<p>This statement from Geophysical Data Analysis: Discrete Inverse Theory: William Menke should make things clear for you</p>

<blockquote>
  <p>The diagonal elements of the covariance matrix are a measure of the
  width of the distribution of the data, and the off-diagonal elements
  indicate the degree to which pairs of data are correlated.</p>
</blockquote>

<p>Regarding the second part of your doubt whether a situation is possible where ds = 0 ; this would imply some error in measurement(for example - some noise) and would not have any implication on the geophysical nature of the data . It would be advisable to take multiple readings at different locations in this kind of situation.</p>

<p>Finally ds provides some insight into the quality of data collected but no true information about the "information content of measurement".</p>

CC BY-SA 3.0
2015-06-14T17:08:43.407Z
2015-06-14T17:08:43.407Z
shrey
<p>According to Rodgers (2000) equation 2.80</p>

<p>$$
d_s = \mathrm{tr}(\mathbf{A})
$$</p>

<p>where $d_s$ is the number of degrees of freedom for the signal, tr denotes the trace, and $A$ is the averaging kernel matrix.</p>

<p>I'm trying to properly understand this.  For example, is it theoretically possible for the averaging kernel to have mainly or even exclusively off-diagonal elements?  In practice, this would mean that the retrieved state at height $h$ would correspond to the true state at height $h+1$, if we take remote sounding of an atmospheric column as an example.  In such a (admittedly contrived) example, we could get $d_s=0$, although we do have information in the measurement — it's just displaced.</p>

<p>Is this situation possible?  If yes, does $d_s$ tell the whole story as for the information content in the measurement?</p>

<hr>

<p><sup>
Clive D. Rodgers, Inverse methods for atmospheric sounding, Theory and Practice. ©2000 World Scientific Publishing Co., London, UK.
</sup></p>

CC BY-SA 3.0
2014-06-16T20:29:33.593Z
I realize inversion is very important in the Earth Sciences, but wouldn't this question be better placed in a maths forum?
winwaed
CC BY-SA 3.0
2014-06-16T20:34:59.790Z
@winwaed I'd expect it's too applied for [Math.SE].  I don't know if it would fit at [Physics.SE] either.  [Let's discuss it at meta](http://meta.earthscience.stackexchange.com/q/299/6).
gerrit
CC BY-SA 3.0
2014-06-17T14:20:25.627Z
Why the downvote?  If you believe it to be off-topic, please do not downvote, but vote to close instead.  If you believe it is a bad question, please leave a comment explaining why.
gerrit
CC BY-SA 3.0
2014-06-17T14:32:02.680Z
(I did not downvote it.)
winwaed
CC BY-SA 3.0
2014-06-17T14:37:13.287Z
@winwaed I didn't think you were, I was addressing whoever did.
gerrit
CC BY-SA 3.0
2014-06-21T07:27:27.170Z
@gerrit voting to close requires a certain rep. People who do not have it might well see downvoting as the nearest sensible thing they could do (and I'd tend to agree, having been reprimanded for flagging off-topic stuff in the past on other sites)
Semidiurnal Simon
CC BY-SA 3.0
2014-06-22T02:48:02.353Z
@SimonW Good point.
gerrit
<remote-sensing>
Are the elements of an averaging kernel matrix always centered on the diagonal, or can they be exclusively off-diagonal in rare cases?
gerrit
# Are the elements of an averaging kernel matrix always centered on the diagonal, or can they be exclusively off-diagonal in rare cases?

(Asked by: username_0 on 2014-06-16)

According to Rodgers (2000) equation 2.80

$$
d\_s = \mathrm{tr}(\mathbf{A})
$$

where $d\_s$ is the number of degrees of freedom for the signal, tr denotes the trace, and $A$ is the averaging kernel matrix.

I'm trying to properly understand this. For example, is it theoretically possible for the averaging kernel to have mainly or even exclusively off-diagonal elements? In practice, this would mean that the retrieved state at height $h$ would correspond to the true state at height $h+1$, if we take remote sounding of an atmospheric column as an example. In such a (admittedly contrived) example, we could get $d\_s=0$, although we do have information in the measurement — it's just displaced.

Is this situation possible? If yes, does $d\_s$ tell the whole story as for the information content in the measurement?

---

Clive D. Rodgers, Inverse methods for atmospheric sounding, Theory and Practice. ©2000 World Scientific Publishing Co., London, UK.

**Tags:** remote-sensing

> **Comments:**
> **username_1**: I realize inversion is very important in the Earth Sciences, but wouldn't this question be better placed in a maths forum?  
> **username_0**: @username_1 I'd expect it's too applied for [Math.SE].  I don't know if it would fit at [Physics.SE] either.  [Let's discuss it at meta](http://meta.earthscience.stackexchange.com/q/299/6).  
> **username_0**: Why the downvote?  If you believe it to be off-topic, please do not downvote, but vote to close instead.  If you believe it is a bad question, please leave a comment explaining why.  
> **username_1**: (I did not downvote it.)  
> **username_0**: @username_1 I didn't think you were, I was addressing whoever did.  
> **username_2**: @username_0 voting to close requires a certain rep. People who do not have it might well see downvoting as the nearest sensible thing they could do (and I'd tend to agree, having been reprimanded for flagging off-topic stuff in the past on other sites)  
> **username_0**: @SimonW Good point.  

---

## 1 answers

### Answer by username_3 on 2015-06-14. Score: 2

This statement from Geophysical Data Analysis: Discrete Inverse Theory: William Menke should make things clear for you

> The diagonal elements of the covariance matrix are a measure of the
> width of the distribution of the data, and the off-diagonal elements
> indicate the degree to which pairs of data are correlated.

Regarding the second part of your doubt whether a situation is possible where ds = 0 ; this would imply some error in measurement(for example - some noise) and would not have any implication on the geophysical nature of the data . It would be advisable to take multiple readings at different locations in this kind of situation.

Finally ds provides some insight into the quality of data collected but no true information about the "information content of measurement".


---

