AUTOMATICALLY FINDING AVALANCHES IN GEOPHONE DATA: A PATTERNRECOGNITION WORKFLOW

Marc J. Rubin∗1, Tracy Camp1, and Alec van Herwijnen2

1Dept. of Electrical Engineering and Computer Science, Colorado School of Mines, Golden, CO, USA2WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland

ABSTRACT: In this article we summarize a pattern recognition workflow to automatically detectavalanche events from passive seismic data collected from geophones near Davos, Switzerlandduring the 2010-2011 snow season. Our workflow consists of three steps: 1) spectral flux based eventselection, 2) feature extraction, and 3) classification. The results are quite promising: our workflowachieves 93% overall classification accuracy with 13% precision for detecting avalanches for the entireseason.

1. INTRODUCTION

Automatically detecting avalanches in nearreal-time (and in any visibility) would provideavalanche forecasters and highway crews withvery important information to help make informeddecisions regarding avalanche danger or roadclosures. In this article we describe a pat-tern recognition workflow to automatically de-tect avalanches from passive seismic (geophone)data. In particular, we describe the signal pro-cessing and machine learning techniques weused to detect avalanches from geophone datacollected during the 2010-2011 winter seasonnear Davos, Switzerland.

Previous researchers have used patternrecognition algorithms to detect avalanches frompreprocessed seismic data. Most notably, theSARA (System for Avalanche Recognition Anal-ysis) software suite uses manually trained fuzzylogic rules to identify avalanches (Leprettre et al.(1996, 1998a,b); Navarre et al. (2009)). Thedownside to their approach is that the fuzzy logicrules are based on manual (expert) analysis ofprevious data; in other words, the rules are notderived automatically or in a timely manner.

Bessason et al. (2007) used a distancedweighted k-nearest neighbor approach (k=3) onpreviously recorded seismic data. The classifica-tion results of this automated method were unsat-isfactory; specifically, only 65% (78 of 119) of theavalanches were correctly identified using their k-nearest neighbor algorithm approach. This worksuggests that there is considerable room for im-provement for automated avalanche detection.∗Corresponding author address: Marc J. Rubin, Dept.

of Electrical Engineering and Computer Science, ColoradoSchool of Mines, Golden, CO, USA 80401; tel: (713) 628 -5822; email: mrubin@mines.edu

2. PATTERN RECOGNITION

In this section we describe the pattern recog-nition workflow we used. First, we briefly de-scribe the seismic data set collected from geo-phones. Second, we detail how we used spectralflux based event selection to pick sizable eventsof interest. Third, we highlight the 10 featureswe extracted from the frequency domain and sub-sequently use for event classification. Lastly, wesummarize our experimentation with 12 differentclassification algorithms trained and tested on theseismic data.

2.1. Geophone Data

The data set consisted of seismic data col-lected during the 2010-2011 snow season fromseven geophones located in a snow slope nearDavos, Switzerland. The geophones recordeddata at 500 Hz with 24-bit precision for over 100days. More details regarding the deployment canbe found in Herwijnen and Schweizer (2011).

Within the seismic data, 385 possibleavalanches were identified, ranging from threeseconds to nearly two minutes in length. Of the385 possible avalanches, 33 were consideredlarge avalanches while the remaining 352 wereassumed to be small avalanches (i.e., sluffs). Ourpattern recognition workflow focused mainly onpositively identifying the 33 large events, i.e., wefelt it was acceptable to miss some of the smallerevents in favor of improved results for detectingthe larger events.

The seismic data was far from clean. Therewas much background and spurious noisecaused by a variety of sources: e.g., wind, skilifts, snow cats, avalanche bombing, helicopters,airplanes, earthquakes, etc. The next section dis-cusses how we processed the noisy data.

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2.2. Spectral Flux Based Event Selection

To select only events of interest from the seis-mic data set, we used spectral flux to determinefive second frames with significant instantaneousincreases in spectral energy. Spectral flux issimply the Euclidean distance between all pointsin two consecutive spectral frames (a 2048-bin,non-overlapping fast Fourier transform (FFT)). Anevent was selected if the instantaneous energywas above a predetermined percentage thresh-old.

In our workflow, we chose a threshold of 90%,meaning that a five-second frame was selectedif the spectral flux increased by 90% relative tothe surrounding five minutes of data (Figures 1and 2). Using this method, we selected 32 of33 slabs, 246 of 352 sluffs, and 32,544 non-avalanche events.

