Snow avalanches generate seismic signals as many other mass movements.
Detection of avalanches by seismic monitoring is highly relevant to assess
avalanche danger. In contrast to other seismic events, signals generated by
avalanches do not have a characteristic first arrival nor is it possible to
detect different wave phases. In addition, the moving source character of
avalanches increases the intricacy of the signals. Although it is possible to
visually detect seismic signals produced by avalanches, reliable automatic
detection methods for all types of avalanches do not exist yet. We therefore
evaluate whether hidden Markov models (HMMs) are suitable for the automatic
detection of avalanches in continuous seismic data. We analyzed data recorded
during the winter season 2010 by a seismic array deployed in an avalanche
starting zone above Davos, Switzerland. We re-evaluated a reference catalogue
containing 385 events by grouping the events in seven probability classes.
Since most of the data consist of noise, we first applied a simple amplitude
threshold to reduce the amount of data. As first classification results were
unsatisfying, we analyzed the temporal behavior of the seismic signals for
the whole data set and found that there is a high variability in the seismic
signals. We therefore applied further post-processing steps to reduce the
number of false alarms by defining a minimal duration for the detected event,
implementing a voting-based approach and analyzing the coherence of the
detected events. We obtained the best classification results for events
detected by at least five sensors and with a minimal duration of
12s. These processing steps allowed identifying two periods of
high avalanche activity, suggesting that HMMs are suitable for the automatic
detection of avalanches in seismic data. However, our results also showed that
more sensitive sensors and more appropriate sensor locations are needed to
improve the signal-to-noise ratio of the signals and therefore the
classification.
Introduction
During the winter season, snow avalanches may threaten people and
infrastructure in mountainous regions throughout the world. Avalanche
forecasting services therefore regularly issue avalanche bulletins to inform
the public about the avalanche conditions. Such an avalanche forecast
requires meteorological data, information about the snowpack and avalanche
activity data. The latter are mostly obtained through visual observations
requiring good visibility. Avalanche activity data are therefore often
lacking during periods of intense snowfall, which are typically the periods
when they are most important for forecasting. A possible alternative approach
to determine the avalanche activity is to use a seismic monitoring system
e.g.,.
Seismic monitoring systems are well suited to detect mass movements such as
rockfalls, pyroclastic flows and snow and ice avalanches
. The ability to detect snow avalanches through seismic
methods was first demonstrated in the 1970s. and
deployed geophones near avalanche paths and manually
identified signals generated by avalanches in the seismogram. They showed
that the seismic signature of avalanches differs from other seismic events
such as earthquakes or nearby blasts. A more in-depth analysis of seismic
signals generated by avalanches was performed 20 years later, identifying
typical characteristics in both the time and time–frequency domain
. Using automatic cameras to film
avalanches, showed that specific features in the seismic
signals were related directly to changes in the flow of the avalanche; these
findings were confirmed by and .
Since then, seismic signal characteristics were used to estimate specific
properties of single avalanches such as the flow velocity
, the total energy of the avalanche
or the runout distance .
While many studies focused on using seismic signals to better understand the
properties of single avalanches, continuous monitoring of avalanche starting
zones to obtain more accurate avalanche activity data is of particular
interest for avalanche forecasting e.g.,.
deployed three-component seismic sensors at two
different field sites and compared seismic signal characteristics to a
database including avalanches, helicopters, thunder rolls and earthquakes.
improved the seismic monitoring system used by
by deploying a seismic array between two known
avalanche paths. The array consisted of six vertical component geophones
arranged in a circle around a three component geophone in the center. Using
array techniques, determined the release area and the
path of manually identified avalanches and estimated their speed. These
studies mainly monitored medium and large avalanches.
Van Herwijnen and Schweizer (2011) however, deployed seismic sensors near an
avalanche starting zone above Davos in the eastern Swiss Alps to also detect
small avalanches. They manually identified several hundred avalanche events
in the continuous seismic data during 4 winter months.
While these studies have highlighted the usefulness of seismic monitoring to
obtain more accurate and complete avalanche activity data, using machine
learning algorithms to automatically detect snow avalanches has thus far
remained relatively unsuccessful. Nevertheless, the interest in these
techniques has been evident for several decades . The first attempt to automatically detect
avalanches focused on using fuzzy logic rules and credibility factors derived
from features of the seismic signal in the time and time–frequency domain
. In a first step, the features of
unambiguously identified seismic events were analyzed including avalanches,
blast and teleseismic events. They then formulated several fuzzy logic rules
for each type of event to train a classifier used to identify the type of a
new unknown seismic event. While the probability of detection (POD), i.e., the
number of detected avalanches divided by the total number of observed
avalanches, was high (≈90%), one of the main drawbacks of this
method is the subjective expert knowledge used to derive the fuzzy logic
rules and the need to adapt these rules to each individual field site.
deployed seismic sensors in several known avalanche
paths along an exposed road in Iceland. They used a nearest-neighbor method
to automatically identify avalanche events. The method consists of comparing
new events with those in a database. Although a 10-year database was used,
the identification performed rather poorly. Seismic signals generated by
rockfalls and debris flows were wrongly classified as avalanches and vice
versa, resulting in a POD of about 65 %.
