Seasonal variations of glacier dynamics at Kronebreen , Svalbard revealed by calving related seismicity

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Introduction
Iceberg calving is a key process of glacier dynamics.It is responsible for 70 % of the annual transfer of mass from glaciers to oceans (van der Veen, 1997), hence contributing to sea level rise (Nick et al., 2009).With tidewater glaciers all around the world retreating, thinning, and accelerating, it is crucial to understand the relationship between glacier dynamics and calving processes.
Iceberg calving is sporadic and therefore requires analysis of single-event data.A wide range of techniques has been used to obtain data of single-event iceberg calving including time-lapse photography (Ahn and Box, 2010), ground based radar (Chapuis et al., 2010), and seismic/acoustic monitoring (Richardson et al., 2010).However, none provide inclusive information about the size, timing, type and location of iceberg calving events, in addition to being fully automatic.So far human-based perception has been used for various glaciers (e.g., Washburn, 1936;Warren et al., 1995;O'Neel et al., 2003O'Neel et al., , 2007) ) and is recognized as the most practical method to acquire information about the calving processes (van der Veen, 1997).However, this technique is practically limited to short observation periods due to the very intensive work in the field.It also contains some obvious problems related to lack of attention from the observers or limited visibility due to darkness or bad weather conditions that may reduce data quality.
Seismic recordings have been used to monitor dynamic glacial activity for about 30 or 40 yr (Vanwormer and Berg, 1973;Weaver and Malone, 1979;Wolf and Davies, 1986).
Studies suggest different processes generating glacier-seismic events, such as sliding at the base due to the glacial flow (Anandakrishnan and Bentley, 1993;Stuart et al., 2005), opening of cracks or crevasses (Blankenship et al., 1987;Deichmann et al., 2000), and calving (Qamar, 1988;O'Neel et al., 2007).Calving events are described as emergent, low-frequency seismic signals, and impulsive, high-frequency acoustic arrivals when measured close to the glacier front (Richardson et al., 2010).The source mechanisms can be fracture processes before the calving (O'Neel and Pfeffer, 2007) Introduction

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Full or the detachment itself, followed by overturning and scraping of icebergs on the ocean seafloor (Amundson et al., 2008).Most studies recorded low-magnitude glacier events at local or regional distances, though moderate glacier earthquakes have been observed globally, for example, deriving from the outlet glaciers on Greenland and in Antarctica (Ekstr öm et al., 2003;Nettles and Ekstr öm, 2010).
The use of seismic data supplementary to direct (visual) monitoring of calving activity therefore opens the potential to match calving activity to seismicity and thus monitor calving autonomously.Once the relation and scaling between calving events and their seismic signals is understood, seismic records may improve the understanding of glacier calving and reveal variations in calving activity over longer time periods than monitoring through direct observations.
The objectives of this study is to obtain a continuous record of calving activity over several months from seismic data recorded at Kronebreen (78 53 • N, 12 30 • E), a grounded, polythermal tidewater glacier, located approximately 14 km south-east of Ny-Ålesund in Western Spitsbergen (Fig. 1).Kronebreen is one of the fastest tidewater glaciers in Svalbard with an average front velocity ranging between 1 and 3.5 m per day during the summer months (K ä äb et al., 2005;Rolstad and Norland, 2009) and with a terminal ice cliff having an elevation ranging from 5 to 60 m above the fjord surface at the end of August 2008 (Chapuis et al., 2010).We installed a geophone in the vicinity of the calving front from spring to autumn in 2009 and 2010 and collected ground-truth data of iceberg calving for 16 days by visual observations with an overlap of dataset of 11 days.Seismic detections are calibrated against the direct observations to extrapolate seismic signals due to calving activity beyond the ground-truth period, providing a proxy for the calving rate.In addition, we monitored the glacier velocity by using GPS measurements close to the front, and recorded the changes in front position by tracking the glacier terminus using terrestrial photogrammetry.In combination with the estimated calving rate, this data set provides an insight into glacier dynamics at the front for several months.Introduction

