Towards the systematic reconnaissance of seismic signals from glaciers and ice sheets &ndash; Part A: Event detection for cryoseismology

Latto, Rebecca B.; Turner, Ross J.; Reading, Anya M.; Winberry, J. Paul

doi:https://doi.org/10.5194/egusphere-2023-1340

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https://doi.org/10.5194/egusphere-2023-1340

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20 Jul 2023

| 20 Jul 2023

Towards the systematic reconnaissance of seismic signals from glaciers and ice sheets – Part A: Event detection for cryoseismology

Rebecca B. Latto, Ross J. Turner, Anya M. Reading, and J. Paul Winberry

Abstract. Cryoseismology is a powerful toolset for progressing the understanding of the structure and dynamics of glaciers and ice sheets. It can enable the detection of hidden processes such as brittle fracture, basal sliding, transient hydrological processes, and calving. Due to the diversity and often low signal-to-noise levels of glacier processes, the automated detection of seismic events caused by such processes can pose a challenge. We present a novel approach for the automated detection of events in glacier environments, the multi-STA/LTA algorithm, with a focus on capturing the many signal types recorded on ice sheet margins. This develops the use of approaches that use the ratio between short and long time averages (sta,lta) of signal amplitude as the means of event detection. Implemented in the open source and widely used ObsPy python package, the algorithm constructs a hybrid characteristic function from a set of sta, lta pairs. We apply the multi-STA/LTA algorithm to data from a seismic array deployed on the Whillans Ice Stream (WIS) in West Antarctica (austral summer 2010–2011), to form an event catalogue. The new algorithm compares favorably with standard approaches, yielding a diversity of seismic events, including all previously identified stick-slip events (Pratt et al., 2014), teleseisms, and other noise-type signals. We investigate a partial association of seismicity with the tidal cycle, and a slight association with ice temperature changes of the Antarctic summer. The new algorithm and workflow has the potential to yield systematic catalogues for further cryoseismology studies: conventional glacier seismology, and those tailored to pattern recognition by machine learning.

Received: 19 Jun 2023 – Discussion started: 20 Jul 2023

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30 Apr 2024

Towards the systematic reconnaissance of seismic signals from glaciers and ice sheets – Part 1: Event detection for cryoseismology

Rebecca B. Latto, Ross J. Turner, Anya M. Reading, and J. Paul Winberry

The Cryosphere, 18, 2061–2079, https://doi.org/10.5194/tc-18-2061-2024,https://doi.org/10.5194/tc-18-2061-2024, 2024

Short summary

Rebecca B. Latto, Ross J. Turner, Anya M. Reading, and J. Paul Winberry

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2023-1340', Anonymous Referee #1, 07 Aug 2023

This manuscript present a new algorithm to detect seismic events. The method is based on the classical sta/lta detection method.

It runs the algorithm using a wide range of time windows (tSTA and tLTA) to build a hybrid detection function.

This method allows to detect a wide variety of seismic signals covering a huge range of signal durations (0.1 - 10000 s).

The method is applied to a dataset from the Whillans Ice Stream. However, this manuscript does not provide new information on the source of these seismic signals.

Most possible "stick-slip" events are already known (Pratt et al, 2014) and their correlation with tides has already been extensively discussed.
I am rather disappointed by this paper.

First, it is purely methodological with no result on glacial processes.

The method could also be applied in many fields of seismology (landslides, volcanoes, ..) that also exhibit a large variety of seismic signals.

It could thus better fit in a journal on seismology.
Second, I am not fully convinced by the advantages of the method compared to other methods.

The main advantage is to automatize what many researchers do by trial and errors.

Many researchers adapt the time windows of the STA/LTA methods (Short and Long Time Average of seismic energy) in order to detect most events

that can be observed by eye when looking at seismograms and spectrograms, while simultanouesly decreasing the rate of "false detections",

ie, anthropogenic and environnemental noise, teleseisms or other types of events different from what they look for.

But I think that this first step of looking at seismic data (on a small subset of the dataset) is essential to discover different types of events and to select signal of interest.
Also, the method only considers two parameters (tSTA and tLTA) but does not discuss the other parameters: the minimum ratio of short and long-term energy used to define an event and the frequency band.

I guess the authors do not filter the data, while it could be an efficient way to remove noise events and to detect weaker events by selecting the frequency of interest and where the signal/noise ratio is largest.
I feel that the method allows to detect more events but most of these events are maybe noise, such increasing the work of event classification, which is the most difficult task.

I believe that a simpler STA/LTA method with well chosen parameter could be almost as efficient than the proposed multi-STA/LTA algorithm, while reducing the number of "false detections".

At least, the authors need to demonstrate that their method detects more events but without increasing the fraction of false detections.

The multi-STA/LTA algorithm is compared with the standard STA/LTA method, but only for the two extreme models (very short or very long time windows, l232).

Why not using all models or the average model?
The manuscript is often hard to read and understand, many points should be clarified (see minor points below).
The manuscript stops when things could start being really interesting.

What are the newly discovered "stick-slip" events? Why were they not detected before? Are they weaker than the others or do they have a different waveform? Could you try to locate these new events?

What are the "tremor-like" signals mentionnent on l326?

Details and minor points:

Figure 1. Plots (a) and (b) could be removed, all information is also on the other subplots.
Algoritm description, section 2.1.

The classical recursive STA/LTA algorithm should be described (even it is described in the cited references) as it is the base of the multiSTA/LTA method.
l118. What are the values of the minimum STA/LTA thresholds (trigger and detrigger) used to define an event?

How are they chosen? Why not optimizing these parameters as done for the time windows?

Fig 1 suggests the threshold is fixed at 3 and is the same to trigger an detrigger an event. Did you try other values?

