Tsunami detection methods for Ocean-Bottom Pressure Gauges

Angeli, Cesare; Armigliato, Alberto; Zanetti, Martina; Zaniboni, Filippo; Romano, Fabrizio; Bayraktar, Hafize Başak; Lorito, Stefano

doi:https://doi.org/10.5194/nhess-2024-113

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https://doi.org/10.5194/nhess-2024-113

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24 Jun 2024

| 24 Jun 2024

Status: a revised version of this preprint is currently under review for the journal NHESS.

Tsunami detection methods for Ocean-Bottom Pressure Gauges

Cesare Angeli, Alberto Armigliato, Martina Zanetti, Filippo Zaniboni, Fabrizio Romano, Hafize Başak Bayraktar, and Stefano Lorito

Abstract. Real-time detection of tsunami waves is a fundamental part of tsunami early warning and alert systems. Several algorithms have been proposed in the literature for that. Three of them and a newly developed one, based on the Fast Iterative Filtering technique, are applied here to a large number of records from the DART monitoring network in the Pacific Ocean. The techniques are compared in terms of earthquake and tsunami event-detection capabilities and statistical properties of the detection curves. The classical Mofjeld's algorithm is very efficient in detecting seismic waves and tsunamis, but it does not always characterize the tsunami waveform correctly. Other techniques, based on Empirical Orthogonal Functions and cascade of filters respectively, show better results in wave characterization but they usually have larger residual than Mofjeld's. The FIF-based detection method shows promising results in terms of detection rates of tsunami events, filtering of seismic waves and characterization of wave amplitude and period. The technique is a good candidate for monitoring networks and in data assimilation applications for realtime tsunami forecasts.

Received: 12 Jun 2024 – Discussion started: 24 Jun 2024

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Cesare Angeli, Alberto Armigliato, Martina Zanetti, Filippo Zaniboni, Fabrizio Romano, Hafize Başak Bayraktar, and Stefano Lorito

Status: final response (author comments only)

RC1:
'Comment on nhess-2024-113', Anonymous Referee #1, 14 Jul 2024
This study compares several algorithms for real-time detection of seismic and tsunami events using data from the DART monitoring network in the Pacific Ocean. The newly developed method based on Fast Iterative Filtering shows promising results in tsunami event detection rates and waveform characterization, making it suitable for real-time tsunami forecasting and data assimilation applications. I suggest a minor revision.
In the Introduction section, it is unnecessary to elaborate extensively on tide gauges because they do not have much significance in tsunami detection. Instead, the focus should be on offshore observations. For instance, I suggest categorizing OBPG systems; those use cable for signal transmission, while others use sonar (DARTs). Additionally, the author could discuss GPS buoys.

In the application of real-time tsunami detection, the computational speed of algorithms is also crucial. I suggest the authors discuss the computational efficiency of these methods and whether they can rapidly produce results.

When DART records sea surface elevation data, significant deviations can sometimes occur. I would like the authors to discuss how to address such situations.

In Line 105, I disagree with this viewpoint; even within the same bay, due to tsunami resonance, long-period signals can exhibit significant differences at different locations.

Regarding FTF-based decomposition, what causes significant false signals to appear before the arrival of a tsunami (Figure 2c)? How can this be avoided?

In Line 274, how is the post-processed waveform obtained? Please provide a detailed explanation.

Other minor comments:
Figure 1: Please add the unit to y-axis

Line 190: Do you refer to “exclude detiding procedures”? Any typo?