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Full A Campell CR1000 data logger recorded the raw voltage signal from the geophone which was sent to final storage through a serial cable to an Acumen compact flash memory module (because the Campbell data storage would not allow a full summer recording).This approach was restricted by transfer speed from the Campbell to the memory module which limited the amount of data we could record to 50 Hz rather than the ideal 100 Hz that is capable from the Campbell.Power supply was provided by a 12 V battery and a solar panel.In May of 2009 and 2010, the geophone was drilled 6 m into the ice and frozen into place.Melting during summer decreased the thickness of the overlaying ice layer to about 3 m.The position of the geophone (see Fig. 1), and therefore also the coupling with the ice and the noise level, differ slightly between 2009 and 2010 since the instrument has been removed during the winter months.The analysis of the seismic record in this paper does not include localization of events and the detailed investigation on their source mechanisms, as that would require more receivers, records of all three spatial wavefield components, and a sensitivity towards lower frequencies (< 10 Hz).

Direct calving observations
We available for matching calving events to seismic detections.The camp from which we observed the glacier front was located approximately 1.5 km west of the front, which provided good coverage of the front.We estimated that 90 % of the front was visible for the observers (see Fig. 1).
For each calving event within the period of visual observation we registered the time (to a relative accuracy of 10 s), style, location and size.The style of iceberg characterizes the type of calving event.We follow O'Neel et al. (2003) classification into 6 classes: avalanches, block slumps, column drops, column rotations, submarine and internal.Avalanches and block slumps affect only parts of the glacier front, block slumps being bigger than avalanches.Column drops affect the entire subaerial part of the ice front which collapses vertically.Column rotations collapse with a rotation movement and can affect the subaerial part of the ice front alone or the entire ice wall, submarine part included.Submarine events are icebergs being detached from the ice front below the water line.The last type of event is internal and refers to either very small calving events that we could not visually observe or ice blocks falling into crevasses.In both cases they are related to glacial activity close to the front.
We also visually estimated a size for each event, which reflects the volume of ice detached from the front during a calving event.It allows a semi-quantitative approach as first introduced by Warren et al. (1995).The size scale repeats the one defined by O'Neel et al. (2003) but we extended it from 1 to 20, 20 being the entire front width collapsing.We estimated the error on the size scale caused by the subjectivity of the observers to be ±1 based on common observation periods where we compared the size each observer gave for a set of training events.This size scale is an indirect measure of iceberg volume (Chapuis and Tetzlaff, 2011).
Finally, we located each event by dividing the 3.5 km glacier front into 6 zones.Zone 6 comprises 700 m of the northernmost part of the glacier front, Zone 5 ranges from 700 to 1500 m, and Zone 1 to 4 are 500 m long each, where Zone 1 is the closest to the observation site (Fig. 1).Introduction

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Glacier velocity from GPS measurements
Glacier velocity is measured using a single code based GPS receiver approximately ∼ 2 km from the front position.The instrument is an IMAU construction (den Ouden et al., 2010) that records one measurement every hour which is sent through ARGOS to the base server.Velocity is determined through a variety of filtering techniques based upon the number of days of smoothing.Here, we use an average of 3 days.

Front position from terrestrial photogrammetry
Repeat photographs were taken automatically every hour from the same location (star in Fig. 1) using Harbotronics time-lapse cameras (e.g., Chapuis et al., 2010).Weekly pictures are used to track the front position.Results are provided in relative changes that allow us to determine whether the glacier front was retreating or advancing, which is the most relevant information for this study.We have photographs from 9 May 2009 until 29 September 2009 and from 16 April 2010 until 13 November 2010.Details on the acquisition systems and methods are described in Chapuis et al. (2010).