I don't understand eq. (1) and point (3) (l116). Is the hybrid function the average (as in eq(1)) or the maximum value (l116) of all single-parameters STA/LTA functions?

l133. I don't understand what represents epsilon?

Section 3.2 should be moved to the "method" section 2. It describes how the catalogue is compiled and is not specific to the Whillans Ice Stream catalog.

l194: I don't understand this sentence: " The reference time precedes this arrival time by half the network time for the N closest seismometers."

What is the "network time"? What is the value of N?

l195 "We also take into account that multi-STA/LTA will decompose events into smaller events based on amplitude variations."

I think this is a drawback of the method; and a simpler STA/LTA method with well chosen parameters and with a detrigger threshold lower than the trigger threshold may avoid this problem.

You should describe in more details how you merge "overlapping events" into one event to obtain the correct event duration.
l213 "Taking uncertainties in start time into account, we label 140 events as stick-slip. Four of those events are determined as additional to the Pratt et al. (2014) catalogue from a manual reconnaissance."

How do you know that these 4 events are "stick-slip"? Could you show examples of a newly detected "stick-slip" event and a known stick-slip event?

l219 "What means "a.u.": arbitrary unit?
l267 "The distribution of the peak amplitude occurrences provides source mechanism information".

Could you specify which "source mechanism information"? Do you mean the Gutenberg-Richer b-value?
l268 "However, in cryoseismology, the actual magnitude cannot be determined because the material strength, slip distance, and area of slipped fault are usually indiscernible."

I don't think this is a big issue and a major source of uncertainty on magnitudes. The problem is even worse for earthquakes that occur at depth were material properties are less constrained.
l271 "The maximum of the occurrences ". Do you mean the maximum of the distribution of event amplitudes?

There are several similar sentences where I was not sure to understand what the authors mean.
l287 "As a drawback to this approach, a small number of event groups might be catalogued under a single energetic reference event even though the source mechanisms could be different."

Yes indeed, many processes produce a wide range of signal peak amplitudes (Gutenberg-Richter law).

I think that the event duration or frequency content is more useful to identify the source process.
l289 "It is possible that events in other locations of interest for cryoseismology have event types with substantially different seismic signatures than those of the WIS (on which our simulated waveform population was based). "

Yes, indeed. For instance, basal stick-slip events have duration ranging between 0.1s and 1000 s and frequencies between 0.01 Hz and 1000 Hz (see Podolskiy and Walter 2016 for a review).

Fig 6 "The multi-STA/LTA algorithm combines advantages of the other algorithms, as it is able to match, and improve upon, the detections achieved by RECmin"

But are all detections real events or is there a significant fraction of false detections (noise)?

Fig S3: The signal with a frequency of 0.01 Hz is consistent with stick-slip, but it could also be a teleseism
Fig S5: Why filtering the signal? At least the spectrogram should be shown for the raw unfiltered signal.
l303 "we have manually identified events of stick-slip origin": could you explain how you distinguished "stick-slip" events from other types of events?
l306 "The general trend between peak amplitude and duration (7, top) and energy and duration (7, bottom) of events is consistent with the positive linear association expected from cryogenic sources "

This is very general and true for many different source types. So it is not useful for classifying events.

l325 "The events of lower energies,..., that occur for long durations (bottom) suggest the presence of harmonic tremors in the catalogue."

This could be interesting! Could you show an exemple of seismic signal and spectrogram ? What could be the source (slow-slip event, water flow, storm ...)?
Section 4.2.1. The correlation with tides and temperature is interesting and is a good way to investigate the source mechanisms.

But it should be done after classifying events in different types, ie, removing known stick-slip and teleseisms.

Rather than amplitude, I think that frequency content (average frequency and width of the spectrum) and signal duration could be better parameters to discriminate source mechanisms.
Fig 7. You could show only plot (a) as it contains almost all information shown by the other plots.
l361: "Further, the production of near-comprehensive, reproducible event catalogues is a critical step towards standardized glacier monitoring as comparative studies between locations are enabled."

I agree that comprehensive and reproducible catalogs are valuable, but I think that standard methods (simple STA/LTA or template matching) can already produce such catalogs.

I think that each glacier is different and that all algorithms and parameters need to be adjusted for each case study.

I also think that using different methods may allow to detect events that are still unknown.

The main problem is not the detection of events, that can be easily automated and reproduced by others.

The classification of events is much more tricky and subjective, often done "by eye" without objective criteria.

I understand that this is the goal of your companion paper (l373) and I am very interested to see how a fully automatic machine learning method can perform.
l370 "The new catalogue will find utility in guiding conventional glacier seismology." Can you explain how?
L408 "We find a partial association of seismicity with the tidal cycle,". This is not a surprise since the catalog contain many already identified stick-slip events that are known to be driven by tides.
L12, L409 " We find a slight association with ice surface temperature, as an indicative example of one atmospheric observable.".

I don't see such a correlation when looking at Fig 9. This "association" should be quantified and tested using a statistical test.
L414 "semi-automated approach". When reaching the conclusion I still don't clearly understand which part of the detection method is not automatic?

Citation: https://doi.org/10.5194/egusphere-2023-1340-RC1
- AC1: 'Reply on RC1', Rebecca Latto, 08 Sep 2023
  
  The Author Comment is uploaded in the form of a supplement attached.
  
  Citation: https://doi.org/10.5194/egusphere-2023-1340-AC1
RC2:
'Comment on egusphere-2023-1340', Anonymous Referee #2, 07 Aug 2023

The authors present an exciting study that describes a “recognisance” algorithm for detecting seismicity from a range of seismic signals. The method is novel and the concept of a recognisance algorithm that is sensitive to a range of seismic signals will likely be of much use to the community.

