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Abstract

We used optical and radar remote sensing datasets to map, estimate the volume, and measure the surface displacements of lava flows emplaced on the flanks of Volcán de Colima, Mexico by extrusion of lava dome material from the end of 2014 to early 2016. Our main result is that the flow motion of the lava contributes significantly to the recorded displacements several months after its emplacement. First, we mapped the deposits and estimated their volumes using two Digital Elevation Models (DEM), one derived from radar data acquired before the peak of activity and one derived from optical images acquired just after this peak of activity (Fig. 1). Coherence information derived from the radar dataset added some temporal constraints on the timing of emplacement of various deposits. We thus estimated a mean extrusion rate of 1–2 m3 s−1 between November 2014 and February 2015. We then used a new approach to reconstruct the 3D displacement field, taking advantage of images acquired by the same satellite, on both ascending and descending tracks, and using a physical a priori on the direction of horizontal displacements (Fig. 2). Our results show that about 2 cm yr−1 of horizontal motion is still recorded a few months after the emplacement on the SW lava flow, which is the only one covered by the two-acquisition geometries. In order to differentiate the potential causes of the observed displacements, we modeled the thermal contraction of the lava flow using a finite element numerical method. Removing the contribution of thermoelastic contraction from the measured displacements enable to infer both the viscoelastic loading and flow motion effects from the residuals. Results show that, thermal contraction, flow motion and viscoelastic loading contribute significantly to the displacements recorded (Fig. 3).

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Figure 1: Deposit thickness and Volcán de Colima slope [A] Vertical difference between two DEMs acquired, respectively, from 2011 to 2014 (TanDEM-X) and in January 2016 (Pleiades). Green lines are for lava flow edges derived from elevation changes. Black lines display thickness contour with an interval of 10 m. [B] Local topographic slope (the local orientations of maximum slope are indicated with short black lines) computed from the TanDEM-X DEM.

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Figure 2: 3D displacement field of the SW lava flow [A] Mean vertical displacement rate (from August 2015 to February 2016). Positive values are for an upward displacement. Green areas represent places affected by very low resolution or layover on one of both tracks. The hatched square is the reference area where displacements are assumed to be null. The red rectangle shows the extent of the zoom displayed in (B). [B] Zoom of the SW lava flow with horizontal motion vectors. Circles represent the uncertainties on displacement vectors. Dashed black lines mark the edges of lava flows derived from DEMs difference. Note that whereas the horizontal displacements are negligible outside the lava flow, the vertical ones remain significant around the lava flow due to loading effects.

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Figure 3: Lava flow cooling and contraction using numerical modeling [A] Conceptual model used for thermal contraction. The solid blue vector indicates the expected surface displacement associated with thermo-elastic compaction. The dashed blue vectors represent its projection on the vertical and horizontal directions. The green arrow corresponds to the 1D direction considered in the numerical model and to the section represented in (B). [B] Evolution of the different increments of the lava flow deformations in the vertical direction. The blue area corresponds to the vertical viscoelastic displacement imposed by the load (this displacement is induced both on and around the lava flow area). The red and green areas indicate the displacement rate resulting from for the thermal contraction and lava downward motion, respectively.

Related publication: Carrara, A., Pinel, V., Bascou, P., Chaljub, E., De la Cruz-Reyna, S., 2019. Post-emplacement dynamics of andesitic lava flows at Volcán de Colima, Mexico, revealed by radar and optical remote sensing data. Journal of Volcanology and Geothermal Research, 381, 1–15. https://doi.org/10.1016/j.jvolgeores.2019.05.019