In an attempt to improve the automatic detection of avalanches,
used a seismic avalanche catalogue presented by
and compared the performance of 12 different
machine learning algorithms. The PODs of all classifiers were high (between
84 and 93 %). However, the main drawback were the high false alarm rates,
much too high for operational tasks.
The methods described above are generally difficult to apply at new sites
since they require time to build a training data set and/or expert knowledge
to define thresholds and rules. To overcome these drawbacks, we investigate
using a hidden Markov model (HMM). A HMM is a statistical pattern recognition
tool commonly used for speech recognition and was first
introduced for the classification of seismic traces by
. The advantage of HMMs compared to other
classification algorithms is that the time dependency of the data is
explicitly taken into account. First studies using HMMs for the
classification of seismic data relied on large training data sets
. Using this approach, created
an earthquake detector.
More recently, developed a new approach which only
requires one training event. This approach was applied for a volcano fast
response system and the detection of rockfalls,
earthquakes and quarry blasts on seismic broadband stations of the Swiss Seismological Service (SED; . Furthermore,
also detected snow avalanches using data from a seismic
broadband station of the SED. During a period with
high avalanche activity in February 1999, 43 very large confirmed avalanches
were detected over a 5-day period with only four presumable false alarms. While
these detection rates are very encouraging, the investigated avalanche period
was exceptional. Furthermore, due to the location of the broadband station at
valley bottom, they could not detect small or medium-sized avalanches.
For avalanche forecasting information on smaller avalanches is also required.
To resolve this issue, we investigate a method to obtain local (i.e., scale of
a small valley) avalanche activity data using a seismic monitoring system
. We therefore implement the approach outlined by
to detect avalanches in the continuous seismic data
obtained with the sensors deployed near avalanche starting zones
.
Based on the duration of the seismic signals, the majority of these
avalanches were likely rather small . We therefore
used this avalanche catalogue to train and evaluate the performance of a HMM
model and discuss limitations and possible improvements.
Field site and instrumentation
We analyzed data obtained from a seismic array deployed above Davos,
Switzerland. The field site is located at 2500 m a.s.l. and is surrounded by
several avalanche starting zones. The site is easily accessible during the
entire year and is also equipped with various automatic weather stations and
automatic cameras observing the adjacent slopes.
The array consisted of seven vertical geophones with an eigenfrequency of 14 Hz. The maximum distance between the sensors was 12m. Six of the
geophones were inserted in a styrofoam housing and placed within the snow,
whereas the seventh geophone (Sensor 7) was inserted in the ground with a
spike (see Figs. 3 and 4 in ). Sensors 1 and 4 are
the ones nearest to the ridge and most deeply covered (see Fig. 2 in
). The seismic sensors were deployed at the field
site from early December until the snow had melted.
The instrumentation was originally designed to record higher frequency
signals in order to detect precursor signals of avalanche release
. A 24 bit data acquisition system (Seismic
Instruments) was used to continuously acquire data from the sensors at a
sampling rate of 500 Hz. The data were stored locally on a low power computer
and manually retrieved approximately every 10 days. A more detailed
description of the field site and the instrumentation can be found in
and .
Data
Continuous seismic data were recorded from 12 January to 30 April 2010.
These data were previously used by ,
and . The recorded seismic data
contain various types of events , including
aeroplanes, helicopters and of course avalanches (Fig. ).
(a) Spectrogram of an unfiltered 30 min time series. (b) Corresponding time series. An airplane
and
an avalanche are visible. Furthermore, noise produced by a snowcat can be seen in the second half of the time series.
Pre-processing of the seismic data
While during the 107-day period several hundred
avalanches were identified, the vast majority of the data consist of noise or
seismic events produced by other sources. To reduce the amount of data to
process, we applied a simple threshold-based event detection method. It
consisted of dividing the continuous seismic data stream in non-overlapping
windows of 1024 samples. For each window, a mean absolute amplitude Ai
was determined. When Ai≥5A‾, with A‾ the
daily mean amplitude, the data were used. Furthermore, a data section of
Δt=60s before and after each event was also included to
ensure that the onset and coda of each event were incorporated. The amount of
data to process was thus reduced by 80 % (Fig. ).
Example of the pre-processing: (a) the mean energy values of each window shown as the blue line and
the threshold value indicated by the red line; (b) the remaining data cut by the pre-processing step.