However, although I would like to see this work published, I think the wording of the manuscript needs some work before it is ready to accept for publication. Firstly, the novelty of the algorithm needs to be clarified. This is probably a minor point, but at the moment the method appears to already be published (Turner et al., 2021), but the authors do not make this explicitly clear throughout the paper. I’d like to see the original methods paper properly acknowledged where the method is first introduced, and the tone of the paper changed to reflect that the method is applied to cryoseismology here rather than introduced as a new method. Secondly, I remain to be convinced by the concept that such a deliberately broad method can outperform a more specific method suited to one task (e.g. basal icequake detection). Perhaps it can, but I see no evidence in the paper to back up the claim made in the conclusions that the method presented can provide a “near-comprehensive event catalogue”.

To conclude, I think this work will be a valuable contribution to the field and I don’t find any issues with the results themselves. However, the text needs to be somewhat revised to tone down the claims made. Assuming the authors are happy to do this, then I would be very happy to see this paper published.

Comments:
The introduction does seem to be written in rather a bold way (e.g. “Since STA/LTA and correlation-type algorithms have enjoyed only limited success when applied to environmental seismology”). Both STA-LTA and cross-correlation methods are effective when used in certain ways, as the authors elude to and indeed that is the premise of this paper. I’d suggest toning down the limitations of these algorithms a little, since actually there has been much success in using these algorithms carefully, as part of broader methods. Indeed, I cannot think of any passive seismology method (other than manual detection) that is not at least somewhat built upon one of these two foundations. I think it would be useful to outline the scope of the study from the very beginning of the introduction, to clearly clarify the scope of the work to the reader before making the perhaps bolder claims.

From the introductory text, I was expecting Section 2.2 to be its own section. However, I can see the logic behind having it as a subsection of the overall algorithm method. Regardless of the structure, I think it would be useful to provide more details on the analysis of the algorithm testing results presented in Fig. 2,3. There is a lot of information held in those two figures, but they are not yet adequately described in the text. In other words, I was excited to read that the algorithm would first be tested on synthetic data, but was then left a little disappointed that the key findings are buried in the figures without much explanation of what they show.

L183-187: Perhaps it is common to only use the vertical component. However, best practice for any body wave data should be to use both vertical and horizontal components. However, mixing the three components via the Eucllidean norm might cause one to loose information about whether a phase is a P or S phase. While information loss is not inherently a problem for detecting events, incorrectly identifying S-wave phase arrivals as P-waves would result in false detections, since they are likely from the same event. I’m not suggesting the authors should revisit this component of their method, but I think they should be clear about the limitations, especially the possibility of false triggering. I’d suggest it would be beneficial to also discuss somewhere how their method might be developed further to act on the vertical and horizontal phases separately. If the authors would like any pointers to literature describing how to use the vertical and horizontal component information for P and S wave association in detail, including how a firn layer can affect such results, then this paper and references therein provide further details (https://doi.org/10.5194/egusphere-2023-657 ). (No requirement to cite this work, just a potentially useful paper that covers the point raised).

L189-190: Some clarification on the novelty of the method is required. Until this point in the paper, I was under the impression that the authors were presenting a new algorithm for event detection. However, from a glance at Turner et al. (2021), it looks like the value of this work is more in showing how the cited algorithm is implemented? Could this be clarified, and if the implementation is originally from Turner et al. (2021), then that paper be clearly referred to in Section 2.

L200-202: Perhaps too technical a question, but more for my interest: Are the signals instrument-response corrected before being passed through the algorithm? If not, then “energy” might be better referred to as “an approximation of the energy”, since different frequencies might exhibit different amplitude responses dictated by the instrument transfer function (response). Normally I wouldn’t raise this point, but since the authors are attempting to describe as broad an event detection algorithm as possible, frequency response could become important in certain instances. Maybe at least worth making readers aware of this point.

Figure 5: It would be nice to see more detail in each of the time-series. I cannot dicern any differences from the plots in this figure. Maybe for each, the authors could include an inset figure zooming in on any differences in the first arrival, perhaps plotting all three signals over one another? Otherwise I’m left questioning what difference the multi-STA/LTA algorithm makes. In summary, I imagine it is better, but the figure does not currently communicate that.

L276-277: Great aim. However, this sentence is very much left hanging. I’d like to see some text introducing how the authors will justify how the work presented here meets that aim in the remaining discussion.

Section 4.1: The biggest limitation of this work to meet its aim is that such a broad search algorithm does likely not perform as well for certain event detection scenarios, compared to more specific methods. For example, a coalescence/stacking based migration algorithm will, by design, be better for filtering out false triggers caused by noise. Therefore, taking basal icequakes as an example, the authors method is unlikely to outperform a migration method. This does not detract from the method presented by the authors, since a general recognisance method is definitely very useful in many contexts. However, it is definitely a limitation that needs to be mentioned, because it really does present a barrier for “a consistent approach to the generation … of event catalogues” (L276-277).

L411-412: This statement is definitely too bold. I am not convinced that one can ever develop algorithms that produce “near-comprehensive” event catalogues. However, more specifically, a general recognisance algorithm, which this is presented as, is unlikely to outperform a specific algorithm for a specific purpose. The algorithm presented here is definitely a valuable contribution to the field, but I see no evidence in the manuscript that it can produce “near-comprehensive” catalogues of seismicity.

Minor comments:
There should be spaces between units (e.g ma^-1 should be m a^-1, otherwise it is technically milli years).

L19: “exceptional” – rather emotive language. Consider removing.

L23-24: I think the definitions of the two event detection types are a little narrow. I’d view STA/LTA algorithms as just one subclass of any algorithm that searches for a peak in energy with particular frequency content (related to the length of the STA window). These can then be used to detect phase-arrivals, or be used in combination with more sophisticated phase associators, or coalescence-based algorithms to improve detection. Perhaps worth somehow very briefly mentioning this (as an STA/LTA algorithm on its own is not very useful/causes lots of false triggers).