Method
Conventional automatic event detection in seismology is usually done using Short Time Average over Long Time Average (STA/LTA) trigger algorithms which report distinct signal arrivals when amplitudes exceed the background noise level, but generally do not distinguish between different kind of events.In order to achieve a more precise classification and to handle the large amount of available data, making use of automatic pattern recognition techniques is therefore becoming increasingly important in seismological studies.Supervised classification algorithms can be employed to detect seismic events based on manually prepared training data sets (e.g., Joswig, 1990;Dowla et al., 1990).On the other hand, unsupervised pattern recognition may be used to generate Introduction

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Full an initial understanding of the unknown data properties without utilizing existing class or event labels as done for supervised learning (Bardainne et al., 2006;K öhler et al., 2009).Clustering is a well-known unsupervised learning method which describes the task to find a meaningful grouping of unlabeled data into respective categories (e.g., Jain, 2010).Here, we present and apply a processing scheme to detect and identify glacier-seismic signals which combines event detection using a STA/LTA trigger and clustering.While the trigger algorithm will automaticly detect all sorts of seismic events in the data, clustering detections into groups with similar signal characteristics helps to distinguish different types of seismic events and false alarms.This approach is suitable and reasonable for our purpose, since no detailed information about the character of potentially observable glacier-seismic signals was available a priori.

Seismic event detection
We use a modified version of the STA/LTA trigger function introduced by Allen (1978) which also provides an estimate for the end time of the event (STA function falls below a certain threshold for a defined number of time steps).We calibrate the algorithm parameters based on visual assessment of identified events in selected time windows.
A STA window length of 0.4 s, a LTA window length of 3.5 s, and a STA/LTA threshold of 3 are chosen (other parameters: C2 = 3, C8 = 3, D8 = 1 s, D5 = 0.6 s, see Allen, 1978).This parameter setting makes the detection algorithm very sensitive to catch all event types, including short and weak ones.A drawback of such sensitivity is that the algorithm results in many false detections.We deal with this problem in the second phase of our approach.

Seismic event clustering
Although clustering is considered an unsupervised process (i.e.grouping of data itself is fully automatic), human interaction is an integral part.Choosing a meaningful number of clusters, validating and interpreting the results are crucial steps which must be Introduction

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Full accomplished by the analyst.Most algorithms generate a cluster solution for a fixed number of clusters and have, therefore, to be tested using different values.Furthermore, cluster validation requires an assessment whether the clustering solution is in fact a good representation of the natural grouping of the data set.In order to select the most meaningful solution, quantitative approaches can be used to compute a measure for the cluster validity, e.g. the Davies Bouldin index (DB, Davies and Bouldin, 1979).However, there is often not a single best solution and validity measures may not result in the most meaningful grouping.Visualization of clustering solutions, which must be useful also for multi-dimensional data, is one way to solve this problem (Vesanto and Alhoniemi, 2000).Finally, once a cluster solution has been found representing the natural grouping of the data, the meaning of clusters has to be determined based on expert knowledge, e.g. by considering examples or a generalized pattern from each cluster.Within this process, it might become necessary to choose another cluster solution or split and merge individual clusters.
We apply the Self-Organizing Map (SOM) technique, a quasi-artificial, unsupervised neural network used to intuitively visualize and cluster multidimensional data (Kohonen, 2001), which has been successfully applied for pattern recognition in seismology (Maurer et al., 1992;Musil and Plešinger, 1996;Tarvainen, 1999;Plešinger et al., 2000;Esposito et al., 2008;K öhler et al., 2010).The main property of SOMs is that data can be mapped on a two-dimensional, regular grid of usually hexagonal SOM units.This mapping is ordered and topology-preserving, meaning that close or similar data vectors in the input space are also close on the SOM.In that way one can visualize the distribution of multi-dimensional data in two dimensions, so that location of data projected on the SOM reflects the natural data grouping in the input space.As a final step, the SOM units can be grouped automatically using common clustering methods.
For more details about the SOM method see Kohonen (2001).In order to cluster a set of detected seismic events, a set of discriminative features is required for each detection which form the input data vector.We choose features that are potentially suitable to distinguish waveforms of different event types and false detections.These features are based on statistics on the seismogram amplitudes, the frequency spectrum, and temporal characteristics of an event (Fig. 2): -Number of Runs: The number of runs is based on a significance test (Runs Test) for temporal randomness which evaluates whether all samples of a sequence are mutually independent (Wald and Wolfowitz, 1940;K öhler et al., 2009).A run of a time series is a sequence of adjacent samples below or above the mean, i.e. a white noise time series would have a high number of runs since the amplitude varies randomly around the mean over time.The seismogram time window considered to compute the number of runs begins 20 s before the event onset and ends 20 s after the event stopped according to the estimate made by the trigger function.The seismogram time window considered to compute the number of runs begins 20 s before the event onset and ends 20 s after the event stopped according to the estimate made by the trigger function.
-Spectrum: The frequency spectrum is computed from the detected signal only (no temporal context).The ratio of mean spectral amplitudes between 12 and 19 Hz and 0.5 to 25 Hz is computed to account for different frequency content of seismic signals.
-SNR: The Signal to Noise ratio is computed as the logarithmized ratio of the RMS (root mean square) of the event amplitudes and the RMS of the time window before the event which has the same length as the event.
-Length: The duration of the event in seconds is obtained from the STA/LTA trigger.