Line 322: “much better then” -> “much better than”
Citation: https://doi.org/10.5194/nhess-2024-113-RC1
- AC1: 'Reply on RC1', Cesare Angeli, 31 Jul 2024
  
  We thank the anonymous referee for the interesting and insightful comments about our work. We would like to post a few comments on some of the points, in order to have better clarifications as long as the interactive discussion is available.
  1 In the Introduction section, it is unnecessary to elaborate extensively on tide gauges because they do not have much significance in tsunami detection. Instead, the focus should be on offshore observations. For instance, I suggest categorizing OBPG systems; those use cable for signal transmission, while others use sonar (DARTs). Additionally, the author could discuss GPS buoys.
  Although we do think that tide gauges can be important for detection, e.g. in the case of late arrivals or to alert further away coastal areas, they do not have any role in the present work, thus we will modify the text, following your suggestions
  
  2 In the application of real-time tsunami detection, the computational speed of algorithms is also crucial. I suggest the authors discuss the computational efficiency of these methods and whether they can rapidly produce results.
  All the algorithms are already fast enough to be used in real time, i.e. the computational costs for each time step is shorter than the sampling time of the instruments. Nonetheless, we understand the need for a more detailed analysis which we will add to the paper.
  
  3 When DART records sea surface elevation data, significant deviations can sometimes occur. I would like the authors to discuss how to address such situations.
  What is asked in this point is not clear to the authors. Thus, we kindly ask for further clarifications.
  
  4 In Line 105, I disagree with this viewpoint; even within the same bay, due to tsunami resonance, long-period signals can exhibit significant differences at different locations.
  This is in fact not a general statement, but it is experimentally valid for the EOF detiding as claimed by Tolkova (2009, 2010). We will specify more clearly the origin and scope of validity of the statement.
  
  5 Regarding FTF-based decomposition, what causes significant false signals to appear before the arrival of a tsunami (Figure 2c)? How can this be avoided?
  The appearances of this oscillations before the real disturbance in the decomposition is related to the loss of causality intrinsic in signal decomposition techniques. This is not exclusive to IMF decompositions, since it can be observed also in the case of classical Fourier trigonometric series and it can be observed in Fig. 18 and 19 of the work by Tolkova (2009).In the specific case, the presence of a "spike" at the end of the signal is the specific cause of these oscillations. By summingthe components within a not too narrow frequency band, their effect is cancelled out, as shown in Fig. 3c.
  
  6 In Line 274, how is the post-processed waveform obtained? Please provide a detailed explanation.
  The post-processed waveforms are taken directly from the material provided by Davies (2019). An explanation of how they are obtained will be added to the paper.
  
  Citation: https://doi.org/10.5194/nhess-2024-113-AC1
- AC4: 'Reply on RC1 - Final', Cesare Angeli, 13 Aug 2024
  
  We thank the anonymous referee for the interesting and insightful comments about our work.
  
  In the following, we would like to answer the comments and suggestions from the referee.
  
  With respect to the previous reply, here we addressed all points raised by the referee, while some minor ones were not addressed in the previous reply.
  1 In the Introduction section, it is unnecessary to elaborate extensively on tide gauges because they do not have much significance in tsunami detection. Instead, the focus should be on offshore observations. For instance, I suggest categorizing OBPG systems; those use cable for signal transmission, while others use sonar (DARTs). Additionally, the author could discuss GPS buoys.
  
  - Although we do think that tide gauges can be important for detection, e.g. in the case of late arrivals or to alert further away coastal areas, they do not have a specific role in the present work, thus we will modify the text, following your suggestions
  
  2 In the application of real-time tsunami detection, the computational speed of algorithms is also crucial. I suggest the authors discuss the computational efficiency of these methods and whether they can rapidly produce results.
  
  - All the algorithms are already fast enough to be used in real time, i.e. the computational costs for each time step is shorter than the sampling time of the instruments. Nonetheless, we understand the need for a more detailed analysis which we will add to the paper.
  
  3 When DART records sea surface elevation data, significant deviations can sometimes occur. I would like the authors to discuss how to address such situations.
  
  - The point is not clear to the authors. If the referee is referring to deviations of the pressure record from the shallow water
  
  regime, a topic which is gaining more attention after the 2022 Tonga tsunami, we point out that we do not address the problem.
  
  Our analysis is based on the application of the four techniques to detect anomalies in the pressure records, as if the technique
  
  was in real-time on the instrument. Each pressure value is expressed in meters through the hydrostatic equivalence for easier
  
  interpretability of the results. However, since the present paper has applications only to tsunamis of tectonic origin, we can
  
  assume the hydrostatic approximation to hold.
  