L23-24: Further to the point above, one could consider array-based methods as a common earthquake detection type too? Not sure if this is worth mentioning, but picking phase arrivals on individual channels is only one class of earthquake detection. If the authors do decide to mention this, then examples of some relevant papers are included in the introduction to this paper: https://doi.org/10.5194/egusphere-2023-657. (sorry to refer to this paper again – just easiest way to point to the relevant literature cited within it).

L43-44: I’d say that cross-correlation algorithms are inherently also similarly prone to missed detections since they are typically based on the assumption that similar events occur within a catalogue.

L72: Is QuakeMigrate a spectral-based method?

L122: “event” – would it be perhaps better to refer to it as an “event phase arrival”?

Figure 1: Isnt the data in Figure 1b also plotted on Figure 1c? Not sure this section is therefore required. Similarly, Figure 1a could also be removed as all the information is contained in (d).

L170: The minimum distance rather than the maximum distance is probably the more relevant number.

L199: That reference is not to software documentation, but to a paper describing the software. Pointing to any software should really be done via a software repository DOI in the Acknowledgements. However, I think the authors are actually refering to the paper here, and so should simply remove the word “documentation”.

L236-237: I think this sentence doesn’t make sense with the word “event” at the end. Apologies if I misread, but would it be possible to reword if indeed it doesn’t make sense?

Figure 5: Add subplot labels, then refer to accordingly in the text (rather than left, right etc). That way the UTC time stamps can be remove from the text. Also, the caption is too long. Consider shortening.

Citation: https://doi.org/10.5194/egusphere-2023-1340-RC2
- AC2: 'Reply on RC2', Rebecca Latto, 08 Sep 2023
  
  The Author Comment was uploaded in the form of a supplement attached.
  
  Citation: https://doi.org/10.5194/egusphere-2023-1340-AC2

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2023-1340', Anonymous Referee #1, 07 Aug 2023

This manuscript present a new algorithm to detect seismic events. The method is based on the classical sta/lta detection method.

It runs the algorithm using a wide range of time windows (tSTA and tLTA) to build a hybrid detection function.

This method allows to detect a wide variety of seismic signals covering a huge range of signal durations (0.1 - 10000 s).

The method is applied to a dataset from the Whillans Ice Stream. However, this manuscript does not provide new information on the source of these seismic signals.

Most possible "stick-slip" events are already known (Pratt et al, 2014) and their correlation with tides has already been extensively discussed.
I am rather disappointed by this paper.

First, it is purely methodological with no result on glacial processes.

The method could also be applied in many fields of seismology (landslides, volcanoes, ..) that also exhibit a large variety of seismic signals.

It could thus better fit in a journal on seismology.
Second, I am not fully convinced by the advantages of the method compared to other methods.

The main advantage is to automatize what many researchers do by trial and errors.

Many researchers adapt the time windows of the STA/LTA methods (Short and Long Time Average of seismic energy) in order to detect most events

that can be observed by eye when looking at seismograms and spectrograms, while simultanouesly decreasing the rate of "false detections",

ie, anthropogenic and environnemental noise, teleseisms or other types of events different from what they look for.

But I think that this first step of looking at seismic data (on a small subset of the dataset) is essential to discover different types of events and to select signal of interest.
Also, the method only considers two parameters (tSTA and tLTA) but does not discuss the other parameters: the minimum ratio of short and long-term energy used to define an event and the frequency band.

I guess the authors do not filter the data, while it could be an efficient way to remove noise events and to detect weaker events by selecting the frequency of interest and where the signal/noise ratio is largest.
I feel that the method allows to detect more events but most of these events are maybe noise, such increasing the work of event classification, which is the most difficult task.

I believe that a simpler STA/LTA method with well chosen parameter could be almost as efficient than the proposed multi-STA/LTA algorithm, while reducing the number of "false detections".

At least, the authors need to demonstrate that their method detects more events but without increasing the fraction of false detections.

The multi-STA/LTA algorithm is compared with the standard STA/LTA method, but only for the two extreme models (very short or very long time windows, l232).

Why not using all models or the average model?
The manuscript is often hard to read and understand, many points should be clarified (see minor points below).
The manuscript stops when things could start being really interesting.

What are the newly discovered "stick-slip" events? Why were they not detected before? Are they weaker than the others or do they have a different waveform? Could you try to locate these new events?

What are the "tremor-like" signals mentionnent on l326?

Details and minor points:

Figure 1. Plots (a) and (b) could be removed, all information is also on the other subplots.
Algoritm description, section 2.1.

The classical recursive STA/LTA algorithm should be described (even it is described in the cited references) as it is the base of the multiSTA/LTA method.
l118. What are the values of the minimum STA/LTA thresholds (trigger and detrigger) used to define an event?

How are they chosen? Why not optimizing these parameters as done for the time windows?

Fig 1 suggests the threshold is fixed at 3 and is the same to trigger an detrigger an event. Did you try other values?

I don't understand eq. (1) and point (3) (l116). Is the hybrid function the average (as in eq(1)) or the maximum value (l116) of all single-parameters STA/LTA functions?

l133. I don't understand what represents epsilon?

Section 3.2 should be moved to the "method" section 2. It describes how the catalogue is compiled and is not specific to the Whillans Ice Stream catalog.

l194: I don't understand this sentence: " The reference time precedes this arrival time by half the network time for the N closest seismometers."

What is the "network time"? What is the value of N?

l195 "We also take into account that multi-STA/LTA will decompose events into smaller events based on amplitude variations."

I think this is a drawback of the method; and a simpler STA/LTA method with well chosen parameters and with a detrigger threshold lower than the trigger threshold may avoid this problem.