Finally, before we used the data for the detection, we applied a bandpass
filter. Previous studies showed that seismic signals generated by avalanches
typically have a frequency below 50 Hz e.g.,. We therefore applied a fourth-order Butterworth bandpass filter
to our data between 1 and 50 Hz.
Reference avalanche catalogue
Van Herwijnen and Schweizer (2011) visually analyzed the unprocessed seismic time
series and the corresponding spectrogram of one sensor and identified N=385 avalanches between 12 January and 30 April 2010. They thus obtained an
avalanche catalogue consisting of the release time ti and duration Ti
for each avalanche. The onset was defined as the first appearance of
energetic low frequency signals (i.e., between 15 and 25Hz),
while the end of the signal was defined as the time when low frequency
signals reverted back to background levels. However, only 25 of these
avalanches were confirmed by visual observations, i.e., on images obtained
from automatic cameras. Hence there remains substantial uncertainty about the
nature of the identified events.
To reduce the uncertainty, three of the authors therefore independently
re-evaluated this avalanche catalogue. From the 385 avalanches in the
original avalanche catalogue only Npre=283 remained after
pre-processing (see Sect. ); none of the 25
confirmed avalanches were dismissed. By visually inspecting the seismic time
series of the seven sensors and the stacked spectrogram for each event, they
then assigned a subjective probability pe to each of these possible
avalanche events. Three probabilities were assigned: 1 when it was certain
that the observed event was an avalanche, 0 when it was certain that the
observed event was not an avalanche and 0.5 when it was uncertain whether the
event was an avalanche or not. The probabilities were then combined into
seven probability classes depending on the mean probability of each event:
Pava=13∑e=13pe,
with pe the subjective probability that each assigned to a specific event.
In Table the number of events in each probability
class is listed. In the reclassified data set, only 20 avalanches were
considered as certain avalanche by all three evaluators and 58 events were
marked as certainly not an avalanche. Furthermore 18 of the 25 visually
confirmed avalanches were within the two highest probability classes.
Number of events per probability class Pava after re-evaluation and the corresponding number of
confirmed events for each class. The first row shows the possible combinations of subjective probability (pe): when pe=1 it
was certain that the event was an avalanche; when pe=0.5 it was uncertain; and when pe=0 it was certain that the event was not an avalanche.
The avalanche activity of the season 2010 is shown in Fig. .
Avalanche activity for the winter season of 2010. The different colors above the zero line
indicate the different probability classes derived from the manual detection. The red bars below the zero
line indicate the visually confirmed events.
Overall, most avalanches were detected in the second half of the investigated
period, with two distinct peaks in the avalanche activity around 22 March and
24 April.
The distribution of the event duration changed as the probability class
increased (Fig. ). All events in the lowest probability
class had a duration <6s. More than 90% of the events had
durations ≥12s for the three highest probability classes.
Event duration distribution per probability class.
MethodsHidden Markov model
To automatically detect avalanches in our continuous seismic data we used
HMMs. This statistical classifier models observations
(i.e., the seismic time series or its features) by a sequence of multivariate
Gaussian probability distributions. The characteristics of the distributions
(i.e., mean and covariance) are derived from training sets of known events, so-called pre-labeled training sets. Several classes describing different types
of events can be implemented in the classifier but each class needs its own
training set to determine its unique distribution characteristics. Therefore,
the actual classifier consists of several HMMs, one for each class. This
classical approach, as used for the classification of seismic time series by,
e.g., and , relies on well-known pre-labeled training sets. In our case, however, avalanches are rare
events and it is nearly impossible or too time consuming to obtain an
adequate training set. To circumvent the problem of obtaining sufficiently
large training sets, we used a new approach developed by .
This classification approach exploits the abundance of data containing mainly
background signals to obtain general wave-field properties. Using these
properties, a widespread background model can be learned from the general
properties. A new event class is then implemented by using the background
model to adjust the event model description by using only one training event.
The so-obtained classifier therefore consists of the background model and one
model for each implemented event class. The classification process itself
calculates the likelihood that an unknown data stream has been generated by a
specific class for each individual class HMM. More detailed information can
be found in .
Feature calculation
Although it is possible to use the raw seismic data as input for the hidden
Markov model, we used a compressed form of it, so-called features. Several
features can be calculated representing different aspects of the time series
such as spectral, temporal or polarization characteristics. The
representation of the seismic signals by features is more adequate to
highlight differences between diverse event types. Since we used single
component geophones, we only used spectral and temporal features. Based on
preliminary analysis, we used the following features:
central frequency
dominant frequency
instantaneous bandwidth
instantaneous frequency
cepstral coefficients
half-octave bands.
A detailed list of the functions used to calculate these features can be
found in .
