The adaptive particle swarm optimization technique for solving microseismic source location parameters

Sun, Hong-Mei; Yu, Jian-Zhi; Zhang, Xing-Li; Wang, Bin-Guo; Jia, Rui-Sheng

doi:https://doi.org/10.5194/npg-26-163-2019

Articles | Volume 26, issue 3

https://doi.org/10.5194/npg-26-163-2019

© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/npg-26-163-2019

© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 26, issue 3

Research article

|

15 Jul 2019

Research article |

| 15 Jul 2019

The adaptive particle swarm optimization technique for solving microseismic source location parameters

Hong-Mei Sun, Jian-Zhi Yu, Xing-Li Zhang, Bin-Guo Wang, and Rui-Sheng Jia

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Final revised paper (published on 15 Jul 2019)
Preprint (discussion started on 02 Apr 2019)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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RC1: 'Minor Revision of the manuscript "Technique for solving for microseismic source location parameters based on adaptive particle swarm optimization"', Anonymous Referee #1, 18 Apr 2019
- AC1: 'Response to comments', Rui-Sheng Jia, 02 Jun 2019
- AC2: 'Response to comments', Rui-Sheng Jia, 02 Jun 2019
RC2: 'suggestions on technical corrections for this paper', Anonymous Referee #2, 29 Apr 2019
- AC2: 'Response to comments', Rui-Sheng Jia, 02 Jun 2019
- AC1: 'Response to comments', Rui-Sheng Jia, 02 Jun 2019

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

AR by Rui-Sheng Jia on behalf of the Authors (02 Jun 2019) Author's response Manuscript

ED: Referee Nomination & Report Request started (03 Jun 2019) by Luciano Telesca

RR by SANDU ILIE (08 Jun 2019)

RR by Anonymous Referee #1 (19 Jun 2019)

Suggestions for revision or reasons for rejection

Dear editor and authors,
my general comment is that the quality of the paper improved with respect to the original version. Nevertheless, there are still some little problems, someone related to issues already pointed out in the first revision, but ignored by the authors.
I here provide a line-by-line comment of the revised version of the manuscript.

Page 1, Line 12: change into “discrepancies between differences of modeled arrival times and differences of measured arrival times”. This correction was suggested also in the first revision, but it has been ignored by you.
Page 2, Line 6: remove the word “time” in the sentence “Waldhauser and Ellsworth (2000) presented earthquake location algorithm based on a double time differences”. The concept presented by Waldhauser and Ellsworth is “double difference” and not “double time differences”. This correction was suggested also in the first revision, but it has been ignored by you.
Page 2, line 30 – Page 3 line 4: Please, reformulate in that way: “Assign the microseismic source …, the coordinates of each geophone …., the time of P-wave arrival to i-th geophone …. and the origin time of the microseismic event as t0. Assuming that the rock layers between the microseismic sources and the geophone are uniform (i.e. uniform velocity model), the equivalent average propagation velocity of the P-wave in the medium is V.
Page 3, line 19: In the revised version of the manuscript, you now refer to measured data or differences between measured data with the symbol ‘. Now, instead, you use the symbol ^, which was used in the first version of the manuscript .Please substitute with ‘ symbol, also in the following equations.
Page 4, line 2: substitute to with t0.
Page 4, line 6: Insert a space between “solution” and “the”
Page 5, line 9: Please specify what each d-th component of the particles represent in this work. You can also write it in the section 3.2, but in my opinion it is important to clearly define it, without leaving the reader autonomously understand it.
Page 6, line 5: Insert a space between “initialization” and “of”.
Page 7, line 8: reformulate as follows: “optimization problems are divided into local and global problems. The former consists in looking for the minimum …, the latter ….”
Page 7, line 25: I still do not understand what is the “initial value of evolutionary generations”. Please, be clearer.
Page 9, line 7: I still do not understand the meaning of N
Page 9, line 11: What are the coordinates of S? It is not represented in the plot.
Page 9, figure 2: As in the first revision, I suggest you again to work on the figure, by adding the dimensions of the cube and by putting a symbol (for example a triangle) where the receivers are located.
Page 10, line 1 and following: No efforts have been done to improve the clarity of this paragraph. Some suggestions: First, the synthetic travel times have been computed. Then, the differences between the arrival times at all the pairs of stations have been retrieved according to equation (n) … and so on.
This paragraph is very important, and the greatest clarity as possible is requested.
Page 11: In the previous revision I asked you to specify how you chose the initial values. Furthermore, it was not clear to me why you mentioned different initial values if only a starting solution was provided in table 3. Neither answers nor modifications have been done concerning my comment. Please work on it.
Page 13: Please add a figure showing an example of real datum (seismogram).

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ED: Publish subject to minor revisions (review by editor) (20 Jun 2019) by Luciano Telesca

AR by Rui-Sheng Jia on behalf of the Authors (23 Jun 2019) Author's response Manuscript

ED: Publish as is (25 Jun 2019) by Luciano Telesca

AR by Rui-Sheng Jia on behalf of the Authors (25 Jun 2019) Manuscript

Short summary

An adaptive PSO optimization method is proposed based on the average population velocity in order to solve for location parameters of the seismic source in a location model. Combined with the actual need to solve for seismic source parameters, the model constraints of inertia weight, accelerating constants, the maximum flight velocity of particles, and other parameters are discussed in order to improve the optimization capacity of the PSO algorithm and avoid being trapped in a local optimum.