You should describe in more details how you merge "overlapping events" into one event to obtain the correct event duration.
l213 "Taking uncertainties in start time into account, we label 140 events as stick-slip. Four of those events are determined as additional to the Pratt et al. (2014) catalogue from a manual reconnaissance."

How do you know that these 4 events are "stick-slip"? Could you show examples of a newly detected "stick-slip" event and a known stick-slip event?

l219 "What means "a.u.": arbitrary unit?
l267 "The distribution of the peak amplitude occurrences provides source mechanism information".

Could you specify which "source mechanism information"? Do you mean the Gutenberg-Richer b-value?
l268 "However, in cryoseismology, the actual magnitude cannot be determined because the material strength, slip distance, and area of slipped fault are usually indiscernible."

I don't think this is a big issue and a major source of uncertainty on magnitudes. The problem is even worse for earthquakes that occur at depth were material properties are less constrained.
l271 "The maximum of the occurrences ". Do you mean the maximum of the distribution of event amplitudes?

There are several similar sentences where I was not sure to understand what the authors mean.
l287 "As a drawback to this approach, a small number of event groups might be catalogued under a single energetic reference event even though the source mechanisms could be different."

Yes indeed, many processes produce a wide range of signal peak amplitudes (Gutenberg-Richter law).

I think that the event duration or frequency content is more useful to identify the source process.
l289 "It is possible that events in other locations of interest for cryoseismology have event types with substantially different seismic signatures than those of the WIS (on which our simulated waveform population was based). "

Yes, indeed. For instance, basal stick-slip events have duration ranging between 0.1s and 1000 s and frequencies between 0.01 Hz and 1000 Hz (see Podolskiy and Walter 2016 for a review).

Fig 6 "The multi-STA/LTA algorithm combines advantages of the other algorithms, as it is able to match, and improve upon, the detections achieved by RECmin"

But are all detections real events or is there a significant fraction of false detections (noise)?

Fig S3: The signal with a frequency of 0.01 Hz is consistent with stick-slip, but it could also be a teleseism
Fig S5: Why filtering the signal? At least the spectrogram should be shown for the raw unfiltered signal.
l303 "we have manually identified events of stick-slip origin": could you explain how you distinguished "stick-slip" events from other types of events?
l306 "The general trend between peak amplitude and duration (7, top) and energy and duration (7, bottom) of events is consistent with the positive linear association expected from cryogenic sources "

This is very general and true for many different source types. So it is not useful for classifying events.

l325 "The events of lower energies,..., that occur for long durations (bottom) suggest the presence of harmonic tremors in the catalogue."

This could be interesting! Could you show an exemple of seismic signal and spectrogram ? What could be the source (slow-slip event, water flow, storm ...)?
Section 4.2.1. The correlation with tides and temperature is interesting and is a good way to investigate the source mechanisms.

But it should be done after classifying events in different types, ie, removing known stick-slip and teleseisms.

Rather than amplitude, I think that frequency content (average frequency and width of the spectrum) and signal duration could be better parameters to discriminate source mechanisms.
Fig 7. You could show only plot (a) as it contains almost all information shown by the other plots.
l361: "Further, the production of near-comprehensive, reproducible event catalogues is a critical step towards standardized glacier monitoring as comparative studies between locations are enabled."

I agree that comprehensive and reproducible catalogs are valuable, but I think that standard methods (simple STA/LTA or template matching) can already produce such catalogs.

I think that each glacier is different and that all algorithms and parameters need to be adjusted for each case study.

I also think that using different methods may allow to detect events that are still unknown.

The main problem is not the detection of events, that can be easily automated and reproduced by others.

The classification of events is much more tricky and subjective, often done "by eye" without objective criteria.

I understand that this is the goal of your companion paper (l373) and I am very interested to see how a fully automatic machine learning method can perform.
l370 "The new catalogue will find utility in guiding conventional glacier seismology." Can you explain how?
L408 "We find a partial association of seismicity with the tidal cycle,". This is not a surprise since the catalog contain many already identified stick-slip events that are known to be driven by tides.
L12, L409 " We find a slight association with ice surface temperature, as an indicative example of one atmospheric observable.".

I don't see such a correlation when looking at Fig 9. This "association" should be quantified and tested using a statistical test.
L414 "semi-automated approach". When reaching the conclusion I still don't clearly understand which part of the detection method is not automatic?

Citation: https://doi.org/10.5194/egusphere-2023-1340-RC1
- AC1: 'Reply on RC1', Rebecca Latto, 08 Sep 2023
  
  The Author Comment is uploaded in the form of a supplement attached.
  
  Citation: https://doi.org/10.5194/egusphere-2023-1340-AC1
RC2:
'Comment on egusphere-2023-1340', Anonymous Referee #2, 07 Aug 2023

The authors present an exciting study that describes a “recognisance” algorithm for detecting seismicity from a range of seismic signals. The method is novel and the concept of a recognisance algorithm that is sensitive to a range of seismic signals will likely be of much use to the community.

However, although I would like to see this work published, I think the wording of the manuscript needs some work before it is ready to accept for publication. Firstly, the novelty of the algorithm needs to be clarified. This is probably a minor point, but at the moment the method appears to already be published (Turner et al., 2021), but the authors do not make this explicitly clear throughout the paper. I’d like to see the original methods paper properly acknowledged where the method is first introduced, and the tone of the paper changed to reflect that the method is applied to cryoseismology here rather than introduced as a new method. Secondly, I remain to be convinced by the concept that such a deliberately broad method can outperform a more specific method suited to one task (e.g. basal icequake detection). Perhaps it can, but I see no evidence in the paper to back up the claim made in the conclusions that the method presented can provide a “near-comprehensive event catalogue”.

To conclude, I think this work will be a valuable contribution to the field and I don’t find any issues with the results themselves. However, the text needs to be somewhat revised to tone down the claims made. Assuming the authors are happy to do this, then I would be very happy to see this paper published.