  Nonetheless, further clarification on the point would be appreciated.
  
  4 In Line 105, I disagree with this viewpoint; even within the same bay, due to tsunami resonance, long-period signals can exhibit significant differences at different locations.
  
  - This is in fact not a general statement, but it is experimentally valid for the EOF detiding as claimed by Tolkova (2009, 2010). We will specify more clearly the origin and scope of validity of the statement.
  
  5 Regarding FTF-based decomposition, what causes significant false signals to appear before the arrival of a tsunami (Figure 2c)? How can this be avoided?
  
  - The appearances of this oscillations before the real disturbance in the decomposition is related to the loss of causality intrinsic in signal decomposition techniques. This is not exclusive to IMF decompositions, since it can be observed also in the case of classical Fourier trigonometric series and it can be observed in Fig. 18 and 19 of the work by Tolkova (2009).In the specific case, the presence of a "spike" at the end of the signal is the specific cause of these oscillations. By summing the components within a not too narrow frequency band, their effect is cancelled out, as shown in Fig. 3c. We will add a comment in this regard in the revised manuscript.
  
  6 In Line 274, how is the post-processed waveform obtained? Please provide a detailed explanation.
  
  - The post-processed waveforms are taken directly from the material provided by Davies (2019). To remove the tidal component a LOWESS filter was applied. An explanation of how they are obtained will be added to the paper.
  
  Other minor comments:
  
  1 Figure 1: Please add the unit to y-axis
  
  - Being a basis set, the computations do not change if the vectors are multiplied by an arbitrary constant. For this reason, a unit of measurement is not strictly necessary. The same holds for the plot ranges.
  
  Nonetheless, we see that in the present state, the text and this figure may be disorienting. So the suggestions about the plot will be implemented and an explanation about the arbitrariness of amplitude of these vectors will be added to the text.
  2 Line 190: Do you refer to ‚“exclude detiding procedures”? Any typo?
  
  - Here, we refer to the tsunami detection tecniques described along the manuscript. We will rephrase for improved clarity.
  
  3 Line 322: ‚“much better then” -> “much better than”
  
  - The text will be corrected.
  
  Citation: https://doi.org/10.5194/nhess-2024-113-AC4
RC2:
'Comment on nhess-2024-113', Anonymous Referee #2, 15 Jul 2024
This work Tsunami detection methods for Ocean-Bottom Pressure Gauges by Angeli et al. (2024) offers some insights on the comparisons between the various algorithms used by the scientific community with respect to the DART sea level stations. However, the current version of the manuscript needs major revisions to alleviate some of the concerns I have.

Major Comments (in order of appearance in the manuscript):
Introduction should state clearly the motivation and scientific questions for why the work is being done.
In addition, the story put forth by the manuscript is on the DARTs. Tide gauges (sea level stations) should have a reduced role in the introduction.

Take more time to elaborate why the work is important.

The work will benefit greatly by comparing computational costs of each of the four algorithms. A robust method that takes up too much time defeats the purpose of real-time detection. The authors say the algorithms are efficient, but it should also be shown. This addition would make the paper more impactful.

Background noise should be done for the same time periods rather than at differing times. This leaves the DART stations open to misrepresented background values that may be influenced by meteorological and oceanographic phenomena. All 5 DARTs studied here have overlapping data at the same times with multiple tsunami detections in those times. Without accounting for these features, it makes the research less impactful, as it remains unclear if differing conditions favor different detection algorithms.
The manuscript should add the New Zealand DARTs, as they provide a good dataset for the Southern Pacific.

Minor Comments (in order of appearance):
Line 46-47: These are not review papers but proper studies. Refer to them as such.

Line 49: The proper citation for the DART network is Titov, V.V., F.I. González, E.N. Bernard, M.C. Eble, H.O. Mofjeld, J.C. Newman, and A.J. Venturato (2005): Real-time tsunami forecasting: Challenges and solutions. Hazards, 35(1), Special Issue, U.S. National Tsunami Hazard Mitigation Program, 41–58.