To calculate the features, we used a sliding window with width, w of 1024
samples. The sliding window is then moved forward with a step of
0.05s or 25 samples, resulting in an overlap of 97%.
For the half-octave bands we used a central frequency of fc=1.3Hz for the first band and a total number of nine bands. Since the
geophones have an eigenfrequency of 14 Hz, only signals with a higher
frequency are recorded without any loss of information. However, preliminary
results showed that half-octave bands with a central frequency higher than
fmin=5Hz are adequate.
Post-processing
The HMM classification resulted in several hundred events in the avalanche
class. Many of these events were, however, of very short duration or only
identified at one sensor and did not necessarily coincide with avalanches in
the reference catalogue; i.e., these events were likely false alarms. We
therefore investigated three post-processing methods to reduce the number of
false alarms, namely
applying a duration threshold for the detected events;
analyzing the results of all sensors by introducing a voting-based
classification;
analyzing the coherence between all sensors for each detected event.
First, we used an event duration threshold. The duration Tj of any
automatically detected event j was determined by the HMM. Based on the
analysis of and , we can
assume that event duration correlates with avalanche size. The first
post-processing method therefore consisted of using a minimal duration
Tmin for the events as described below in
Sect. . Any automatically detected event j with Tj≤Tmin was thus removed. Similarly, any avalanche i in the
reference catalogue with a duration Ti≤Tmin was also
removed.
Second, we used a voting-based threshold by tallying the classified events of
each sensor. used a similar approach and found that with
increasing votes the false alarm rate decreased. The overall idea is that
although an avalanche event might not be recorded by one sensor, for instance
due to poor coupling of the sensor, it is unlikely that an avalanche is
missed by all sensors, especially larger avalanches .
Any automatically detected event with Vj≤Vmin with V
the number of votes was removed.
Third, we used a threshold based on the cross-correlation coefficient between
the seven sensors. Wave fields generated by avalanches should be relatively
coherent, while wave fields generated by noise (e.g., wind) are expected to be
incoherent. We therefore divided the seismic data in non-overlapping windows
of 1024 samples and for each window we defined a mean normalized correlation
coefficient as
R(twin)=1Npairs∑k=1Npairsrkl(twin),
with Npairs=21 the number of sensor pairs,
rkl(twin) the maximum in the normalized cross correlation
between sensor k and l and twin the time of the sliding window. The
normalized cross correlation is defined as
ϕ‾kl(t)=ϕkl(t)ϕkk(0)ϕll(0),
with ϕkk(0) and ϕll(0) the zero lag autocorrelation of each
sensor, which is equal to the energy of each single time window. The maximum
of the normalized correlation is picked for a maximum lag of
tmax=0.05s, which is the time a sonic wave field
at a speed of 330 m s-1 needs to travel the maximum distance
between the most distant receiver pair (≈15m):
rkl(twin)=maxϕ‾kl(t),|t|≤0.05s.
The normalized cross correlation only yields values between -1 (perfectly
anticorrelated) and 1 (perfectly correlated). A value of 0 means that the
signals are completely uncorrelated. Finally, the coherence of each
automatically detected event was defined as
Cj=maxR(twin),0≤twin≤Tj.
Any automatically detected event with Cj≤Cmin was
removed.
Model performance evaluation
To evaluate the performance of the HMM classification, we
compared the automatic picks with the reference data set described in Sect. . To assign an event classified by the HMM as a positive
detection we defined a tolerance interval d: the time tjHMM of an
event had to be within the interval ti-d≤tjHMM≤ti+d,
with ti the release time of the ith avalanche in the reference data set
and d=60s. The tolerance interval d was necessary, since the
release times ti of the avalanches were picked manually and may contain
some uncertainties. In addition, the releases times tjHMM do not
necessarily coincide with the reference data since the classifier is not an
onset picker.
To describe the performance of the classifier we used three values:
Nhit, Nunassigned and Nmiss. The
first value Nhit describes the total number of avalanches which
were correctly detected by the classifier, i.e., events identified by the
classifier which corresponded to avalanches in the reference data set. The
second value Nunassigned describes the number of events
identified by the classifier which did not correspond to an avalanche in the
reference data set. We do not call these events false alarms as during the
manual detection some avalanche events might have been missed that are
therefore not present in the reference data set. Avalanche events may still
be found in the unassigned detections. Finally, the third value
Nmiss describes the number of avalanches in the reference data
that were not identified by the HMM classifier. The three values were used to
evaluate the overall model performance in terms of POD and false alarm ratio (FAR), defined as POD =Nhit/(Nhit+Nmiss) and
FAR =Nunassigned/(Nhit+Nunassigned).
