The salt mining industrial exploitation located in Vauvert (France) has been injecting water at high pressure into wells to dissolve salt layers at depth. The extracted brine has been used in the chemical industry for more than 30 years, inducing a subsidence of the surface. Yearly leveling surveys have monitored the deformation since 1996. This dataset is supplemented by synthetic aperture radar (SAR) images, and since 2015, global navigation satellite system (GNSS) data have also continuously measured the deformation. New wells are regularly drilled to carry on with the exploitation of the salt layer, maintaining the subsidence. We make use of this careful monitoring by inverting the geodetic data to constrain a model of deformation. As InSAR and leveling are characterized by different strengths (spatial and temporal coverage for InSAR, accuracy for leveling) and weaknesses (various biases for InSAR, notably atmospheric, very limited spatial and temporal coverage for leveling), we choose to combine SAR images with leveling data, to produce a 3-D velocity field of the deformation. To do so, we develop a two-step methodology which consists first of estimating the 3-D velocity from images in ascending and descending acquisition of Sentinel 1 between 2015 and 2017 and second of applying a weighted regression kriging to improve the vertical component of the velocity in the areas where leveling data are available. GNSS data are used to control the resulting velocity field. We design four analytical models of increasing complexity. We invert the combined geodetic dataset to estimate the parameters of each model. The optimal model is made of 21 planes of dislocation with fixed position and geometry. The results of the inversion highlight two behaviors of the salt layer: a major collapse of the salt layer beneath the extracting wells and a salt flow from the deepest and most external zones towards the center of the exploitation.

中文翻译：

---

结合InSAR和找平数据进行多源反演，监测Vauvert（法国）深盐开采的地表变形

位于法国Vauvert的盐矿工业开发区一直在向井中注入高压水以溶解深层的盐层。提取的盐水已经在化学工业中使用了30多年，导致表面下陷。自1996年以来，每年进行的水准测量都对变形进行了监测。此数据集辅以合成孔径雷达（SAR）图像，自2015年以来，全球导航卫星系统（GNSS）数据也不断测量了变形。定期钻新井以继续开采盐层，以保持沉降。我们通过倒转大地测量数据来约束变形模型来利用这种仔细的监视。由于InSAR和水准仪具有不同的优势（InSAR的时空覆盖范围，精确度）和弱点（InSAR的各种偏差，尤其是大气，用于水平度的空间和时间覆盖非常有限），我们选择将SAR图像与水平度数据相结合，以生成变形的3-D速度场。为此，我们开发了一种分两步进行的方法，该方法首先包括根据2015年至2017年间Sentinel 1的升序和降幅采集的图像估算3-D速度，然后应用加权回归克里金法改善图像的垂直分量。可获得整平数据的区域的速度。GNSS数据用于控制所得的速度场。我们设计了四个日益复杂的分析模型。我们对组合的大地测量数据集进行求逆，以估计每个模型的参数。最佳模型由具有固定位置和几何形状的21个位错平面组成。反演的结果突出了盐层的两种行为：萃取井下方盐层的严重塌陷，以及盐从最深，最外部的区域流向开采中心。