Cite DART data as National Oceanic and Atmospheric Administration (2005): Deep-Ocean Assessment and Reporting of Tsunamis (DART(R)). NOAA National Centers for Environmental Information. doi:10.7289/V5F18WNS [access date].

Line 72: Do not use DART for more than one acronym. It is confusing, and it muddies the narrative. Instead, call it MOF for Mofjeld (1997).

Line 86: Be consistent with use of “on board” or “on-board.” Most instances of the use of “on board” are incorrect.

Line 106: State the length of a lunar day in other time units (e.g., hours, minutes, or seconds).

Figure 1: Missing y-axis units. Also, make y-axis the same limit for all subplots.

Line 144: Fix the units in the brackets.

Line 175: Why do you choose this range of periods?

Line 196: Colloquial speak — replace with a different, appropriate modifier.

Line 200: Replace “taller” with larger.

Line 206: What does it mean to be a “long” deployment?

Figure 7: This figure needs a long caption to explain all of what is going on in it. What is the x-axis? Tidal coefficients? If so, mention it! Do not assume everyone knows what they are. Also, choose a better colorblind friendly color scheme. I am lost as to what is being plotted here because they all look the same.

Same as Figure 7 for Figure 8. It needs to be clearer that the x-axis is time not tidal coefficients. DO NOT leave it in the text!

Line 325: DARTs should be referred to as DART stations rather just DART buoys. DART buoy refers to the buoy, and it neglects the bottom pressure recorder. DART station is all inclusive of the whole system.

Line 335: “Remains the best performing …” is a less awkward turn of phrase.

Line 361: Authors should consider posting FIR filter coefficients and EOF scripts as they were calculated using MATLAB with examples to ensure correct application. I was able to check everything else; however, good scientific coding practices dictate that even if native functions are used that they be viewable to ensure correct application of them.

Overall, it is a decent paper. The authors may want to check grammar and verb tenses to ensure that the paper does not read awkwardly. I could make more suggestions there, but I addressed the more egregious ones.
Citation: https://doi.org/10.5194/nhess-2024-113-RC2
- AC2: 'Reply on RC2', Cesare Angeli, 31 Jul 2024
  
  We thank the anonymous referee for the thorough and insightful comments about our work. In the following, we would like to briefly respond to some selected points, in order to receive further feedback, if the referee deems it appropriate. It is implied that any other comment is accepted and all corrections will be implemented in the final version of the paper.
  Major comments:
  2 The work will benefit greatly by comparing computational costs of each of the four algorithms. A robust method that takes up too much time defeats the purpose of real-time detection. The authors say the algorithms are efficient, but it should also be shown. This addition would make the paper more impactful.
  Each technique is fast enough to be used in real time, i.e. the computational costs for each time step is shorter than the sampling time of the instruments. Nonetheless, we understand the need for a more detailed a quantitative analysis which we will add to the paper.
  
  3 Background noise should be done for the same time periods rather than at differing times. This leaves the DART stations open to misrepresented background values that may be influenced by meteorological and oceanographic phenomena. All 5 DARTs studied here have overlapping data at the same times with multiple tsunami detections in those times. Without accounting for these features, it makes the research less impactful, as it remains unclear if differing conditions favor different detection algorithms.
  1 The manuscript should add the New Zealand DARTs, as they provide a good dataset for the Southern Pacific.
  
  The major difficulty for the analysis of overlapping time series in different DART is finding portions of data as close to "ideal" as possible to reduce the influence of other factors. In particular, as specified in the paper, we want weeks long time series with no seismic shaking, no tsunami signal, no discontinuity, no holes in the data and enough preceding data to compute accurate tidal coefficients for TDA. This last point is the most critical. Nonetheless, the suggestion is very interesting and on point. We will find overlapping signals in conditions as close as possible to the criteria explained above and we will add the analysis to the paper.
  