To determine the best threshold values for the post-processing steps (Sect. ), we plotted POD against FAR values for all
probability classes and for different values of Tmin,
vmin and Cmin. One curve therefore illustrates
the POD and FAR with respect to the probability classes for a fixed threshold
value. Ideally, when only considering the 100 % probability class, the POD
value should be 1 while the FAR value should also be relatively high since
all detections of the lower classes are counted as false alarms. By taking
more probability classes into account, the FAR will decrease while the POD
value should stay close to 1. A perfect model should therefore result in
constant POD values of 1 whereas the FAR values should decrease to 0. For
realistic models, however, POD and FAR values are expected to decrease when
accounting for more probability classes. By plotting POD against FAR values
for different threshold values, the optimal threshold value can be found by
searching for the largest area under the curve (AUC). A perfect model would
result in an AUC value of 1, while an AUC value of 0.5 corresponds to a model
with a 50–50 chance. Threshold values obtained for models with an AUC value
lower than 0.5 are therefore not reliable and any random threshold value can
be chosen. In our case, the POD–FAR curves did not span from 0 to 1 on the
x axis. To determine the AUC value we therefore calculated the ratio between
the area under the POD–FAR curve and the area under the bisector with the
same x-axis limits:
AUC=AUCHMMAUCbisector⋅0.5.
ResultsTemporal feature distribution
To investigate changes in feature distribution over time, for instance due
to diurnal changes in environmental noise levels or seasonal changes in snow
cover properties, we calculated hourly and daily mean values for all features
(see Fig. for the central frequency and dominant
frequency). Throughout the season there were large variations in the feature
distribution at various timescales. First, there were strong diurnal
variations (yellow lines in Fig. ). These were
observed in all features (not shown). Second, there were also large
variations at longer timescales (blue lines in Fig. ). While for some features there were significant
seasonal trends, for instance for the dominant frequency (Pearson r=-0.56,
p<0.001; Fig. b), for others there were no
clear trends, for instance for the central frequency (Pearson r=0.23,
p=0.02; Fig. a). Building a single background
model to classify the entire season would therefore likely not result in a
reliable classification. The background model thus has to be regularly
recalculated. We therefore decided to recalculate the background model each
day to classify the events within the same day.
(a) Temporal variations in the central frequency hourly average
(yellow) and daily average (blue). (b) Temporal variations in the dominant
frequency.
Training event
While building a representative background model is important, choosing an
appropriate training event is of utmost importance for the classifier. As
outlined in , the avalanche catalogue consists of
various avalanches of different size and type. At the beginning of the season
the avalanches are most likely dry-snow avalanches, while at the end of the
season there a mostly wet-snow avalanches. Our avalanche catalogue only
consists of the release time and little information is available on the type
of the avalanches. We therefore compared the feature distribution of four
different avalanche events, on 21 January, 27 February, 22 March and 24 April
2010, to investigate whether substantial differences related to avalanche type
existed (Fig. ).
(a) Central frequency with normalized time for four different avalanche events (colors) from 4 different
months. For comparison, the time was normalized by the event duration, with 0 indicating the start of the avalanche
and 1 the end of the event. (b) Second cepstral coefficient.
While there were some subtle differences in the feature distribution for the
four avalanches (e.g., between the avalanches on 21 January and 22 March
2010 in Fig. a), overall the four avalanches
exhibited very similar behavior. Thus, when viewing avalanches in the
feature space, wet- and dry-snow avalanches appear to be very similar. We
therefore used one single avalanche class for the HMM classifier and used one
training event to learn the model. Specifically, we used an avalanche with
Pava=100% recorded on 22 March 2010 (Fig. ) with a duration of 30s. However, as
seen in Fig. , the most rapid changes in feature
values occurred at the beginning of the events. In the coda changes in
feature values were rather slow, providing limited relevant information for
the classifier. We therefore only used the first 8 s of the training event
(marked by the red rectangle in Fig. ).
The event used for training the HMM. (a) Stacked spectrogram of all seven sensors. (b) Seismic waveform for
each individual sensor (colors). The red area highlights the part of the signal used as the training event for the HMM.
We used the entire reference data set containing 283 avalanche events to
evaluate model performance as function of probability class. The first model
was built for each individual sensor and without any post-processing. For
each sensor a separate model was built containing only the data of the
specific sensor. In Table the number of detections
for each probability class is listed. For the highest probability class the
POD values were relatively high, ranging from 70 to 95 % and the values
generally decreased with decreasing probability class. For the confirmed
avalanches, the POD ranged between 80 and 92 %.
Nevertheless, even for the lowest probability class events were still
detected. Furthermore, numerous events, between 124 and 2091 events, were
detected for each sensor which were not listed in the reference data set.
Clearly without any post-processing the number of unassigned events was high.
Number of detections (Nhit) and probability of detection (POD) for each sensor and each probability
class as well as the POD for confirmed events Evtconf and number of events that were not in the reference
data set (Nunassigned).