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Figure 1: The spectral flux of a slab avalanche eventthat occurred on January 22, 2011 (at time 400 sec-onds). The red line represents the 90% threshold.

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Figure 2: The frequency domain of a slab avalanchethat occurred on January 22, 2011 (at time 400 sec-onds).

2.3. Feature Extraction

The next step was to transform each five-second selected event into quantifiable featuresthat differentiate the avalanches from the non-avalanche events. Using an open-source Matlabtoolbox created for music signal processing (i.e.,MIRToolBox developed by Lartillot and Toiviainen(2007)), we extracted 10 features from the fre-quency domain (Table 1). These features, oftenused in music pattern recognition (e.g., Klapuriand Davy (2006)), provide a numerical summaryof the size, shape, and peak of the frequencyspectrum (e.g., Figure 3). It is important to notethat the relatively slow sampling rate (500 Hz) andclose proximity of the seven geophones (five to10m) made estimating characteristics of the seis-mic waveform (e.g., velocity, arrival times, backazimuth, etc.,) implausible.

Source FeaturesTop 1% Energy mean, standard deviation,

maximumFrequency Domain centroid, spread, skewness,

regularity, flatness, 85% rolloff,kurtosis

Table 1: We extracted 10 features from the frequencydomain to create a quantitative summary of each five-second spectral frame. Three features (i.e., mean,standard deviation, and maximum) were calculatedfrom the 1% most powerful frequencies of each frame.

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Figure 3: For each five second frame, we calculatedseveral features from the frequency spectrum (2048-bin FFT). This event is a slab avalanche recorded onJanuary 22, 2011.

2.4. Classification

The last and most important step in our pat-tern recognition workflow was to build a modelto detect the 278 avalanche frames from the32,822 total selected events. To do this, we ex-perimented with 12 different classification algo-

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rithms ranging from probabilistic and statistical(e.g., Bayes, Gaussian processes), to highly non-linear function approximators (e.g., artificial neu-ral network, support vector machine). Specifically,we tested an artificial neural network (ANN), naiveBayes, Bayes network, CART tree, fuzzy logicrules, Gaussian processes (Gauss), J48 Tree, k-nearest neighbors (KNN), random forest (RanFor-est), RIPPER, decision stump, and support vectormachine (SVM).

The classification experiments consisted of100 iterations of training and testing, wheretraining was performed using 10% of all knownavalanches (both slabs and sluffs) and an equalnumber of non-avalanche events. In each itera-tion, all remaining data not used for training wasused to test the classifier.

The non-avalanche events were selected us-ing stratified cluster-based subsampling, whichis a method used to mitigate the unfavorable ef-fects of extreme class imbalance issues (Yen andLee (2009)). Briefly, we used K-means cluster-ing to separate the non-avalanche events intoseven different groups, and then employed strati-fied subsampling to insure fair representation ofeach cluster in the training subset. We choseseven groups because there were roughly seventypes of noise events: i.e., wind, ski lifts, snowcats, airplanes, helicopters, avalanche controlwork (bombing), and earthquakes.

The results from our experiments are quitepromising (Table 2). All 12 algorithms had 80%overall classification accuracy or above, with 10performing over 90%. Furthermore, all classifiers(with the exception of KNN) reported precisionrates at or above 7%, with seven achieving pre-cision rates over 10%. The best classifier was adecision stump, which reached 93% overall accu-racy, nearly 90% recall, and over 13% precisionfor the entire season’s worth of data. We notethat neither Leprettre et al. (1998a) nor Bessasonet al. (2007) report the precision of their classifi-cation models.

3. CONCLUSIONS

In this article we present our successful pat-tern recognition workflow to detect avalanchesfrom geophone data. There are several conclu-sions that can be made from our results. Mostnotably, using data from only a single geophonesensor, we can detect avalanches with over 90%accuracy and 13% precision. For the avalancheforecasting practitioner, these results imply that asingle geophone may be a viable and inexpensiveoption to monitor specific avalanche paths. Addi-

tionally, with the rapid improvements in wirelesssensing technology, the possibility of using inex-pensive wireless geophones to detect avalanchesis becoming a reality.