  Minor comments
  17 Line 361: Authors should consider posting FIR filter coefficients and EOF scripts as they were calculated using MATLAB with examples to ensure correct application. I was able to check everything else; however, good scientific coding practices dictate that even if native functions are used that they be viewable to ensure correct application of them.
  
  The codes for both of them are very simple and only use basic MATLAB functions. All the informations needed for the implementation are present in the paper. Nonetheless, the codes will be added either as Supplementary Material or as listings in a dedicated appendix.
  
  Citation: https://doi.org/10.5194/nhess-2024-113-AC2
- AC3: 'Reply on RC2 - Final', Cesare Angeli, 13 Aug 2024
  
  We thank the anonymous referee for the thorough and insightful comments about our work.
  In the following, we would like to briefly answer the comments and suggestions of the referee.
  With respect to the previous comment, here we addressed all the points raised by the referee, while some minor ones were not addressed in the previous reply.
  
  Major comments:
  
  1 Introduction should state clearly the motivation and scientific questions for why the work is being done.
  - The motivation behind the work is to test tsunami detection algorithms. More details on this aspect will be added, in particular in terms of the characteristics and properties of the techniques that we investigate in later sections. The space dedicated to discussion of coastal sea level stations will also be reduced.
  
  2 The work will benefit greatly by comparing computational costs of each of the four algorithms. A robust method that takes up too much time defeats the purpose of real-time detection. The authors say the algorithms are efficient, but it should also be shown. This addition would make the paper more impactful.
  - Each technique is fast enough to be used in real time, i.e. the computational costs for each time step
  is shorter than the sampling time of the instruments. As an order of magnitude, Mofjeld's algorithm and TDA require
  only few hundred/thousand floating point operations (if tidal coefficients have already been computed for TDA), while
  EOF detiding and the FIF-based technique require around a tenth of a second per step.
  We understand the need for a more detailed a quantitative analysis, given orders of magnitude of difference between techniques.
  So, we will add it to the paper.
  
  3 Background noise should be done for the same time periods rather than at differing times. This leaves the DART stations open to misrepresented background values that may be influenced by meteorological and oceanographic phenomena. All 5 DARTs studied here have overlapping data at the same times with multiple tsunami detections in those times. Without accounting for these features, it makes the research less impactful, as it remains unclear if differing conditions favor different detection algorithms.
  1 The manuscript should add the New Zealand DARTs, as they provide a good dataset for the Southern Pacific.
  - The major difficulty for the analysis of overlapping time series in different DART is finding portions of data as close to "ideal" as possible to reduce the influence of other factors. In particular, as specified in the paper, we want long enough (several weeks) time series such that they do not include seismic shaking, discontinuity, gaps before the tsunami signal in order to compute accurate tidal coefficients for TDA. This last point is the most critical. Nonetheless, the suggestion is very interesting and on point.
  We will find overlapping signals in conditions as close as possible to the criteria explained above and we will add the analysis to the paper.
  We will check the suggested New Zealand stations to get the best possible geographical coverage.
  
  Minor comments
  
  1 Line 46-47: These are not review papers but proper studies. Refer to them as such.
  - The text will be reworded accordingly.
  
  2 Line 49: The proper citation for the DART network is Titov, V.V., F.I. Gonzàlez, E.N. Bernard, M.C. Eble, H.O. Mofjeld, J.C. Newman, and A.J. Venturato (2005): Real-time tsunami forecasting: Challenges and solutions. Hazards, 35(1), Special Issue, U.S. National Tsunami Hazard Mitigation Program, 41‚Äì58.
  - It will be added to the manuscript.
  
  3 Cite DART data as National Oceanic and Atmospheric Administration (2005): Deep-Ocean Assessment and Reporting of Tsunamis (DART(R)). NOAA National Centers for Environmental Information. doi:10.7289/V5F18WNS [access date].
  - It will be added to the manuscript.
  