To reduce the number of unassigned events we applied a minimum duration
Tmin to remove events. To obtain a reasonable threshold value,
we determined the area under the POD–FAR curve for different
Tmin values (Fig. ).
(a) POD–FAR curves for sensor 1 for different minimum event durations Tmin (colors).
(b) Area under the POD–FAR curve with Tmin for all sensors (colors). The stars show the Tmin
value with the largest area under the curve for each sensor.
Due to the large number of unassigned events (see Table ) AUC values were generally below 0.5 and using a
minimum duration threshold did not result in much improvement. However, for
sensors 1 and 6 the number of unassigned events was much lower and there was
an optimum Tmin threshold value around 12s (Fig. b). In the following, we therefore used the same
minimal duration Tmin=12s for all sensors. While
with this Tmin threshold the POD values for the highest
probability class somewhat decreased, they still remained high (between 68
and 84 %; Table ). Furthermore, the POD for the confirmed avalanches also remained relatively high, ranging
from 60 to 84 %. Note that the duration threshold was also applied to
remove events from the reference data set. For a threshold value of
Tmin=12s, the number of events in the reference
data set reduced from 283 to 170 while the number of confirmed avalanches was
not affected.
Number of detections (Nhit) and probability of detection (POD) for each sensor and each probability
class as well as the POD for confirmed events Evtconf and number of events that were not in the reference
data set (Nunassigned) using a minimal event duration of Tmin=12s.
Overall the number of unassigned events substantially decreased (compare
Tables and ), especially
for sensors 1 and 6. For these two sensors the POD values also substantially
decreased for the lower probability classes but the two main avalanche
periods in March and April are clearly visible in the detections (see Fig. a for sensor 1). However, for the other sensors, the
POD values remained relatively high for the lower probability classes and
there were still many unassigned events and the two main avalanche periods
were less evident (see Fig. b for sensor 7).
Clearly, for some of the sensors the number of unassigned events remained
very high.
Classification results using a minimal event duration length of
12s for (a) sensor 1 and (b) sensor 7. Grey bars show the number
of matching detections (hit), blue bars show the unassigned events and the
red bars show the events that were not detected as an avalanche (missed). The
hatched bars indicate the number of hits (grey) or misses (red) for the
confirmed events.
Array-based classification
To further reduce the number of unassigned events (potential false alarms),
we applied two array-based post-processing methods to eliminate events,
namely a minimum number of votes and coherence threshold (see Sect. ). To define an optimal number of votes or
coherence threshold we used the same procedure as for the determination of a
minimal duration threshold (Fig. ). Since for these
array-based methods the detections from all sensors were pooled, the number
of unassigned events was very high. This resulted in much higher FAR values
and thus poor model performance with low AUC values, all below 0.5. Arbitrary
values for vmin or Cmin could therefore be
chosen. However, the overall goal of these post-processing steps was to
reduce the number of unassigned events, while still retaining a reasonable
POD. We therefore analyzed the effect of different
threshold values on FAR values as well as on POD values for the probability
classes.
Overall, for both processing steps the number of unassigned events decreased
with increasing threshold values (Figs. and ). By using a minimal number of five votes the POD
stayed relatively high with a low number of unassigned events. Following the
same procedure we selected a coherence value of 0.6. Using this value, the
POD was relatively high and the number of unassigned events could be reduced
to less than 100 events.
(a) POD for each probability class depending on the minimum number of votes (colors). (b) Number of
unassigned events for different number of votes (colors).
(a) POD for each probability class depending on the minimal coherence (colors). (b) Number of
unassigned events for different coherence threshold values (colors).
In total, we thus have three different post-processing steps which can be
applied to the data: a minimal event duration Tmin=12s, a minimum number of votes vmin=5 and a
coherence threshold Cmin=0.6. By combining these three steps,
six array-based post-processing workflows were implemented:
v:vj≥vmin
c:Cj≥Cmin
vc:vj≥vmin and Cj≥Cmin
tv:Tj≥Tmin and vj≥vmin
tc:Tj≥Tmin and Cj≥Cmin
tvc:Tj≥Tmin, vj≥vmin and Cj≥Cmin.
(a) POD for each probability class for different post-processing workflows (colors). v is minimal number of votes
used, t is minimal event duration and c is minimal coherence. (b) Number of unassigned events remaining after the
post-processing for each workflow.
All events detected by at least five sensors. The different colors indicate the number of votes. (a) The results
without any limitation of the duration of the events are plotted. (b) Only events with a minimum duration as mentioned
before are taken into account. On the right the two main avalanche periods are more clearly visible.