Comments:
The introduction does seem to be written in rather a bold way (e.g. “Since STA/LTA and correlation-type algorithms have enjoyed only limited success when applied to environmental seismology”). Both STA-LTA and cross-correlation methods are effective when used in certain ways, as the authors elude to and indeed that is the premise of this paper. I’d suggest toning down the limitations of these algorithms a little, since actually there has been much success in using these algorithms carefully, as part of broader methods. Indeed, I cannot think of any passive seismology method (other than manual detection) that is not at least somewhat built upon one of these two foundations. I think it would be useful to outline the scope of the study from the very beginning of the introduction, to clearly clarify the scope of the work to the reader before making the perhaps bolder claims.

From the introductory text, I was expecting Section 2.2 to be its own section. However, I can see the logic behind having it as a subsection of the overall algorithm method. Regardless of the structure, I think it would be useful to provide more details on the analysis of the algorithm testing results presented in Fig. 2,3. There is a lot of information held in those two figures, but they are not yet adequately described in the text. In other words, I was excited to read that the algorithm would first be tested on synthetic data, but was then left a little disappointed that the key findings are buried in the figures without much explanation of what they show.

L183-187: Perhaps it is common to only use the vertical component. However, best practice for any body wave data should be to use both vertical and horizontal components. However, mixing the three components via the Eucllidean norm might cause one to loose information about whether a phase is a P or S phase. While information loss is not inherently a problem for detecting events, incorrectly identifying S-wave phase arrivals as P-waves would result in false detections, since they are likely from the same event. I’m not suggesting the authors should revisit this component of their method, but I think they should be clear about the limitations, especially the possibility of false triggering. I’d suggest it would be beneficial to also discuss somewhere how their method might be developed further to act on the vertical and horizontal phases separately. If the authors would like any pointers to literature describing how to use the vertical and horizontal component information for P and S wave association in detail, including how a firn layer can affect such results, then this paper and references therein provide further details (https://doi.org/10.5194/egusphere-2023-657 ). (No requirement to cite this work, just a potentially useful paper that covers the point raised).

L189-190: Some clarification on the novelty of the method is required. Until this point in the paper, I was under the impression that the authors were presenting a new algorithm for event detection. However, from a glance at Turner et al. (2021), it looks like the value of this work is more in showing how the cited algorithm is implemented? Could this be clarified, and if the implementation is originally from Turner et al. (2021), then that paper be clearly referred to in Section 2.

L200-202: Perhaps too technical a question, but more for my interest: Are the signals instrument-response corrected before being passed through the algorithm? If not, then “energy” might be better referred to as “an approximation of the energy”, since different frequencies might exhibit different amplitude responses dictated by the instrument transfer function (response). Normally I wouldn’t raise this point, but since the authors are attempting to describe as broad an event detection algorithm as possible, frequency response could become important in certain instances. Maybe at least worth making readers aware of this point.

Figure 5: It would be nice to see more detail in each of the time-series. I cannot dicern any differences from the plots in this figure. Maybe for each, the authors could include an inset figure zooming in on any differences in the first arrival, perhaps plotting all three signals over one another? Otherwise I’m left questioning what difference the multi-STA/LTA algorithm makes. In summary, I imagine it is better, but the figure does not currently communicate that.

L276-277: Great aim. However, this sentence is very much left hanging. I’d like to see some text introducing how the authors will justify how the work presented here meets that aim in the remaining discussion.

Section 4.1: The biggest limitation of this work to meet its aim is that such a broad search algorithm does likely not perform as well for certain event detection scenarios, compared to more specific methods. For example, a coalescence/stacking based migration algorithm will, by design, be better for filtering out false triggers caused by noise. Therefore, taking basal icequakes as an example, the authors method is unlikely to outperform a migration method. This does not detract from the method presented by the authors, since a general recognisance method is definitely very useful in many contexts. However, it is definitely a limitation that needs to be mentioned, because it really does present a barrier for “a consistent approach to the generation … of event catalogues” (L276-277).

L411-412: This statement is definitely too bold. I am not convinced that one can ever develop algorithms that produce “near-comprehensive” event catalogues. However, more specifically, a general recognisance algorithm, which this is presented as, is unlikely to outperform a specific algorithm for a specific purpose. The algorithm presented here is definitely a valuable contribution to the field, but I see no evidence in the manuscript that it can produce “near-comprehensive” catalogues of seismicity.

Minor comments:
There should be spaces between units (e.g ma^-1 should be m a^-1, otherwise it is technically milli years).

L19: “exceptional” – rather emotive language. Consider removing.

L23-24: I think the definitions of the two event detection types are a little narrow. I’d view STA/LTA algorithms as just one subclass of any algorithm that searches for a peak in energy with particular frequency content (related to the length of the STA window). These can then be used to detect phase-arrivals, or be used in combination with more sophisticated phase associators, or coalescence-based algorithms to improve detection. Perhaps worth somehow very briefly mentioning this (as an STA/LTA algorithm on its own is not very useful/causes lots of false triggers).

L23-24: Further to the point above, one could consider array-based methods as a common earthquake detection type too? Not sure if this is worth mentioning, but picking phase arrivals on individual channels is only one class of earthquake detection. If the authors do decide to mention this, then examples of some relevant papers are included in the introduction to this paper: https://doi.org/10.5194/egusphere-2023-657. (sorry to refer to this paper again – just easiest way to point to the relevant literature cited within it).

L43-44: I’d say that cross-correlation algorithms are inherently also similarly prone to missed detections since they are typically based on the assumption that similar events occur within a catalogue.

L72: Is QuakeMigrate a spectral-based method?

L122: “event” – would it be perhaps better to refer to it as an “event phase arrival”?

