suggested that the process will become more reliable as more events are detected (Bessason et al.(2007)). Monitoring on volcanoes is generally less specific to channels than the above avalanche monitoring. Primarily, the systems are used to monitor the changing nature of the vol- cano itself; lahar or other mass flow monitoring is both incidental and additional to the other recordings made. Jolly et al. (2002) took advantage of the 8-station network of seismometers deployed by the Montserrat Volcano Observatory to monitor the growing dome and subsequent collapses. The study uses a best-fit model to find the location of a pyroclastic flow as source, similarly to earthquake location methods (e.g., Lahr

(e.g., at Piton de la Fournaise; Aki and Ferrazzini (2000)) and pyroclastic flows (e.g., at Unzen Volcano, Japan; Yamasoto (1997)).

Warning systems that are more specifically designed to monitor the passage of lahars and debris flows are also found. Lavigne et al. (2000b) details the system in place for the monitoring of channels at Mt. Merapi, Indonesia, where alerts are sent to the Volcanological Survey of Indonesia. The systems emplaced to monitor the flows of Mt. Pinatubo in the Philippines also alerts to passing flows once minimum amplitude thresholds are exceeded (e.g., Tungol and Regalado (1996)). A similar system has also been established at Mt. Ruapehu, New Zealand. This latter system, the Eastern Ruapehu Lahar Warning System (ERLAWS), comprises geophones at three stations along the Whangaehu channel. It was installed to specifically monitor the threat from crater lake collapse, but remains installed as a result of its effectiveness during the March 2007 lahar (Leonard et al.(2008)). In combination with the Eruption Detection System (EDS) also installed at Ruapehu, early warnings for both eruption-triggered and other lahars can be provided (Keys (2007)).

2.4.3.2 Velocity of flow

Seismic data have, in the past, been used to estimate the frontal velocity of mass flows. This requires the use of a series of sensors deployed along a channel at known distances.

Suwa et al. (2000) showed that, while flow-induced ground vibrations did occur prior to the arrival at a site, the peaks coincided with the peaks in the hydrograph records. As such, it is a simple matter to pick the arrival of a flow at a site using seismic records. Correlation between sites will give an average frontal velocity. Many studies have utilised this method (e.g., Galgaro et al. (2005); Lavigne et al. (2000b); Tungol and Regalado (1996)). In some cases, however, there is not always an unique, well- defined flow front that easily heralds the arrival of a flow. Arattano and Marchi (2005) present a cross-correlation method that can be employed in such cases between data to define the mean velocity of the flow.

2.4.3.3 Flow discharge and size

A number of studies have attempted to use recordings of the seismic amplitude of la- hars and other mass flows to estimate their size and discharge. The maximum signal

amplitude recorded at Unzen Volcano, Japan, during pyroclastic flows shows a good correlation with the volume of rocks within the flow (Uhira et al. (1994)). This rela- tionship, however, is not a simple one (Calder et al. (2002)). The best-fit of flow size and discharge will vary considerably with sensor distance from the channel and type of flow (Tungol and Regalado (1996)). It is, however, not surprising that the longer the seismic record of an event, the larger the volume of material within a flow (Zobin et al.

(2005)).

Suwa et al. (2000) proposed that the peak vibrational energy recorded by a seis- mometer is proportional to the peak discharge of a flow, while volume estimations could be made by integrating the acceleration amplitude. Galgaro et al. (2005) calcu- lated the discharge of a flow in the Acquabona catchment, Italy, using mean frontal velocity and flow section area. They also used a regression line to estimate the total volume of the flow from the integral of ground vibrations. AFM data was used at Mt. Pinatubo to define a pseudohydrograph, the strength of which was used as a proxy for relative discharge. Absolute discharge could then be calculated by calibration with actual discharge measurements at the site (Tungol and Regalado (1996);Marcial et al.

(1996)).

2.4.3.4 Signal characteristics

As illustrated above, previous studies have been made to investigate the ground vi- brations generated by different types of flows. For example, the dynamics of the more than 50 lahars on Mt Merapi that followed the nuees ardentes of 22nd _{November 1994}

were monitored using acoustic flow monitoring (AFM) systems, real-time seismic am- plitude measurements (RSAM), and seismic spectral amplitude measurements (SSAM) (Lavigne et al. (2000b)). The lahar durations on average ranged from between 30 and 90 minutes, the majority of which occurred in the afternoon. Instrumental thresholds were proposed following observations of mean velocities and peak discharges in order to improve the warning systems. Large lahars were identified by>1500 mV AFM value

on the low-gain, broad-band setting, >400 RSAM units, or >80 SSAM units on the highest frequency band (Lavigne et al. (2000b)).

Studies have also been made with regards to the individual frequencies excited by a flow. Chen et al. (1991) observed the passage of 18 debris flows at Jiangjia Gully

in China; the frequencies generated by the flows are mainly between 25-85 Hz, with a peak frequency of c. 50 Hz. Others have concluded energy from hyperconcentrated and debris flows are concentrated in the 10-100 Hz range (Marcial et al. (1996); Lavigne et al. (2000b)).

Experimental flows made byHuang et al.(2003, 2004) investigated the sources of the ground vibrations generated by debris flows. The signals recorded are due to both the collisions of large boulders and particles within the flow and the frictional interaction of the flow with bedload. Huang et al. (2003, 2004) measured bed-friction generated signals with frequencies in the range 10-300 Hz, although predominantly between 20 Hz and 80 Hz. The frequencies of collisional motion produced signals between 10-500 Hz. These studies were followed by Huang et al. (2007), in which ground vibrations were measured during field experiments in Taiwan. These showed that individual rocks produced frequencies of 10-150 Hz, with larger rocks generating signals at the lower end of the spectrum. The observation was also made that the signals of a debris flow front produces ground vibrations of lower frequencies (<50 Hz) than those of the body and tail of the flow (50-100 Hz). The reason for this is ascribed to the accumulation of larger stones [sic] at the front of the flow (Huang et al. (2007)).

The above studies were made with sensors installed next to the channels and/or the flumes used in the experiments. Distinguishing different frequencies made by a flow using the records of sensors deployed for volcano monitoring is much harder. This is because the higher amplitudes will not be observed due to the anelastic attenuation of higher frequency propagating waves with distance (Aki (1980)). Surinach et al.(2005) illustrate that snow avalanche signals can be resolved as different from earthquakes and explosions due to the increase in the high frequency component as a flow approaches the sensor. Similar behaviour could be expected with signals from lahars. For sensors installed away from channels, however, the attenuation of higher frequencies will make this much harder to see. Zobin et al. (2009), for example, show peak frequencies for lahars recorded at Volc´an de Colima, Mexico, as in the range of 6-8 Hz. The sensors, however, are at least 2 km from the source of the ground vibrations and this will have a distinct effect on the maximum frequencies recorded. Comparisons made between the records of lahars and pyroclastic flows, however, made by the same instrument indicate that, despite this, the two flow types can be resolved as the latter produce

peak frequencies of 3-4 Hz (Zobin et al. (2009)).