The number of unassigned events decreased most with a combined approach
always including the number of votes (vc, tv, or tvc in Fig. ). When using only one array-based post-processing
step, the number of unassigned events remained high (v and c in Fig. b). While the lowest number of unassigned events was
achieved when combining all three post-processing steps, this model also
resulted in low POD values for all probability classes (tvc Fig. a). Overall, the highest POD values and the steepest
decrease for the lowest probability classes were obtained for the voting-based processing with and without a minimal duration of the events (v and
tv in Fig. a). For both these post-processing
workflows the two periods of high avalanche activity are visible (Fig. ). However, by also applying the duration threshold, the
total number of detections decreased and the two periods in March and April
became more clearly visible (Fig. b).
Unassigned events
We compared the automatic detections with the reference avalanche catalogue
and obtained a large number of unassigned events. It remains unclear whether
the unassigned events are all false alarms or partly correspond to avalanches
that were not identified in the reference data. We therefore visually
inspected the unassigned events. In order to keep this reanalysis manageable,
we only focused on the post-processing steps which resulted in less than 50
unassigned events. Thus, we individually investigated single sensor results
from sensor 1 and 4 with Tmin=12s and array-based
results for tv, vc and tvc. The visual inspection showed that for the single
sensor results between 36 and 47 % of the unassigned events were likely
unidentified avalanches and less than one-third were false alarms (Table ). For the array-based results, however, most
of the unassigned events (between 45 and 79 %) were false alarms while
fewer events were likely associated with avalanches that were missed by
.
Results of the reanalysis of the detections not covered by the test data set. Left side of the table shows the results of
the reanalysis of two single sensors, while the right side shows the results of the array-based classification. For the single sensors a
minimum duration for the events of tmin=12 s was taken into account. The voting-based processing steps analyzed are
minimum number of votes (v), minimum duration (t) and coherence (c).
We trained HMMs, a machine learning algorithm, to
automatically detect avalanches in continuous seismic data recorded near an
avalanche starting zone above Davos, Switzerland, for the winter season of
2010. To reduce the amount of data to process, we pre-processed the
continuous data using an amplitude threshold (Fig. ). We then implemented single sensor and array-based
post-processing steps and the performance of the models was evaluated using a
previously published reference avalanche catalogue obtained from the same
seismic data .
After pre-processing the data, the reference avalanche catalogue contained
283 avalanches between 12 January and 30 April 2010, events that were
identified by visual inspection of the waveform and spectrogram of a single
sensor . Since only 25 of these events were
independently confirmed avalanches, considerable uncertainty remained about
the identified events. To reduce the uncertainty in the reference catalogue,
three of the authors therefore re-evaluated the data. This allowed us to
assign seven subjective probability classes between 0 and 100 % to each event.
Overall, only 20 events were marked as certain avalanche (Table ) and hence the performance of the classifiers can
only be evaluated for these particular events. For the remaining events,
there are still uncertainties and hence the performance of the classifier can
only be estimated. Furthermore, this reanalysis highlighted the difficulty in
obtaining an objective and reliable reference avalanche catalogue. It also
showed that expert decisions are biased and there is a need for a reliable
automatic classifier to identify avalanches in continuous seismic data.
Recent work by showed very promising results for applying
a HMM to automatically detect avalanches in continuous seismic data. While
they only focused on a 5-day period during an exceptional avalanche cycle
in 1999, our goal was to classify continuous seismic data spanning more than
100 days. This prevented us from building a single background model to
classify the entire season since temporal variations in feature distributions
at various timescales were present (Fig. ).
Indeed, when using a single background model to classify the entire season
for sensor 1 only two-thirds of the events were detected by having almost 6
times the number of unassigned events. One possible reason for these
variations in feature distribution was likely the setup of the sensor array.
The geophones were packed in a Styrofoam housing and inserted within the
snowpack. As such, less snow covered the sensors than if they had been
inserted in the ground, making them more susceptible to environmental noise.
Furthermore, it is also likely that the snow cover introduced additional
noise in spring due to the rapid settlement and water infiltration. We
therefore recalculated the background model for each day and for each sensor
to classify the data from the same day. However, for the operational
implementation this would be impractical, since there would always be a 24 h delay in the detections. Other strategies for regularly updating the
background model should therefore be investigated
e.g.,.
We performed the automatic classification over the entire season by
recalculating the classifier for each day and for each sensor. Overall,
POD values decreased with decreasing probability
class and the highest POD values were associated with the highest probability
class for all sensors (Table ). Indeed, between 70
and 95 % of all avalanches in the highest probability class were detected,
which is comparable to the results presented by and
, who reported POD values of approximately 65 and
90 %. Nevertheless, without any post-processing, the number of unassigned
events was high, questioning the reliability of the models as many of these
events were likely false alarms. Post-processing of the results was therefore
required. Applying a minimal signal duration drastically reduced the number
of unassigned events while still retaining reasonable POD values, in
particular for sensor 1 and sensor 4 (Table ).
