An Analysis of Meteorological Anomalies in Kamchatka in Connection with the Seismic Process
Round 1
Reviewer 1 Report
Comments and Suggestions for AuthorsThis paper is an attempt to correlate two time series of events where one series have discontinuities. This paper must be revised because, on one hand, more explanations are necessary and, on the other hand, the conclusions of this work are not clear.
Section 2.1: Meteorological data. For example the temperature is subject to strong day/night and seasonal variations. How your data processing in Section 3 can take into account this point?
Section 2.2: Earthquake data. Why you have selected EQs with a starting magnitude equal to 5.5 ? Of course a limit must be chosen but do you think that an EQ with a magnitude equal to 5 and a depth equal to 10 km would produce lower meteorological effects than an EQ of magnitude 6 and a depth of 600 km ? Nothing is said in the paper about the depth influence of the selected EQs. Nothing is said in the paper about the possible effect (or not) of the aftershocks (there is an important one in your Table 1). Nothing is said in the paper about the location of the epicenters. Most of your EQs occur in the open sea. Is it good or not for the meteorological effects ? In a word, different EQ data sets could be useful to confirm your conclusion.
Section 3: Why you have to undertake such complicated data processing ? What is the reason and what is the aim ? At each step what is the physical meaning of the new sets of data you obtain (EEMD waveform, EEMD amplitude, EEMD frequency) for each IMF level ? Also what is the physical meaning of an IMF level ?
Line 173 lower suffix of B must be corrected
Line 263 not 15
Line 287 why you work with the EEMD amplitude ? If you do not work with the EEMD frequency it is not necessary to show these data.
Line 304 I think it is a joke !!!
Lines 348-354 I have many problems with this paragraph. “For a correct comparison of two event flows, their average intensities must be approximately equal.” I do not think it is necessary you can compare two data sets with very different intensities. “ This means that the number of the largest local maxima of the amplitudes of the envelopes of meteorological parameter series must be equal to the number of earthquakes, i.e. 418.” If I well understand you select 418 largest EEMC amplitudes among the complete data set. Why ? How they are selected? “With an increase in the number of the IMF level, the amplitudes of their envelopes become more and more low-frequency.” This sentence is not clear. I just see that the EEMD frequency decreases when the number of the IMF level increases. “As a result, it is possible to select 418 largest local maxima of the amplitudes of the envelopes only for a certain number of lower decomposition levels.” Why ? If I check in Figures 3, 4 , and 5 it is possible to find 418 largest amplitudes for low IMF levels (low than 6). Also what do you understand by “local maxima” ?
Line 359 what do you mean by “reverse” influence ?
Lines 374-375 Explanation of this sentence ?
Line 421 What is the meaning of “the average value of the components of the influence matrices” ?
Line 432 What is the meaning of “ the “reverse” advance” ?
Section 6 Conclusions. It is not clear because you mention “characteristic periods of variations of 8-16 days” and in the next sentence “largest local maxima of the amplitudes of the envelopes of atmospheric pressure and air temperature series was detected with a lead time of 1.5-2 years before three strong earthquakes.” Why ? Do you mean that for all EQs a meteorological variation can be observed in the range 8-16 days before but for strong EQs it could be up to 1.5-2 years before ?
Many sentences are very hard to understand. Example on lines 529-532: “As for the assessment of the average measure of the lead time of the largest local maxima of instantaneous amplitudes relative to the time of the flow of earthquakes with a magnitude of at least 5.5, shown in Fig. 9, a strong non-stationarity of this measure, the presence of both a strong lead and almost zero, is of particular interest.”
Comments on the Quality of English LanguageNo comment except that the sentences are too long and sometimes difficult to understand.
Author Response
Reviewer #1
Section 2.1: Meteorological data. For example the temperature is subject to strong day/night and seasonal variations. How your data processing in Section 3 can take into account this point?
Response: The Hilbert-Huang method used provides a decomposition of the time series into a sequence of IMF levels that strongly depend on frequency, and it naturally takes into account both daily and seasonal periodicity. Please see the decomposition into IMF levels in Figures 3-5 and the dependences of average frequencies on IMF level numbers presented in Figure 6.