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SOM training and cluster definition
The STA/LTA trigger generates 24 278 detections for the entire seismic record, including an unknown amount of false detections.We use all detections to generate the SOM input data set by computing the characteristics introduced above from each signal.The frequency distribution of each feature in Fig. 2 reveals that at least two distinguishable classes are present in the data set, since some features show a clear binomial distribution.
After the SOM is trained, it is clustered using an average linkage hierarchical clustering algorithm (Vesanto and Alhoniemi, 2000).Cluster solutions from 2 to 35 clusters are generated.The best solution is defined manually using the DB index as a guideline and evaluating the so-called unified distance matrix plot of the SOM (U-matrix, Fig. 3a) which illustrates the probability density distribution of data vectors (Vesanto and Alhoniemi, 2000).Comparison of the clustered SOM (Fig. 3b) and the U-matrix allows for the validation of clusterings.For a perfect grouping, cluster borders should appear as more reddish (less-dense) areas in the U-matrix plot in comparison to the regions inside the clusters.In other words, a cluster is a bounded, blue area on the SOM.We correct the number of clusters obtained from one good solution (low DB index) by splitting individual clusters based on the hierarchical cluster solution (grey clusters in Fig. 3b), resulting in 25 clusters.Introduction

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Classification based on direct observations
The cluster solution in Fig. 3b represents the grouping of seismic detections in the feature space.However, in order to identify the meaning of each cluster, ground-truth data is required to match known event types to their corresponding clusters.Further-  2010) detections are obtained (white symbols on the SOM in Fig. 3b).We then match these seismic detections with directly observed calving events at the glacier front.We choose a 40 s long time window (±20 s from start of a detection) to find calving events which could be related to the corresponding seismic event.Table 1 presents a summary of the results for the different types, locations, and sizes of visually observed calving events.
In order to evaluate the resulting recognition rates and to exclude random matches, we apply a binomial test for statistical significance (Table 1).Results show that the matches for Zone 2, 3, 4, and 6 can be explained by a number obtained by chance assuming a significance level of 5 % for passing the test for randomness.Only results for Zone 1 and 5 cannot be explained by random matches.However, only calving observations in Zone 1, which is the closest to the geophone, are recognized as seismic events with a rate of 9 %, which is clearly higher than for the other zones.The rates are even higher when we only consider block slumps and column drops in Zone 1.For column rotations and submarine events, too few observation are present to obtain a reliable statistic.However, it seems that avalanches do not emit clear seismic signals strong enough to be recorded by the geophone.The recognition rate increases with size of the observed calving event up to 16 %.It is intuitively clear that only the closest 3302 Introduction