  4 Line 72: Do not use DART for more than one acronym. It is confusing, and it muddies the narrative. Instead, call it MOF for Mofjeld (1997).
  - The text and figures will be modified accordingly.
  
  5 Line 86: Be consistent with use of “on board” or “on-board.” Most instances of the use of ”on board” are incorrect.
  - The text will be corrected.
  
  6 Line 106: State the length of a lunar day in other time units (e.g., hours, minutes, or seconds).
  - The lunar day is 24h 50.4min, as reported by Tolkova (2009, 2010). It will be added to the text
  
  7 Figure 1: Missing y-axis units. Also, make y-axis the same limit for all subplots.
  - Being a basis set, the computations do not change if the vectors are multiplied by an arbitrary constant. For this reason, a unit of measurement is not strictly necessary. The same holds for the plot ranges.
  Nonetheless, we see that in the present state, the text and this figure may be disorienting. So the suggestions about the plot will be implemented and an explanation about the arbitrariness of amplitude of these vectors will be added to the text.
  
  8 Line 144: Fix the units in the brackets.
  - It is a typesetting error. It will be fixed.
  
  9 Line 175: Why do you choose this range of periods?
  - We choose this range to keep in the oscillation in the tsunami frequency band. Nonetheless, we should also note that the technique is quite robust in changing these parameters, in particular the larger period. We will explain this in more detail.
  
  10 Line 196: Colloquial speak “replace with a different, appropriate modifier.
  - We will replace with a more appropriate modifier.
  
  11 Line 200: Replace “taller” with larger.
  - It will be done.
  
  12 What does it mean to be a “long” deployment?
  - In this context, a "long" deployment is one that is long enough to compute a tidal model accurately for the TDA technique, so at least several months. An explanation will be added.
  
  13 Figure 7: This figure needs a long caption to explain all of what is going on in it. What is the x-axis? Tidal coefficients? If so, mention it! Do not assume everyone knows what they are. Also, choose a better colorblind friendly color scheme. I am lost as to what is being plotted here because they all look the same.
  - The plot shows absolute average value for each time series in the dataset, thus each tick on the x axis represent one the signals. A detailed description of the plot will be added to the caption. We will change the color scheme to a more inclusive one.
  
  14 Same as Figure 7 for Figure 8. It needs to be clearer that the x-axis is time not tidal coefficients. DO NOT leave it in the text!
  - The answer to comment 13 holds here as well.
  
  15 Line 325: DARTs should be referred to as DART stations rather just DART buoys. DART buoy refers to the buoy, and it neglects the bottom pressure recorder. DART station is all inclusive of the whole system.
  - The text will be corrected in order to clear this misunderstanding.
  
  16 Line 335: “Remains the best performing“ is a less awkward turn of phrase.
  - It will be reworded for clarity.
  
  17 Line 361: Authors should consider posting FIR filter coefficients and EOF scripts as they were calculated using MATLAB with examples to ensure correct application. I was able to check everything else; however, good scientific coding practices dictate that even if native functions are used that they be viewable to ensure correct application of them.
  
  -The codes will be provided as requested.
  
  Citation: https://doi.org/10.5194/nhess-2024-113-AC3

Cesare Angeli, Alberto Armigliato, Martina Zanetti, Filippo Zaniboni, Fabrizio Romano, Hafize Başak Bayraktar, and Stefano Lorito

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https://doi.org/10.5194/nhess-2024-113-supplement

Cesare Angeli, Alberto Armigliato, Martina Zanetti, Filippo Zaniboni, Fabrizio Romano, Hafize Başak Bayraktar, and Stefano Lorito

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Short summary

To issue precise and timely tsunami alerts, detecting the propagating tsunami is fundamental. The most used instruments are pressure sensors positioned at the ocean bottom, called Ocean-Bottom Pressure Gauges (OBPGs). In this work, we study four different techniques that allow to recognize a tsunami as soon as it is recorded by an OBPG and a methodology to calibrate them. The techniques are compared in terms of their ability to detect and characterize the tsunami wave in real time.


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