However, there were large differences in model performance between the
sensors (Figs. and ). The
reason for these performance differences is very likely the deployment of the
sensors. Indeed, sensors 1 and 4 were deployed at the top of the slope closest
to a cornice where the snow was the deepest . The
other five sensors were covered by less snow due to local inhomogeneities,
leaving these sensors more sensitive to environmental noise. For future
deployments it will thus be important to deploy the sensors below a
homogeneous snow cover and not within the snow cover. This should reduce the
amount of environmental noise and consequently the number of false alarms.
To further reduce the number of false alarms, we implemented two array-based
post-processing steps, namely a voting-based approach and a signal coherence
threshold. In combination with the minimal event duration, we thus
investigated six array-based post-processing workflows. Results showed that
these array-based methods were effective in reducing the number of unassigned
events (Fig. ). However, the POD values generally
also decreased, resulting in overall fewer detections. Combined
post-processing methods which included the voting-based approach resulted in
better model performance, in line with results presented by
. The best model performance was obtained by combining the
event duration threshold for events with at least five votes. The number of
unassigned events reduced to about 30 and POD values were highest (∼55%) for the highest probability class and decreased for the lower classes.
Despite the large differences in model performance for the individual
sensors, the model still performed marginally better when pooling the data
from the entire array. These results are promising as with an improved sensor
deployment strategy array-based post-processing is likely to further improve.
Comparing our model performance to previously published studies is not
straightforward. We assigned subjective probability classes to our reference
avalanche catalogue rather than using a yes or no approach. Furthermore, we
used geophones deployed in an avalanche starting zone, while
, and used
sensitive broadband seismometers deployed at valley bottom. Therefore, it is
very likely that there was more environmental noise in our data and many of
the detected avalanches in our reference data set were rather small
. Given these differences in instrumentation and
deployment, our detection results are encouraging and highlight the advantage
of using HMMs for the automatic identification of avalanches in continuous
seismic data.
The main advantages of the proposed approach is that only one training event
(Fig. ) is needed to classify the entire season. As
shown by , for large avalanches it is possible to build a
HMM with a high POD and very low FAR with one training event. Even though we
used less-sensitive sensors in this work, we were also able to identify
periods of high avalanche activity (compare Fig.
with Figs. and ). Furthermore,
when only considering the visually confirmed avalanches, the POD was typically around 80 % (see Tables
and ). This suggests that HMMs can easily be
implemented at new sites. In contrast, the model used by
relied on a 10-year database, and
used a set of fuzzy logic rules derived by the
experts. Note that the post-processing steps we investigated are likely
site dependent, in particular the event duration threshold. However, such a
threshold value is intuitive, has a linear influence on model outcome and is
thus easily tunable.
For operational use, the model should be able to automatically detect
avalanches in near real time. The main disadvantage of the proposed approach
is its computational cost. The feature calculation for 1 day takes
≈1h for the pre-processed data and ≈7h for the
unprocessed data. Replacing the used features with computationally less
expensive attributes would decrease the processing time drastically and
encourage real-time applications.
In this work, we classified the data from each sensor individually, requiring
a separate background model for each sensor. The results from the different
sensors were then combined using post-processing rules either on a voting-based approach or taking the coherence into account. Strictly speaking, the
coherence could have been added to the model as an additional feature.
However, calculating the coherence for 21 receiver pairs, even after applying
the amplitude threshold during pre-processing, was still very time consuming
(≈200% real time).
Overall, our results suggest that HMMs may be well suited for the automatic
detection of avalanches in continuous seismic data. The variable model
performance between the different sensors highlighted problems which can
likely be overcome by improving the sensor deployment strategy. Specifically,
we suggest that the sensors should be deployed 30 to 50 cm underground at a
site with a homogeneous and preferably thick snow cover and to increase the
distance between the sensors to apply array processing techniques for source
localization . In addition, further avalanche events may
be used for training to improve model performance. Finally, incorporating
localization parameters as new features in the HMM could open the door for
further model improvement, as is done for the automatic detection of
avalanches in continuous infrasound data .
These features can then either be implemented directly into the HMM or be
used in additional post-processing steps.
Data availability
Due to the huge amount of seismic raw data and parameterized waveforms,
we are not able to provide these data. Instead we provide a data package containing the classification results, the reference
data set, the start and end times of the pre-processed seismic data and some Python scripts. The data are available under the
following:
10.16904/envidat.29 (Heck and van Herwijnen, 2018).
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
Matthias Heck was supported by a grant of the Swiss National Science Foundation (200021_149329). We thank
numerous colleagues from SLF for help with field work and maintaining the instrumentation.
Edited by: Margreth Keiler
Reviewed by: Naomi Vouillamoz and one anonymous referee
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