Section 2.2: Earthquake data. Why you have selected EQs with a starting magnitude equal to 5.5 ? Of course a limit must be chosen but do you think that an EQ with a magnitude equal to 5 and a depth equal to 10 km would produce lower meteorological effects than an EQ of magnitude 6 and a depth of 600 km ? Nothing is said in the paper about the depth influence of the selected EQs. Nothing is said in the paper about the possible effect (or not) of the aftershocks (there is an important one in your Table 1). Nothing is said in the paper about the location of the epicenters. Most of your EQs occur in the open sea. Is it good or not for the meteorological effects ? In a word, different EQ data sets could be useful to confirm your conclusion.
Response: It should be noted that the purpose of this study was to attempt a quantitative assessment of the effect of advance by the behavioral features of meteorological parameters of seismic events in general, rather than specific earthquakes. As for earthquakes that occurred in the ocean, it is known that they are preceded by emissions of radon bubbles that rise to the surface, which is accompanied by atmospheric ionization effects and may be one of the physical mechanisms that explain the appearance of the advance effect, see [4-10] in the new version of the paper.
Section 3: Why you have to undertake such complicated data processing ? What is the reason and what is the aim ? At each step what is the physical meaning of the new sets of data you obtain (EEMD waveform, EEMD amplitude, EEMD frequency) for each IMF level ? Also what is the physical meaning of an IMF level ?
Response: Apparently, the Hilbert-Huang method used is unfamiliar to the esteemed reviewer, which is why he calls it "too complicated". In fact, this method has long been in the arsenal of time series analysis methods (since 1998) and in its implementation it is no more complicated than, for example, the wavelet decomposition method. Here I am powerless to change anything in the article, the basis of which is precisely the use of the Hilbert-Huang decomposition. I can only advise the esteemed reviewer to familiarize himself with items [25-26] in the list of references in the new version of the paper. Besides that this method is realized in Matlab and in Python open access libraries, although we used our own software. The physical meaning of the IMF levels is exactly the same as the physical meaning of frequency bands in the Fourier decomposition or detail levels in the decomposition using orthogonal wavelets. But there is a significant difference that distinguishes the Hilbert-Huang decomposition: this is the dependence of amplitudes and frequencies on time. Therefore, the concepts of instantaneous amplitudes of instantaneous frequency envelopes are used. All this is discussed in detail in [25-26] from the list of references in the new version of the paper. I can only again advise the esteemed reviewer to familiarize himself with the methods.
Line 173 lower suffix of B must be corrected
Response: Thank you, improved.
Line 263 not 15
Response: Thank you, improved. I have added one more formula (7) and all other formulae were renumbered. Thus, in this place now is (7).
Line 287 why you work with the EEMD amplitude ? If you do not work with the EEMD frequency it is not necessary to show these data.
Response: The graphs of instantaneous frequencies were given precisely because potential readers of the article might be interested in the behavior of not only instantaneous amplitudes of envelopes, but also instantaneous frequencies. That is, the meaning of these graphs was to make the text of the article more understandable to those readers who are not very familiar with the method used. The graphs in Figure 6 are intended for the same purposes.
Line 304 I think it is a joke !!!
Response: We have omitted the derivation of this formula, since such a derivation would be an exercise in differentiating a complex function. But if the reviewer insists, then we can provide the derivation of this formula. We have added one more formula numbered (11) and renumbered the remaining formulas. In total, there are now 19 formulas in the article.
Lines 348-354 I have many problems with this paragraph. “For a correct comparison of two event flows, their average intensities must be approximately equal.” I do not think it is necessary you can compare two data sets with very different intensities. “This means that the number of the largest local maxima of the amplitudes of the envelopes of meteorological parameter series must be equal to the number of earthquakes, i.e. 418.” If I well understand you select 418 largest EEMC amplitudes among the complete data set. Why ? How they are selected? “With an increase in the number of the IMF level, the amplitudes of their envelopes become more and more low-frequency.” This sentence is not clear. I just see that the EEMD frequency decreases when the number of the IMF level increases. “As a result, it is possible to select 418 largest local maxima of the amplitudes of the envelopes only for a certain number of lower decomposition levels.” Why ? If I check in Figures 3, 4 , and 5 it is possible to find 418 largest amplitudes for low IMF levels (low than 6). Also what do you understand by “local maxima” ?
