# Routine Processing and Automatic Detection of Volcanic Ground Deformation Using Sentinel-1 InSAR Data: Insights from African Volcanoes

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## Abstract

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## 1. Introduction

## 2. Materials and Methods

#### 2.1. Pre-Processing of Sentinel-1 Data

#### 2.2. Atmospheric Noise

#### 2.3. Reference Frame

#### 2.4. Time Series Characterisation

#### 2.4.1. Time Series Functions

#### 2.4.2. Velocity Uncertainties

## 3. Results

#### 3.1. Atmospheric and Reference Frame Uncertainties

#### 3.2. Analysis of Deformation Signals

#### 3.2.1. Signal-To-Noise Ratio

#### 3.2.2. Deformation Characteristics

#### 3.3. Velocity Uncertainties

## 4. Discussion

#### 4.1. Automated Detection Based on Signal-To-Noise Ratio

- At Adwa and Ayelu, the velocities recorded at point B are 0.1 cm/yr, which is in the same order of magnitude as our estimates of LOS velocity uncertainties. In addition, part of the signal is correlated with topography, which suggests tropospheric residuals signals.
- At O’a caldera, the phase information is very sparse as the volcanic area is covered by three lakes (Shala, Abijata and Langano). Ground displacements has been only retrieved at the caldera rim and LOS velocities do not exceed 0.2 cm/yr. No clear deformation signal has been observed.
- At Longonot, the temporal noise is ∼2 cm, which is larger than the mean value calculated for the EARS. The signal detected has a long wavelength, and it can be observed in the entire scene ($50\times 50$ km), with LOS rates of displacement ranging from 0.5 cm/yr in the NW to −0.5 cm/yr in the SE. This pattern may suggest residual orbital ramps.
- At Ma Alalta, the signal is located on Quaternary rhyolite lava flows [46]. The maximum rate of LOS displacement is about −0.7 cm/yr, which is larger than the four cases previously described. Therefore, it is difficult to determine if this signal is noise or real ground deformation associated with the compaction or remobilization of volcanic products as previously observed in Kone lava flow [9].

#### 4.2. Previously Unreported Deformation

#### 4.3. Deformation Classification

#### 4.4. Measurement Uncertainty

#### 4.4.1. Regional Detection Threshold

#### 4.4.2. Velocity Errors for Long-Term Signals

## 5. Conclusions

## Supplementary Materials

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**Flowchart showing the important steps for the automatic production and classification of Sentinel-1 time series.

**Figure 2.**Cumulative Distribution Function (CDF) of the phase-elevation correlation values (${R}^{2}$) recorded for each volcano at different location along the rift: (

**a**) Afar, (

**b**) Main Ethiopia Rift, and (

**c**) Kenya-Tanzania. Blue lines indicate volcanoes with low phase-elevation correlation (CDF(0.5) > 0.8) and red lines indicate volcanoes with high phase-elevation correlation (CDF(0.5) < 0.8), (

**d**) map showing the degree of phase-elevation correlation through the value CDF(0.5) for all the volcanoes processed. All volcanoes showed with red triangles were corrected using phase-elevation correlation, (

**e**) distribution of the phase-elevation correlation along the EARS.

**Figure 3.**Atmospheric corrections using the empirical method of linear phase-elevation correlation. The top row shows an example for Silali volcano showing high correlation (${R}^{2}$ = 0.8) with (

**a**) the interferogram, (

**b**) the phase-elevation model, (

**c**) the corrected interferogram and (

**d**) the phase-elevation plot. The bottom row shows an example for Suswa volcano showing low correlation (${R}^{2}$ = 0.04) with (

**e**) the interferogram, (

**f**) the phase-elevation model, (

**g**) the corrected interferograms and (

**h**) the phase-elevation plot.

**Figure 4.**Distribution of the LOS velocity estimates at the centre of the volcanoes (

**a**) Dallol, (

**b**) Nabro and (

**c**) South island, using all the pixels outside the volcanoes as a potential reference point. Vertical black lines indicate the peak of frequency. The standard deviation of the distribution (1$\sigma $) is an indication of the velocity uncertainty due to the choice of the reference point, (

