An enhanced correlation identification algorithm and its application on spread spectrum induced polarization data

He, Siming; Guan, Jian; Ji, Xiu; Xu, Hang; Wang, Yi

doi:https://doi.org/10.5194/npg-28-247-2021

Articles | Volume 28, issue 2

https://doi.org/10.5194/npg-28-247-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/npg-28-247-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 28, issue 2

Research article

|

19 May 2021

Research article |

| 19 May 2021

An enhanced correlation identification algorithm and its application on spread spectrum induced polarization data

Siming He, Jian Guan, Xiu Ji, Hang Xu, and Yi Wang

Download

Final revised paper (published on 19 May 2021)
Preprint (discussion started on 26 Jun 2020)

Interactive discussion

Status: closed

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

- Printer-friendly version

- Supplement

RC1: 'Review of manuscript "npg-2020-8"', Anonymous Referee #1, 14 Sep 2020
RC2: 'A review of "An enhanced correlation identification algorithm and its application on spread spectrum induced polarization data"', Anonymous Referee #2, 28 Sep 2020
- AC2: 'Re: comments of Reviewer 3', Siming He, 26 Nov 2020
AC1: 'Re: comments of Reviewer 1', Siming He, 26 Nov 2020

Peer-review completion

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

AR by Siming He on behalf of the Authors (26 Nov 2020) Author's response Manuscript

ED: Referee Nomination & Report Request started (16 Dec 2020) by Richard Gloaguen

RR by Anonymous Referee #2 (31 Dec 2020)

RR by Anonymous Referee #3 (24 Feb 2021)

Suggestions for revision or reasons for rejection

1. The reference list needs to be updated. It should be alphabetic and the year should be at the end. See https://www.nonlinear-processes-in-geophysics.net/submission.html#manuscriptcomposition for guide lines.
2. Høyer et al. 2018 is missing from the reference list.
3. Page 1: “SSIP technology has a certain degree of noise immunity”.
Why? What to you refer to here? Reference missing.
4. Page 9, Line 1: Reference missing for Res2DInv.
5. Page 9, Line 8, “red point”. I guess you are referring to Figure 8? Maybe just “data points”?
6. Page 9, Line 17, “algorithm” should be “algorithms”
7. Figure 8: What is the difference between blue and red dots? If there is no difference, I suggest that you use the same color.
8. Figure 11: Please elaborate the figure text. What are the number in the plot?
9. Experiment:
I think the experiment is missing some information in order to be acceptable:
a. What is the resistivity of the two cavities. It is difficult to access the inversion results, when the resistivity is unknown.
b. As commented by previous review, you need to show some data and data fits. So, a figure with the recorded data (resistivity and phase) and the forward response (resistivity and phase) of the final inversion model. This could be added to Figure 11.
c. You try to sell the algorithm for handling IP data, so you need to show a section with the phase results. This was also pointed out by a previous reviewer. If you IP results are not satisfying due to low chargeability (as you write in your answer), then I suggest that you find another example. As a minimum, this should be include in a discussion.
10. A discussion should be added in the manuscript with the answer to comment 5 from reviewer 1 (version 3)
11. Is the code available for other researchers to use? Please add a section with code availability after the conclusion.

Hide

ED: Publish subject to minor revisions (review by editor) (25 Feb 2021) by Richard Gloaguen

AR by Siming He on behalf of the Authors (08 Mar 2021)

ED: Publish subject to minor revisions (review by editor) (10 Mar 2021) by Richard Gloaguen

AR by Siming He on behalf of the Authors (20 Mar 2021) Author's response Author's tracked changes Manuscript

ED: Publish subject to technical corrections (06 Apr 2021) by Richard Gloaguen

AR by Siming He on behalf of the Authors (14 Apr 2021) Author's response Manuscript

Short summary

We propose an enhanced correlation identification (ECI) algorithm to attenuate background noise. The cross-correlation matching method is helpful for the extraction of useful components of the raw SSIP data and suppression of background noise. Experiments on both synthetic and real SSIP data show that the ECI algorithm is proposed to preserve the valid information of the raw SSIP data to display the actual location and shape of adjacent high-resistivity anomalies.