Figure 1: Isnt the data in Figure 1b also plotted on Figure 1c? Not sure this section is therefore required. Similarly, Figure 1a could also be removed as all the information is contained in (d).

L170: The minimum distance rather than the maximum distance is probably the more relevant number.

L199: That reference is not to software documentation, but to a paper describing the software. Pointing to any software should really be done via a software repository DOI in the Acknowledgements. However, I think the authors are actually refering to the paper here, and so should simply remove the word “documentation”.

L236-237: I think this sentence doesn’t make sense with the word “event” at the end. Apologies if I misread, but would it be possible to reword if indeed it doesn’t make sense?

Figure 5: Add subplot labels, then refer to accordingly in the text (rather than left, right etc). That way the UTC time stamps can be remove from the text. Also, the caption is too long. Consider shortening.

Citation: https://doi.org/10.5194/egusphere-2023-1340-RC2
- AC2: 'Reply on RC2', Rebecca Latto, 08 Sep 2023
  
  The Author Comment was uploaded in the form of a supplement attached.
  
  Citation: https://doi.org/10.5194/egusphere-2023-1340-AC2

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload

ED: Reconsider after major revisions (further review by editor and referees) (20 Sep 2023) by Evgeny A. Podolskiy

Dear Rebecca Latto,

Thank you very much for providing responses to the reports by the two reviewers.

As you already know, Reviewer #2 was primarily concerned with the language and toning down/clarifying the claims regarding the originality, novelty, and universality of your approach, whereas Reviewer #1 was more skeptical. Specifically, their key points of criticism were a lack of new insights into the source mechanisms, an unconvincing advantage of the method compared to others, an assumption that simpler STA/LTA might be equally efficient, and unclear writing.

Considering that mixed reviewers’ opinions are convincingly counterbalanced by your careful responses indicating ways to provide an improved version of the manuscript, I invite you to provide a revised version, which will address the remarks by the reviewers according to your rebuttal letters. As we receive your updated package, it will be forwarded to the same two reviewers for a second round of reviews.

I understand revisions may take time, especially when you have the second companion paper. Therefore, please do not hesitate to contact us if you need more time for all this work.

P.S.

(1) In regard to possible redundancies in the subplots of Fig. 1 indicated by both Reviewers. I think there is no problem now since the figure introduces the reader to the basics step-by-step. The point is not to show “all information” concisely but to show a recipe for getting from raw data to detection.

(2) I could not follow one of your replies to Reviewer #1 (“Our experience is that varying the parameters mentioned results in a full catalogue.”), and perhaps it could be clarified.

Best regards,
Evgeny Podolskiy

Hide

AR by Rebecca Latto on behalf of the Authors (19 Nov 2023) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (22 Nov 2023) by Evgeny A. Podolskiy

RR by Anonymous Referee #2 (22 Nov 2023)

RR by Anonymous Referee #1 (04 Dec 2023)

Suggestions for revision or reasons for rejection

The authors did not really address my previous comments apart from minor technical points.
They usually answered by referring to "part B", like in the manuscript, which looks like an advertisement for their next paper.
The tone of their manuscript and response letter is slightly pretentious and patronizing.
It's not that I did not understand their goal, just I don't think this study is very interresting and innovative.

For instance, the authors claimed that "Our experience is that varying the parameters mentioned results in a full catalogue"
How do you know that you detected all events? Just because you detected all previously known "stick-slip"events?
But there could be smaller stick-slip events that you missed, or other types of events. How to be sure? What is an "event" anyway?

Their goal is to bust a "catch-all" catalog, but there are better methods for this.
The multi STA/LTA method only detect impulsive events and cannot detect tremor, which occurs frequently on glaciers.
Tremor and all event types can be simultaneously detected and located by migration based or beam-forming methods (eg, Nanni et al GRL 2022).
The multi STA/LTA method is also not better than other methods at detecting small events, template-matching methods can detect much smaller signals.

The method is not new, it has already been published (Turner et al 2021). This work is only an application to a special case study.
But the interpretation of the results is left for a future paper.

I still disagree that the proposed method outperforms a simple STA/LTA algorithm.
Indeed, the authors have not shown that their method is better than "any other simple STA/LTA method", just that it is better that a simple STA/lTA algorithm with extreme unrealistic values of short and long time windows.

Moreover, for known stick-slip events, the method is not able to detect the correct start and end times (see response letter and electronic supplement).

The main advantage of the method is that it searches automatically for the optimal values of some model parameters (time windows), but some other important parameters (triggering and detriggering thresholds) are adjusted by trial an error. The proposed method thus does not remove the need to visually optimize some model parameters.

Figure S2 is supposed to show a high confidence stick-slip event not previously identified.
What represent the pink areas? The time window when the event is detected at each station?
I don't see any event when looking at these seismograms. I can't believe that there is an increase in energy ratio SA/LTA larger than the chosen threshold of 3.
Or the event may be in a different frequency range than the bandpass filter 0.001-1Hz used in fig S2?
Could you show the spectrogram of this event at one or more stations? And add a plot to show the temporal evolution of STA, LTA and STA/LTA?

This event is identified as stick-slip just because it shows 3 successive pulses (electronic supplement).
This is not very convincing. It could also correspond to 3 successive events.
It also has a different frequency range than known stick slips events.
You should at least locate this event before making a claim like that. Or show that this event repeats at regular time intervals.

Again, I understand why you filter seismograms, but why spectrograms (fig S5)?

The authors do not filter the signal or deconvolve the data by the instrument response because it leads to "artificial detections"
I have never seen that. What kind of signals did you detect? Are these real signals (ambient noise...) or numerical problems?