Algorithm Accuracy∗ Recall† Precision‡

STUMP 93.0 89.5 13.2SVM 92.9 89.7 10.5CART 92.4 89.7 12.3Gauss 92.4 91.2 09.9RanForest 92.1 89.5 10.2RIPPER 91.6 88.8 11.7Bayes 91.3 90.3 10.4J48 90.9 88.6 10.7BayesNet 90.4 89.9 07.9ANN 90.3 88.1 08.1Fuzzy 89.7 90.5 08.2KNN 81.4 82.8 03.6

Table 2: The mean results of 12 machine learning algo-rithms trained and tested 100 times on a single sensor’sdata. The results are sorted by accuracy.

There are two future directions we plan totake in our work. First, for the 2012-2013 snowseason, we plan to deploy the seven wired geo-phones in a circular pattern with a radius of 30m(similar to Lacroix et al. (2012)). With such anarrangement, we should be able to estimate theseismic waveforms of each event. In addition, byestimating arrival times, we should be able to cal-culate an avalanche’s seismic velocity and direc-tion of travel; this information will be very helpfulin differentiating ambiguous signals.

Second, in addition to the circular arrange-ment of wired geophones, we also plan to installan inexpensive wireless geophone in the same lo-cation. The wireless geophone is a prototype sys-tem developed by the Colorado School of Minesto record continuous geophone data at 250 Hzwith 24-bit precision and 64X gain (Figure 4). Ini-tial field tests revealed that our inexpensive wire-less prototype (˜$100 excluding sensor) performsnearly identically to a $750 single channel digi-tizer (i.e. Cirrus Logic CRD5378 excluding sen-sor). Results from the 2012-2013 field deploy-ment will inform the viability of our inexpensivewireless platform for detecting avalanches.

∗Accuracy: Percentage of all frames correctly predicted.†Recall: Percentage of avalanches correctly predicted.‡Precision: Percentage of frames labeled as avalanches

that were actually avalanches.

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Figure 4: The GeoMoteShield, designed by ColoradoSchool of Mines, will be used to interface a geophonesensor with an Arduino Fio wireless mote platform.

4. REFERENCES

Bessason, B., G. Eiriksson, O. Thorarinsson,A. Thorarinsson, and S. Einarsson, 2007: Auto-matic detection of avalanches and debris flowsby seismic methods. Journal of Glaciology,53 (182), 461–472.

Herwijnen, A. and J. Schweizer, 2011: Monitor-ing avalanche activity using a seismic sensor.Cold Regions Science and Technology, 69 (2-3), 165–176.

Klapuri, A. and M. Davy, 2006: Signal Process-ing Methods for Music Transcription. Springer-Verlag New York, Inc., Secaucus, NJ, USA.

Lacroix, P., J. Grasso, J. Roulle, G. Giraud,D. Goetz, S. Morin, and A. Helmstetter, 2012:Monitoring of snow avalanches using a seis-mic array: Location, speed estimation, and re-lationships to meteorological variables. Journalof Geophysical Research, 117 (F01034).

Lartillot, O. and P. Toiviainen, 2007: A Matlab tool-box for musical feature extraction from audio.Proceedings of the 10th International Confer-ence on Digital Audio Effects.

Leprettre, B., N. Martin, F. Glangeaud, and J.-P. Navarre, 1998a: Three-component signalrecognition using time, time-frequency, and po-larization information– application to seismicdetection of avalanches. IEEE Transactions onSignal Processing, 46 (1), 83 –102, doi:10.1109/78.651183.

Leprettre, B., J.-P. Navarre, J. Panel, F. Tou-vier, A. Taillefer, and J. Roulle, 1998b: Proto-type for operational seismic detection of natu-ral avalanches. Annals of Glaciology, 26, 313–318.

Leprettre, B., J.-P. Navarre, and A. Taillefer, 1996:First results from a pre-operational system forautomatic detection and recognition of seismicsignals associated with avalanches. Journal ofGlaciology, 42 (141), 352–363.

Navarre, J.-P., E. Bourova, J. Roulle, and D. Y. De-lio, 2009: The seismic detection of avalanches:an information tool for the avalanche forecaster.Proceedings of the International Snow ScienceWorkshop.

Yen, S. and Y. Lee, 2009: Cluster-based under-sampling approaches for imbalanced data dis-tributions. Expert Systems with Applications,36, 5718–5727.

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