<p>This might be too late, but here's my answer:</p>
<p>You can acess the fit between two waveforms by simply taking the difference at first, let's call that a residual, taking the <span class="math-container">$L_p$</span> norm of the residual gives you a measure of fit between the two waveforms, the <span class="math-container">$L_2$</span> norm is frequently used. You can also use a cross-correlation function, which is sensitive to the time lag between the two waveforms.</p>
<p>Another way of quantifying the differences between two time-series is Dynamic Time Warping (DTW for short), this has been applied to speech recognition for quite some time (<a href="https://ieeexplore-ieee-org.gaelnomade-1.grenet.fr/document/1163055" rel="nofollow noreferrer">Sakoe and Chiba 1978</a>). A more convenient way of doing so with seismic data has been proposed by <a href="https://library.seg.org/doi/abs/10.1190/geo2012-0327.1" rel="nofollow noreferrer">Hale 2013</a>, the first part of the paper addresses applying DTW to seismic waveforms, the second part he applies it to seismic images (a collection of waveforms, or a gather). DTW gives a map, that indicates how to change --or warp-- one time series to match the other, also, it gives a measure of fit, in Hale 2013 it is called the DTW distance, this quantity is not a norm.</p>
<p>I hope this answers your question.</p>

CC BY-SA 4.0
2024-06-18T16:48:20.327Z
2024-06-18T16:49:47.973Z
2024-06-18T16:49:47.973Z
Mustapha Benziane
<p>Our group has built an Arduino-based device with an MPU6050 to act as our seismometer for our project, with the data being displayed on Amaseis. Our proctor demands that we quantitively assess its &quot;effectiveness&quot; through factors that we determined. We decided it is evaluated based on (this is subject to change depending on the answers to this question) proximity,  effective range, and accuracy.</p>
<p>Please, how do we determine the proximity, effective range, and accuracy of our project, and what statistical tool (as it is required to use one) do we need in order to do so?</p>
<p><em><strong>Note</strong></em>: For accuracy, we've been planning to go to our local seismological institution (PHILVOLCS) to obtain waveform data, and we ought to compare it to our data. Although, this raises many questions such as:</p>
<p>How do we quantitively compare two seismic data for accuracy, if they are taken from different locations? (and most probably different timeframes)</p>
<p>As it is probably obvious, we are very unknowledgeable in this subject, and we would VERY much appreciate any help, feedback, or recommendation. As junior high students, we are very lost on how to proceed with this now.</p>

CC BY-SA 4.0
2023-01-09T03:20:39.097Z
I would argue that the effectiveness of your sensor is related primarily to sensitivity and frequency response. From my understanding, seismic sensors measure amplitudes from 0.1 m to 1 picometer at a range of frequecies from 10 Hz to 1 miliHertz. So determining what the specifications for your sensor are is important before trying to compare waveforms to another sensor
Stevan V. Saban
<seismology><seismic><spectral-analysis>
How to quantitively compare two seismic waveform data?
antuK3n
# How to quantitively compare two seismic waveform data?

(Asked by: username_0 on 2023-01-08)

Our group has built an Arduino-based device with an MPU6050 to act as our seismometer for our project, with the data being displayed on Amaseis. Our proctor demands that we quantitively assess its "effectiveness" through factors that we determined. We decided it is evaluated based on (this is subject to change depending on the answers to this question) proximity, effective range, and accuracy.

Please, how do we determine the proximity, effective range, and accuracy of our project, and what statistical tool (as it is required to use one) do we need in order to do so?

***Note***: For accuracy, we've been planning to go to our local seismological institution (PHILVOLCS) to obtain waveform data, and we ought to compare it to our data. Although, this raises many questions such as:

How do we quantitively compare two seismic data for accuracy, if they are taken from different locations? (and most probably different timeframes)

As it is probably obvious, we are very unknowledgeable in this subject, and we would VERY much appreciate any help, feedback, or recommendation. As junior high students, we are very lost on how to proceed with this now.

**Tags:** seismology, seismic, spectral-analysis

> **Comments:**
> **username_1**: I would argue that the effectiveness of your sensor is related primarily to sensitivity and frequency response. From my understanding, seismic sensors measure amplitudes from 0.1 m to 1 picometer at a range of frequecies from 10 Hz to 1 miliHertz. So determining what the specifications for your sensor are is important before trying to compare waveforms to another sensor  

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## 1 answers

### Answer by username_2 on 2024-06-18. Score: 1

This might be too late, but here's my answer:

You can acess the fit between two waveforms by simply taking the difference at first, let's call that a residual, taking the $L\_p$ norm of the residual gives you a measure of fit between the two waveforms, the $L\_2$ norm is frequently used. You can also use a cross-correlation function, which is sensitive to the time lag between the two waveforms.

Another way of quantifying the differences between two time-series is Dynamic Time Warping (DTW for short), this has been applied to speech recognition for quite some time ([Sakoe and Chiba 1978](https://ieeexplore-ieee-org.gaelnomade-1.grenet.fr/document/1163055)). A more convenient way of doing so with seismic data has been proposed by [Hale 2013](https://library.seg.org/doi/abs/10.1190/geo2012-0327.1), the first part of the paper addresses applying DTW to seismic waveforms, the second part he applies it to seismic images (a collection of waveforms, or a gather). DTW gives a map, that indicates how to change --or warp-- one time series to match the other, also, it gives a measure of fit, in Hale 2013 it is called the DTW distance, this quantity is not a norm.

I hope this answers your question.


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