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Full and largest events are detectable, since the detection threshold is limited by the noise level in the seismic data.Even though we obviously are not able to monitor seismic emissions from the entire glacier front, we can proceed with investigating the subset which we are able to detect.We cannot exclude that we observe also events from the other zones (e.g.Zone 5), but only for Zone 1 the evidences are strong enough that we see indeed calving events in the seismic data.
The matching rates in Table 1 have been computed using all detections and are not based on specific clusters.However, we are now able to identify individual event clusters considering the detections matching with Zone 1 events.The red symbols in Fig. 3b represent the matching detections in the SOM space and are clearly confined to the lower part of the map.Hence, clusters located within that area are most likely glacier-event classes (i.e.iceberg calving).Some clusters do not include matches with direct observations, but the corresponding detections are clear seismic events.Furthermore, there are transition clusters where it is not clear whether the corresponding detection are instrument artifacts or very short and weak seismic events.Those clusters are not labeled as event clusters.To simplify the following discussion, we reduce the obtained clusters to four classes based on the percentage of matched detections (Zone 1) within a cluster (Table 2 and Fig. 3c).Those classes reflect the uncertainty of whether signals are related to the calving process as well as the character of its signal.The classes are: -Class 1: Clear glacier-seismic events related to calving (> 30 % matched within cluster) -Class 2: Most likely glacier-seismic events (> 5 % could be matched) Two matches with Zone 1 events (from 20 in total) are clearly identified as false detections (Class 4) which matched by chance (see Fig. 3b and Table 2), in agreement with what is expected from the significance test (Table 1).For the other locations, the fraction of Class 4 events among all matches is significantly higher, what confirms our hypothesis that these matches are produced by coincidence for the most part (6 of 10 for Zone 2, 5 of 6 for Zone 3, 6 of 11 for Zone 4, 11 of 13 for Zone 5, 4 of 4 for Zone 6).

Seismic signal characteristics
In order to investigate the meaning of clusters, SOM component plane plots are useful for displaying the feature distribution or values of a particular data vector component which is associated with any of the SOM units (Fig. 3d-f).Randomly selected examples of seismic waveforms (Fig. 4) and the SOM component planes show the different characteristics of each event class.As expected, all event clusters (Class 1 to 3) are characterized by high signal to noise ratios (SNR) compared to the rest of detections (Fig. 3f).Class 1, which is clearly related to iceberg calving, consists of rather long events, typically 4-10 s (see Fig. 3d), with several local maxima in their amplitude (Fig. 4).Very short signals seem to be characteristic of Class 2. Detections with high amplitude peaks (higher standard deviation, Fig. 3e) mainly belong to the event group which could not be correlated with calving events (Class 3).We do not observe typical seismic signals with clearly separated P and S-Wave onsets.The complexity of waveforms, especially those of Class 1 (Fig. 4), could reflect the nature of a calving event, which is rather a sequence of events than a signal from a single, shortly acting source.It is also possible that we observe parts of the acoustic signal of an event coupled with the ice surface in addition to direct seismic waves (Richardson et al., 2010).It should be noted that due to the limited sensitivity of the instrument we are not able to record seismic signals at longer periods which have been shown to be emitted by calving events (Richardson et al., 2010).Introduction

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Temporal patterns in glacier-related seismicity
We obtain 138 detections belonging to Class 1, 2, and 3 which can be interpreted as glacier-seismic events within the period of direct observation.When we assume that seismic signals are generated by the same (glacial) processes over the year, a number of 7850 events occurred within the entire time of seismic measurement, among which 792 belong to Class 1. Seismic activity seems to be generally higher in 2009 compared to 2010 (Fig. 5).However, the comparison between the absolute event rates in both years might be biased due to changing location and coupling of the instrument.In both years the event activity seems to be higher in autumn than in the summer months.
The general seasonal trend is overlaid by short-term patterns of periods of 10 to 15 days in both years.For Class 1 events several peaks in the event rate in July 2009 are observed.However, at least one minimum of activity in July coincides with increase background noise level in the seismic data (see Fig. 5a).Therefore, it is not clear whether simply less events are detected on the geophone or less signals are emitted by the glacier.In 2010, the noise level is more stable besides one peak in the beginning of May.The noise level seems to increase slightly in summer 2010 which could be a result of decreasing burial depth of the geophone or continuous emitted noise of melt water.There are pronounced peaks in event rate in May and in beginning of August for Class 1 events (Fig. 5b).