Response: In our method it is essential that the two event streams, relative to which it is established which of the event streams is ahead of the other on "average", are very important that the average intensity of events, i.e. the total number of events divided by the time interval, are approximately the same. It is impossible to compare event streams if the average intensity in one of them differs significantly from the average intensity in the other stream. Therefore, we chose the condition that the number of the largest local maxima at the IMF level be equal to the number of seismic events. This could be done only for the first, most high-frequency IMF levels. This was done in the working calculations. However, it was level number 6 that gave the most impressive results compared to other levels, for which it was possible to identify 418 local extremes of instantaneous amplitudes. We attribute the fact that level number 6 turned out to be the most effective to the fact that its period range corresponds to 8-16 days, which includes half the period of the lunar month (28 days). A local maximum is a generally accepted term and means a point in the argument of a smooth function (in our case, the argument is time) for which the values ​​to the left and right of this point are less than the value at the point itself.
Line 359 what do you mean by “reverse” influence ?
Response: In this paragraph of the article above we called "direct" the influence of the largest local maxima of instantaneous amplitudes at the 6th IMF level on the moments of seismic events. Therefore, we called the opposite influence of earthquake times on local maxima of instantaneous amplitudes "reverse".
Lines 374-375 Explanation of this sentence ?
Response: In the orthogonal wavelet decomposition, the frequency band corresponding to the detail level with the number k has minimum and maximum periods equal to 2^k*DeltaT and 2^(k+1)*DeltaT, where DeltaT is the time step [28]. For the detail level with the number k=6 and the time step of 3 hours, the minimum period is equal to 192 hours or 8 days, and the maximum period is equal to 384 hours or 16 days.
Line 421 What is the meaning of “the average value of the components of the influence matrices” ?
Response: Since the elements of the influence matrix are calculated in sliding time windows, their maximum values ​​also depend on time. As described in the implementation of the influence matrix method, these maximum values ​​of the influence matrix components are then averaged in successive time fragments of length "epsilon", equal to 0.1 years. This is described in paragraph 5 of the method implementation.
Line 432 What is the meaning of “ the “reverse” advance” ?
Response: I can write again what we mean by “direct” and “reverse” influence. We called "direct" the influence of the largest local maxima of instantaneous amplitudes at the 6th IMF level on the moments of seismic events. Therefore, we called the opposite influence of earthquake times on local maxima of instantaneous amplitudes "reverse". The term “advance” may be changed to “ahead”.
Section 6 Conclusions. It is not clear because you mention “characteristic periods of variations of 8-16 days” and in the next sentence “largest local maxima of the amplitudes of the envelopes of atmospheric pressure and air temperature series was detected with a lead time of 1.5-2 years before three strong earthquakes.” Why ? Do you mean that for all EQs a meteorological variation can be observed in the range 8-16 days before but for strong EQs it could be up to 1.5-2 years before ?
Response: The characteristic periods of variations of 8-16 days follow from the fact that the most effective IMF level, as well as the detail level of wavelet decompositions, was number 6. This is also evident from the graphs in Figure 6. As for the time of occurrence of precursor anomalies, we repeat once again that the purpose of this article was not to search for precursors of specific seismic events. We are talking about the general level of seismicity, so to speak, about the "seismic temperature" of the region. It turned out that the parameters of meteorological time series really precede the increase in the general "seismic temperature" in the range of variation periods of 8-16 days and this is observed 1.5-2 years before such an increase. But note that this effect is not constant and it changes over time, which is illustrated in Figure 9.
Many sentences are very hard to understand. Example on lines 529-532: “As for the assessment of the average measure of the lead time of the largest local maxima of instantaneous amplitudes relative to the time of the flow of earthquakes with a magnitude of at least 5.5, shown in Fig. 9, a strong non-stationarity of this measure, the presence of both a strong lead and almost zero, is of particular interest.”