**d**) map showing the LOS velocity uncertainty for all the volcanoes processed, (

**e**) distribution of the uncertainty along the EARS.

**Figure 5.**Limit of detection of displacements for the different sections of the rift: (

**a**) Afar, (

**b**) MER and (

**c**) Kenya-Tanzania. The signal amplitude corresponds to the absolute value of the cumulative displacement recorded by the time series after 5-years at the location (B). Temporal noise corresponds to the standard deviation of the 5-years time series at the location (A) outside the deformation. The red lines show the two detection thresholds: $|{A}_{B}|=2{\sigma}_{A}$ and $|{A}_{B}|=3{\sigma}_{A}$. Volcanic centres with amplitude above $3{\sigma}_{A}$ are automatically classified as deformed volcanoes whereas volcanoes with amplitude between $2{\sigma}_{A}$ and $3{\sigma}_{A}$ need manual inspection. Arrows indicate the mean LOS velocity for the year 2019. Gray ellipse indicates cases for which one ground signal is identified on different neighboured volcanic centres. The symbol (*) indicates volcanic centres where phase-elevation corrections have been applied. Vertical dashed lines indicate the average temporal noise for each section of the rift, (

**d**) minimum LOS velocity that will be detected as a function of the duration of the time series, assuming the threshold of 2${\sigma}_{A}$. Solid lines are derived from the mean value of the temporal noise found in (

**a**–

**c**) and dashed lines indicate the confidence intervals within 1 standard deviation.

**Figure 6.**Examples of nine time series of LOS displacement showing the fit for the two functions: linear (blue) and sigmoid (red). Linear trend is preferred for the volcanoes (

**a**) Dallol, (

**b**) Gada Ale, and (

**c**) Corbetti whereas sigmoid trend best fit for (

**d**) Fentale, (

**e**) Suswa, (

**f**) Erta Ale, (

**g**) Tullu Moje, (

**h**) Kone and (

**i**) Olkaria.

**Figure 7.**LOS velocity uncertainties ${\sigma}_{v}$ as a function of the duration of the InSAR time series $\Delta t$ (log-log scale) for the seven volcanoes showing persistent linear displacements for the period 2015–2020: Dabbahu, Alu-Dallafilla, Dallol, Gada Ale, Corbetti, Paka and Silali. Linear trends are an indication of a power-law relationship: ${\sigma}_{v}$ = $a\Delta {t}^{-b}$, where a corresponds to the LOS velocity uncertainties after a 1-year period and b the decay rate (Table 2).

**Figure 8.**LOS displacement detected at the two Kenyan volcanoes, (

**a**–

**c**) Silali and (

**d**–

**f**) Paka, (

**a**,

**d**) wrapped cumulative displacements (LOS) for the 5-year time period (2015–2020), (

**b**,

**e**) time series of LOS displacements at the location B, (

**c**,

**f**) comparison between the profiles of displacements (red lines) and the topography (shaded area) along W-E (left) and S-N (right) directions.

**Figure 9.**Map showing the ratio between the amplitude of the cumulative displacements and the temporal noise for the 64 EARS volcanoes analysed. Deformation signals are automatically detected for 16 volcanoes having a ratio exceeding 3 (colour triangles). Small panels show individual time series of LOS displacements for each deformed volcano in which temporal evolution is fitted either by linear (blue), sigmoid (red) or seasonal (pink) functions.

**Table 1.**Statistical values (mean, median, standard deviation, minimum and maximum values) associated with the temporal noise ${\sigma}_{A}$ for each of the three EARS regions (Afar, MER, Kenya-Tanzania). Units are in centimetres.

Region | Nb of Volc. | Mean | Median | Std | Min/Max |
---|---|---|---|---|---|

Afar | 31 | 1.4 | 1.3 | 0.5 | 0.7/2.8 |

MER | 16 | 1.2 | 1.1 | 0.4 | 0.2/2.2 |

Kenya-Tanzania | 17 | 1.4 | 1.4 | 0.5 | 0.4/2.2 |

**Table 2.**Values of the signal-to-noise ratio and the best-fit model for InSAR time series of all volcanoes where $|{A}_{B}|>3{\sigma}_{A}$.