Extrapolation of calving rate beyond calibration period
Within the time period of direct observations, Class 1 is clearly related to iceberg calving.We have to make two assumptions if we want to extrapolate the event rate of this class as a proxy for the calving rate beyond the calibration period.First, there should be no other dominant process at the glacier that generates Class 1 events in the calibration period and behaves independently from calving over time (e.g.basal sliding, fracturing).We have to consider this possibility since about 43 % of the Class 1 events do not exhibit matches with directly observed calving (Table 2).Secondly, even if all

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Full Class 1 events are generated by calving in the calibration period, there could be another process which is not active in the calibration period and generates Class 1 events at another time.However, different glacial processes have been shown to emit signals with different characteristics (e.g., Stuart et al., 2005;West et al., 2010).Therefore, the clustering which we performed should be able to distinguish those processes.Furthermore, when we assess similarity of seismic events on a more detailed level and consider sub-clusters of Class 1, i.e. divide the class into the hexagonal SOM units (see Fig. 3) and consider the associated seismic detection, we observe the same temporal patterns as for all events in Class 1 and no sub-cluster includes events which occur within particular time periods only.Therefore, we have good indications that most events in Class 1 are indeed related to calving, however we cannot fully exclude the possibility that other processes or local seismic sources could be entrapped in our proxy for glacier calving due to the limitations of our data set.Note that Class 1 events do not represent the total amount of calving events at the entire glacier front, but consist of signals emitted by the largest events close to the measurement site.

Relationship between calving process and glacier dynamics
Recent thinning, acceleration and retreat of tidewater in different parts of the world raises the question of the relationship between calving processes and glacier dynamics.Is iceberg calving the cause or the consequence of glacier acceleration?Benn et al. (2007) reported that an equal number of studies bring evidence in favor for both views, calving causing glacier flow increase (e.g., Hughes, 1986;Motyka et al., 2003;Joughin et al., 2004;Nick et al., 2009) and increased calving activity following glacier acceleration (e.g., Venteris et al., 1997;Kirkbride and Warren, 1999).
To investigate the relationship between glacier speed and calving activity on seasonal time-scales, we analyzed qualitatively the three dataset available for 2009 and 2010: indirect measurement of iceberg calving (counts from seismic monitoring), glacier velocity (GPS measurements) and front positions (photogrammetry).In of observations.Furthermore, the change in front position is shown (first derivative).
A positive front position indicates advance and negative retreat of the front with respect to the reference date.The solid green line represents the average position of the entire front while the dashed green line represents the front position only in Zone 1.We attempt to generate a timeline of the processes described above to determine which happened first and triggered the other ones.
For the year 2009 (Fig. 5a), the glacier speed is rather constant during the spring with acceleration beginning in mid-June, several peaks during the summer and deceleration at the end of July.Velocity remains more or less constant during the entire autumn except a peak at the end of August caused by a rather large rain event.The calving related seismicity, represented by the seismic Class 1 events, remains rather low during the summer with three peaks that are more or less synchronous with the glacier speed peaks.During the autumn, our proxy for calving activity is about three times larger than during the summer.Finally, the glacier front slowly advances during the spring, reaching a plateau at the beginning of July when the front advance is at its maximum.
The front remains rather constant all summer and starts retreating at the mid to end of July until the end of September (end of our photographs dataset).The change in front position is positive only during spring, it then becomes zero until the beginning of July and then negative until the end of September.
In 2010 (Fig. 5b), the glacier speed starts to increase in mid-May until a maximum is reach at the end of June, followed by a short decrease and another maximum at the end of July before decreasing drastically until mid-August where it reaches another smaller maximum and finally decreases until the end of September.The calving related seismicity remains rather low during the spring and the summer apart for two maxima, one mid-May and one at the beginning of August.The last one correlates well with an increase in velocity.At the end of August the calving related seismicity is increasing and staying at a higher level than during summer until the end of October.Finally the glacier position behaves differently than in 2009 with a fast and constant advance from mid-April until mid or the end of July, immediately followed by a rather fast and Introduction