Hide

ED: Reconsider after major revisions (further review by editor and referees) (22 Dec 2023) by Evgeny A. Podolskiy

Dear Rebecca Latto,

Thank you again for submitting your revised manuscript entitled "Towards the systematic reconnaissance of seismic signals from glaciers and ice sheets - Part A: Event detection for cryoseismology”.

Your manuscript has now been seen by 2 referees. While Referee #2 was fully satisfied by your revisions and replies to the other referee and recommended this work for publication as is, some important points were raised by Referee #1 (please see below). In brief, Referee #1 did not find your previous replies satisfactory, questioned the innovative nature of the paper, and was not convinced by the demonstration of the advantage of your approach versus other algorithms. The Referee also raises additional specific points that need to be addressed for the paper to be acceptable for publication.

We are interested in the possibility of publishing your paper in The Cryosphere, but we would like to consider your response to these concerns in the form of a revised manuscript before we make a final decision on publication. We, therefore, invite you to resubmit your manuscript, which would address the points raised or explain if there are specific requests from the Referee that you believe are technically impossible or unlikely to yield a meaningful outcome.

Please do not hesitate to contact us if you have any questions. We look forward to seeing the revised paper and thank you for the opportunity to review your work.

Best regards,
Evgeny Podolskiy
Editor, The Cryosphere

***
Referee#1’s comments:

The authors did not really address my previous comments apart from minor technical points.
They usually answered by referring to "part B", like in the manuscript, which looks like an advertisement for their next paper.
The tone of their manuscript and response letter is slightly pretentious and patronizing.
It's not that I did not understand their goal, just I don't think this study is very interesting and innovative.

For instance, the authors claimed that "Our experience is that varying the parameters mentioned results in a full catalogue"
How do you know that you detected all events? Just because you detected all previously known "stick-slip"events?
But there could be smaller stick-slip events that you missed, or other types of events. How to be sure? What is an "event" anyway?

Their goal is to bust a "catch-all" catalog, but there are better methods for this.
The multi STA/LTA method only detect impulsive events and cannot detect tremor, which occurs frequently on glaciers.
Tremor and all event types can be simultaneously detected and located by migration based or beam-forming methods (eg, Nanni et al GRL 2022).
The multi STA/LTA method is also not better than other methods at detecting small events, template-matching methods can detect much smaller signals.

The method is not new, it has already been published (Turner et al 2021). This work is only an application to a special case study.
But the interpretation of the results is left for a future paper.

I still disagree that the proposed method outperforms a simple STA/LTA algorithm.
Indeed, the authors have not shown that their method is better than "any other simple STA/LTA method", just that it is better that a simple STA/lTA algorithm with extreme unrealistic values of short and long time windows.

Moreover, for known stick-slip events, the method is not able to detect the correct start and end times (see response letter and electronic supplement).

The main advantage of the method is that it searches automatically for the optimal values of some model parameters (time windows), but some other important parameters (triggering and detriggering thresholds) are adjusted by trial an error. The proposed method thus does not remove the need to visually optimize some model parameters.

Figure S2 is supposed to show a high confidence stick-slip event not previously identified.
What represent the pink areas? The time window when the event is detected at each station?
I don't see any event when looking at these seismograms. I can't believe that there is an increase in energy ratio SA/LTA larger than the chosen threshold of 3.
Or the event may be in a different frequency range than the bandpass filter 0.001-1Hz used in fig S2?
Could you show the spectrogram of this event at one or more stations? And add a plot to show the temporal evolution of STA, LTA and STA/LTA?

This event is identified as stick-slip just because it shows 3 successive pulses (electronic supplement).
This is not very convincing. It could also correspond to 3 successive events.
It also has a different frequency range than known stick slips events.
You should at least locate this event before making a claim like that. Or show that this event repeats at regular time intervals.

Again, I understand why you filter seismograms, but why spectrograms (fig S5)?

The authors do not filter the signal or deconvolve the data by the instrument response because it leads to "artificial detections"
I have never seen that. What kind of signals did you detect? Are these real signals (ambient noise...) or numerical problems?

Hide

AR by Rebecca Latto on behalf of the Authors (26 Feb 2024) Author's response Author's tracked changes Manuscript

ED: Publish subject to technical corrections (18 Mar 2024) by Evgeny A. Podolskiy

AR by Rebecca Latto on behalf of the Authors (21 Mar 2024) Manuscript

Journal article(s) based on this preprint

30 Apr 2024

Towards the systematic reconnaissance of seismic signals from glaciers and ice sheets – Part 1: Event detection for cryoseismology

Rebecca B. Latto, Ross J. Turner, Anya M. Reading, and J. Paul Winberry

The Cryosphere, 18, 2061–2079, https://doi.org/10.5194/tc-18-2061-2024,https://doi.org/10.5194/tc-18-2061-2024, 2024

Short summary

Rebecca B. Latto, Ross J. Turner, Anya M. Reading, and J. Paul Winberry

Supplement

https://doi.org/10.5194/egusphere-2023-1340-supplement

Data sets

Electronic Supplement Rebecca B. Latto and Ross J. Turner and Anya M. Reading https://github.com/beccalatto/multi_sta_lta

Model code and software

An ObsPy library for event detection and seismic attribute calculation: preparing waveforms for automated analysis Ross J. Turner and Rebecca B. Latto and Anya M. Reading http://doi.org/10.5334/jors.365

Rebecca B. Latto, Ross J. Turner, Anya M. Reading, and J. Paul Winberry

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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.

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Short summary

The study of icequakes allows for investigation of many glacier processes that are unseen by typical reconnaissance methods. However, detection of such seismic signals is challenging because of low signal-to-noise levels and diverse source mechanisms. Here, we present a novel algorithm that is optimized to detect signals from a glacier environment. We apply the algorithm to seismic data recorded in the 2010–2011 austral summer from Whillans Ice Stream then evaluate the resulting event catalogue.


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