Response: We have changed this sentence to the following: “In Figure 9, a strong non-stationarity of the average measure of the lead time of the greatest local maxima of instantaneous amplitudes relative to the time of the earthquake flow is noticeable. We believe that such non-stationarity is of interest for subsequent studies of its connection with cyclonic processes in the northern Pacific Ocean.”
Reviewer 2 Report
Comments and Suggestions for AuthorsAs I understand, the start of this method was initiated by authors in publication in Russian (for another period of time) https://www.researchgate.net/publication/357144592_ANOMALII_METEOROLOGICESKIH_PARAMETROV_I_SILNYE_ZEMLETRASENIA_NA_PRIMERE_RAJONA_POLUOSTROVA_KAMCATKA
The content is relevant to the journal's scope.
I personally, think, that 2 publications are also important to cite:
Schekotov, A., Borovleva, K., Pilipenko, V., Chebrov, D., Hayakawa, M. (2023). Meteorological Response of Kamchatka Seismicity. In: Kosterov, A., Lyskova, E., Mironova, I., Apatenkov, S., Baranov, S. (eds) Problems of Geocosmos—2022. ICS 2022. Springer Proceedings in Earth and Environmental Sciences. Springer, Cham. https://doi.org/10.1007/978-3-031-40728-4_17
S. Uyeda, T. Nagao, K. Hattori, M. Hayakawa, K. Miyaki, et al.. Geophysical Observatory in Kamchatka region for monitoring of phenomena connected with seismic activity. Natural Hazards and Earth System Sciences, 2001, 1 (1/2), pp.3-7. ffhal-00301546
Some references are incorrect upon MDPI style. Like [18] Kopylova G. N., V. Yu. Ivanov, V. A. Kasimova, = should be Kopylova G. N., Ivanov V. Yu. , Kasimova V. A. etc. Somewhere there is "and" between surnames, somewhere is missed. For the books Publisher Name and city are also missed.
Author Response
I personally, think, that 2 publications are also important to cite:
Schekotov, A., Borovleva, K., Pilipenko, V., Chebrov, D., Hayakawa, M. (2023). Meteorological Response of Kamchatka Seismicity. In: Kosterov, A., Lyskova, E., Mironova, I., Apatenkov, S., Baranov, S. (eds) Problems of Geocosmos—2022. ICS 2022. Springer Proceedings in Earth and Environmental Sciences. Springer, Cham. https://doi.org/10.1007/978-3-031-40728-4_17
- Uyeda, T. Nagao, K. Hattori, M. Hayakawa, K. Miyaki, et al.. Geophysical Observatory in Kamchatka region for monitoring of phenomena connected with seismic activity. Natural Hazards and Earth System Sciences, 2001, 1 (1/2), pp.3-7. ffhal-00301546
Some references are incorrect upon MDPI style. Like [18] Kopylova G. N., V. Yu. Ivanov, V. A. Kasimova, = should be Kopylova G. N., Ivanov V. Yu. , Kasimova V. A. etc. Somewhere there is "and" between surnames, somewhere is missed. For the books Publisher Name and city are also missed.
Response: I have inserted the recommended items into new version of the paper. Pleas look new version of the paper.
Reviewer 3 Report
Comments and Suggestions for AuthorsManuscript ID: atmosphere-3414626
Title: Analysis of meteorological anomalies in Kamchatka in connection with the seismic process
This manuscript presents a detailed analysis of meteorological anomalies associated with seismic processes in Kamchatka. The use of Huang decomposition and Hilbert transforms is well explained and the findings add valuable insights into the interaction between atmospheric and lithospheric processes. Below are minor revision suggestions to improve overall quality.
1. To clarify the hypothesis statement in the first sentence of the abstract: “The hypothesis of occurrence of meteorological anomalies before earthquakes is investigated” should be replaced with “This study investigates the hypothesis that meteorological anomalies may precede earthquake events”. Also, at the end of the abstract, the importance of the results of the study should be emphasized. For example, “This study reveals that certain meteorological anomalies can be a precursor for seismic activity.”