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Full constant retreat until the beginning of October when it starts to plateau while continuing to retreat.The change in front position is mostly positive until mid-July, is zero for a few weeks and then becomes negative until mid-November.
From the two reconstructed timelines we can identify some patterns in the timing of the glacier dynamic events.Seismicity as a proxy for iceberg calving remains relatively low from May until mid-September while both velocity and front position undergo large fluctuations.For both years, calving related seismicity increases in the autumn, when velocity is lowest and rather constant and the front retreating.Changes in velocity do not affect the seasonal fluctuations observed in calving related seismicity, namely low activity during spring and summer and increased activity from September on.On the other hand, changes in velocity might affect small, weekly variations observed in calving related seismicity, with an increase of iceberg discharge when velocity increases, as in the case in spring 2009 and early August 2010.
On seasonal time-scales, calving seems to be a dynamical process not following the variation in velocity.Calving related seismicity shows marked increase in the autumn, which is visually translated by a continuous retreat of the glacier front.In this case, the large release of ice at the front cannot be explained by a larger ice flux, since velocity is at its lowest and rather constant.This shows that our proxy for the calving rate is controlled by other dynamical variables and that calving activity seems not to be directly related to the absolute value of glacier velocity at the front.Velocity and calving related seismicity are obviously linked, but through intermediate processes like stretching rate that favors the opening of crevasses (Venteris et al., 1997), basal velocity that influences the basal conditions, velocity linked to stretching of the ice and potentially thinning, etc., all those processes leading to a more unstable glacier front more prone to calving.Introduction

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Conclusions
We have analyzed seismic data and direct visual observations of calving events at the terminus of Kronebreen, Svalbard.We have applied a traditional STA/LTA trigger algorithm with a very sensitive setting to detect seismic signals emitted by glacial activity.
The signals of all detections have been clustered to distinguish between different types of events and false detections.For clustering and identification of event clusters Self-Organizing Maps have been used which simplifies visualizations of multi-dimensional data.By comparing ground-truth data from the calving front with the obtained seismic detections, we are able to match about 10 % of close calving events (< 1 km from the geophone) with seismic signals.This allows us to define three seismic event classes which are, with different degrees of uncertainty, related to glacier activity.By extrapolating our results beyond the time of direct observations, about 5100 seismic events are detected overall during several months in 2009 and 2010, including signals due to calving and probably also signals emitted by other sources in the glacier.The class of seismic events clearly related to calving activity suggests about 790 larger calving events in the vicinity of the seismic instrument.We have found that we are not able to monitor the entire calving front and to detect smaller events due to the noise level in the seismic data.This may be a result of the low sensitivity of the geophone.Nevertheless, using this subset of events as a proxy for activity at the glacier front, temporal patterns in the event rate are found that reveal seasonal changes.
We have analyzed the relationship between glacier velocity, front position and calving rate.Higher calving related seismicity is found in autumn compared to the summer.Considering short-term variations, the seismic event rate is at least partially correlated with patterns in the ice flow velocity measured close to the glacier front with peaks in velocity corresponding to small peaks in calving related seismicity.However, on a seasonal time-scale, velocity and rate of seismic events due to calving behave rather independently: in the autumn we observe a large increase in calving related seismicity while velocity is constant and at its lowest values for the year.On seasonal time-scales, Introduction