Volcano | Detection | Model | Sigmoid | ${\mathit{R}}^{2}>0.75$ | |||
---|---|---|---|---|---|---|---|

$|{\mathit{A}}_{\mathit{B}}|$ (cm) | ${\mathit{\sigma}}_{\mathit{A}}$ (cm) | $|{\mathit{A}}_{\mathit{B}}|/{\mathit{\sigma}}_{\mathit{A}}$ | $\mathsf{\Delta}\mathit{A}\mathit{I}\mathit{C}$ | ${\mathit{t}}_{\mathit{c}}$ (Days) | $\mathit{\tau}$ (Days) | ${\mathit{U}}_{\mathit{m}\mathit{a}\mathit{x}}$ (cm) | |

Fentale | 5.8 | 1.0 | 5.8 | −133 | 20150422 | 43 | 8.3 |

Erta Ale | 18.0 | 1.9 | 9.5 | −86 | 20170626 | 146 | −19.0 |

Tullu Moje | 14.7 | 1.0 | 14.7 | −61 | 20161130 | 212 | 13.4 |

Suswa | 5.5 | 1.0 | 5.5 | −49 | 20181025 | 64 | 6.2 |

Kone | 6.1 | 1.2 | 5.1 | −31 | 20190329 | 426 | −8.2 |

Olkaria | 7.9 | 1.4 | 5.6 | −10 | 20170429 | 261 | −10.7 |

Model | Linear | ${\mathit{R}}^{\mathbf{2}}>\mathbf{0.75}$ | |||||

$\mathsf{\Delta}\mathit{A}\mathit{I}\mathit{C}$ | ${\mathit{v}}_{\mathit{B}}$ (cm/yr) | ${\mathit{\sigma}}_{\mathit{B}}$ (cm/yr) | $\mathit{b}$ | ||||

Corbetti | 26.1 | 1.6 | 16.3 | −4 | 4.6 | 0.1 | 1.47 |

Alu−Dallafilla | 6.10 | 1.5 | 4.1 | −3 | −1.3 | 0.1 | 1.53 |

Silali | 3.4 | 0.4 | 8.5 | −2 | −0.7 | 0.05 | 1.35 |

Dallol | 14.6 | 1.2 | 12.2 | 10 | −3.2 | 0.1 | 1.45 |

Paka | 4.4 | 0.5 | 8.8 | 11 | −0.7 | 0.06 | 1.61 |

Gada Ale | 9.9 | 1.4 | 7.1 | 21 | −1.9 | 0.1 | 1.45 |

Dabbahu | 22.0 | 2.7 | 8.1 | 23 | 3.9 | 0.2 | 1.65 |

Model | Linear | + Seasonal | |||||

${\mathit{v}}_{\mathit{B}}$ (cm/yr) | ${\mathit{A}}_{\mathit{s}}$ (cm) | ||||||

Nabro | 5.5 | 1.2 | 4.6 | 1.7 | 2.9 | ||

Alutu | 3.8 | 0.2 | 19 | −0.8 | 2.1 | ||

Haledebi | 5.8 | 1.3 | 4.5 | 0.4 | 2.1 |

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**MDPI and ACS Style**

Albino, F.; Biggs, J.; Lazecký, M.; Maghsoudi, Y.
Routine Processing and Automatic Detection of Volcanic Ground Deformation Using Sentinel-1 InSAR Data: Insights from African Volcanoes. *Remote Sens.* **2022**, *14*, 5703.
https://doi.org/10.3390/rs14225703

**AMA Style**

Albino F, Biggs J, Lazecký M, Maghsoudi Y.
Routine Processing and Automatic Detection of Volcanic Ground Deformation Using Sentinel-1 InSAR Data: Insights from African Volcanoes. *Remote Sensing*. 2022; 14(22):5703.
https://doi.org/10.3390/rs14225703

**Chicago/Turabian Style**

Albino, Fabien, Juliet Biggs, Milan Lazecký, and Yasser Maghsoudi.
2022. "Routine Processing and Automatic Detection of Volcanic Ground Deformation Using Sentinel-1 InSAR Data: Insights from African Volcanoes" *Remote Sensing* 14, no. 22: 5703.
https://doi.org/10.3390/rs14225703