2. In the introduction, it would be good to briefly state what makes this study different from previous studies.
3. Figures 3-5: A more detailed explanation of what each figure represents, focusing specifically on how waveforms correspond to seismic and meteorological events, would be easier for the reader to understand.
4. The results referring to Figure 8 are somewhat unclear. More explicit links could be made in the text. Clear statements linking the results to the hypothesis could be added. For example “Figure 8 demonstrates a clear leading effect of meteorological anomalies before seismic events.”
5. The discussion section could benefit from more emphasis on practical implications. It would be useful to add a paragraph to this section discussing how the findings may affect earthquake forecasting systems in the real world.
6. In the conclusion, it would be appropriate to include recommendations on how the findings can contribute to practical applications (e.g. early warning systems).
7. Some references lack complete formatting. All references, including DOI links where appropriate, should be re-typed in accordance with the journal's citation conventions.
Author Response
This manuscript presents a detailed analysis of meteorological anomalies associated with seismic processes in Kamchatka. The use of Huang decomposition and Hilbert transforms is well explained and the findings add valuable insights into the interaction between atmospheric and lithospheric processes. Below are minor revision suggestions to improve overall quality.
- To clarify the hypothesis statement in the first sentence of the abstract: “The hypothesis of occurrence of meteorological anomalies before earthquakes is investigated” should be replaced with “This study investigates the hypothesis that meteorological anomalies may precede earthquake events”. Also, at the end of the abstract, the importance of the results of the study should be emphasized. For example, “This study reveals that certain meteorological anomalies can be a precursor for seismic activity.”
Response: Done, The abstract changed.
- In the introduction, it would be good to briefly state what makes this study different from previous studies.
Response: We have added in the Introduction the phrase: “The novelty of the used method of joint analysis of meteorological time series and seismicity lies in the joint use of the Hilbert-Huang decomposition and the influence matrix method to assess the relationship between two point processes.”
- Figures 3-5: A more detailed explanation of what each figure represents, focusing specifically on how waveforms correspond to seismic and meteorological events, would be easier for the reader to understand.
Response: Figures 3-6 of Hilbert-Huang decomposition are not connected with seismic process. The connection begins starting from the Figure 7 where different time moments sequences are presented for biggest local maxima of amplitudes and time moments of earthquakes. These sequences of time moments are analyzed further using influence matrix method.
- The results referring to Figure 8 are somewhat unclear. More explicit links could be made in the text. Clear statements linking the results to the hypothesis could be added. For example “Figure 8 demonstrates a clear leading effect of meteorological anomalies before seismic events.”
Response: Please look figure caption in the new version of the paper.
- The discussion section could benefit from more emphasis on practical implications. It would be useful to add a paragraph to this section discussing how the findings may affect earthquake forecasting systems in the real world.
Response: Please look the new version of the paper. The purpose of this article was not to search for precursors of specific seismic events. We are talking about the general level of seismicity, so to speak, about the "seismic temperature" of the region. It turned out that the parameters of meteorological time series really precede the increase in the general "seismic temperature" in the range of variation periods of 8-16 days and this is observed 1.5-2 years before such an increase. But note that this effect is not constant and it changes over time, which is illustrated in Figure 9. In Figure 9, a strong non-stationarity of the average measure of the lead time of the greatest local maxima of instantaneous amplitudes relative to the time of the earthquake flow is noticeable. We believe that such non-stationarity is of interest for subsequent studies of its connection with cyclonic processes in the northern Pacific Ocean.
- In the conclusion, it would be appropriate to include recommendations on how the findings can contribute to practical applications (e.g. early warning systems).
Response: Please look the end of Conclusion section in the new version of the paper.
- Some references lack complete formatting. All references, including DOI links where appropriate, should be re-typed in accordance with the journal's citation conventions.
Response: I have added all doi which could be found. For some old references points doi are absent but they could be found in internet.
Round 2
Reviewer 1 Report
Comments and Suggestions for AuthorsThe author must write his paper for a reader. The reader is not required to know all the signal processing methods, and he should not be required to read additional articles in journals that often require a fee. On the other hand, he must know why the author used this method and what physical quantities this method allows to obtain.