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Full iceberg calving might be controlled by other glacier dynamical processes like stretching rate, basal sliding, crevasse deepening due to melt water at the glacier surface, buoyancy perturbations, front destabilization due to changes in the front geometry or to calving activity itself (Chapuis and Tetzlaff, 2011).
Our results show the capability of monitoring glacier activity with seismic receivers to extend observational data sets and to obtain new insights about glacier dynamics.We found that a single-channel geophone, although limited by its sensitivity, can deliver useful information about calving activity.Improving the autonomous classification of calving related seismicity will benefit from more than one instrument to determine the location of seismic sources at the glacier as well as more sensitive instrumentation (e.g.Full  Full  Full Discussion Paper | Discussion Paper | Discussion Paper | Several months of single-channel (vertical component) seismic data have been recorded on a PE-3 geophone from 29 June to 15 August 2009, 11 September to 11 November 2009, and 8 May to 6 November 2010 with a sampling rate of 50 Hz.
monitored the calving activity at Kronebreen based on human perception (viewing and hearing).Midnight sun in this region lasts from 18 April to 24 August which allowed continuous visual monitoring of the calving activity within our summer field excursions.Four persons observed the calving front of Kronebreen during a total of 16 days split into two periods: from 14 August 2009 00:00 to 26 August 2009 16:00 GMT and from 5 August 2010 23:30 to 15 August 2010 16:00.An overlap with seismic data of Introduction Discussion Paper | Discussion Paper | Discussion Paper | ∼1.5 days in 2009 (477 observations) and 10 days in 2010 (2413 observations) are Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Examples and more detailed description of SOM clustering and visualization can be found e.g. in K öhler et al. (2009) and K öhler et al. (2010).Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | -Skewness: Standard deviation and skewness are computed from the signal envelope.The same time window as for the number of runs is used.Both features are normalized with the mean of the envelope.
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Class 3: Maybe glacier-seismic events (no matches, but clearly no false detections) Class 4: No glacier-seismic event (instrument artifacts and triggered background noise fluctuationsDiscussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Fig. 5 the front position is indicated by a relative position compared to the first day 3306 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper |

Fig. 1 .Fig. 2 .Fig. 3 .Fig. 4 .Fig. 5 .
Fig. 1.Location of Kronebreen on Svalbard and position of instrumentation and observation site close to the calving front.Lower photo shows view of glacier front as seen from the camera position close to the visual observation site.
more, we have to inspect detection examples from each cluster and decide what sort of signals is present based on seismological expertise.Due to the lack of man-made noise in the remote study area, we can assume that most detected seismic events are related to natural (e.g.glacial) activity.First, all detected seismic signals are identified during the period of direct observations, in which 98 (2009) and 238 ( Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | van der Veen, C. J.: Calving Glaciers: Report of a Workshop February 28-March 2, 1997, Tech.rep., BPRC Report No. 15, Byrd Polar Research Center, The Ohio State University, Columbus, Ohio, 1997.3293Venteris, E. R., Whillans, I. M., and van der Veen, C. J.: Effect of extension rate on terminus position, Columbia Glacier, Alaska, USA, Ann.Glaciol., 24, 49-53, 1997.3306, 3308  Introduction

Table 1 .
Results of the matching between seismic detections and visually observed calving events for 11 days."All" means that all observations are used including a number of 101 not assigned to one of the 6 Zones."Matching Rate" is percentage of visual observations that can be related to seismic detections."Significance Random Match" refers to binomial test for statistical significance of matches.It is significance that matches can be produced by chance.

Table 2 .
Event classes obtained from clustering and defined based on matching seismic detections and visual observations.Class 1: clear glacier events related to calving, Class 2: most likely glacier events, Class 3: maybe glacier events, Class 4: no glacier events."Detections in Matching period" is the number of seismic detections during the matching/ground-truth period."MatchesZone1" is number (and percentage) of seismic detections which can be related to a direct observation in Zone 1. "All detections" refers to seismic detection within entire time of seismic recording in 2009 and 2010.The last column states the clusters merged to define classes (see Fig.3b,c).