Estimation of subsurface porosities and thermal conductivities of polygonal tundra by coupled inversion of electrical resistivity, temperature, and moisture content data

Studies indicate greenhouse gas emissions following permafrost thaw will amplify current rates of atmospheric warming, a process referred to as the permafrost carbon feedback. However, large uncertainties exist regarding the timing and magnitude of the permafrost carbon feedback, in part due to uncertainties associated with subsurface permafrost parameterization and structure. Development of robust parameter estimation methods for permafrost-rich soils is becoming urgent under accelerated warming of the Arctic. Improved parameterization of the subsurface properties in land system models would lead to improved predictions and a reduction of modeling uncertainty. In this work we set the groundwork for future parameter estimation (PE) studies by developing and evaluating a joint PE algorithm that estimates soil porosities and thermal conductivities from time series of soil temperature and moisture measurements and discrete in-time electrical resistivity measurements. The algorithm utilizes the Model-Independent Parameter Estimation and Uncertainty Analysis toolbox and coupled hydrological-thermal-geophysical modeling. We test the PE algorithm against synthetic data, providing a proof of concept for the approach. We use specified subsurface porosities and thermal conductivities and coupled models to set up a synthetic state, perturb the parameters, and then verify that our PE method is able to recover the parameters and synthetic state. To evaluate the accuracy and robustness of the approach we perform multiple tests for a perturbed set of initial starting parameter combinations. In addition, we varied types and quantities of data to better understand the optimal dataset needed to improve the PE method. The results of the PE tests suggest that using multiple types of data improve the overall robustness of the method. Our numerical experiments indicate that special care needs to be taken during the field experiment setup so that (1) the vertical distance between adjacent measurement sensors allows the signal variability in space to be resolved and (2) the longer time interval between resistivity snapshots allows signal variability in time to be resolved

Understanding the relative importance of vertical and horizontal flow in ice-wedge polygons

Ice-wedge polygons are common Arctic landforms. The future of these landforms in a warming climate depends on the bidirectional feedback between the rate of ice-wedge degradation and changes in hydrological characteristics. This work aims to better understand the relative roles of vertical and horizontal water fluxes in the subsurface of polygonal landscapes, providing new insights and data to test and calibrate hydrological models. Field-scale investigations were conducted at an intensively instrumented location on the Barrow Environmental Observatory (BEO) near Utqiagvik, AK, USA. Using a conservative tracer, we examined controls of microtopography and the frost table on subsurface flow and transport within a low-centered and a high-centered polygon. Bromide tracer was applied at both polygons in July 2015 and transport was monitored through two thaw seasons. Sampler arrays placed in polygon centers, rims, and troughs were used to monitor tracer concentrations. In both polygons, the tracer first infiltrated vertically until encountering the frost table and was then transported horizontally. Horizontal flow occurred in more locations and at higher velocities in the low-centered polygon than in the high-centered polygon. Preferential flow, influenced by frost table topography, was significant between polygon centers and troughs. Estimates of horizontal hydraulic conductivity were within the range of previous estimates of vertical conductivity, highlighting the importance of horizontal flow in these systems. This work forms a basis for understanding complexity of flow in polygonal landscapes

Hydrological Parameter Estimations from a Conservative Tracer Test with Variable-Density Effects at the Boise Hydrogeophysical Research Site

Reliable predictions of groundwater flow and solute transport require an estimation of the detailed distribution of the parameters (e.g., hydraulic conductivity, effective porosity) controlling these processes. However, such parameters are difficult to estimate because of the inaccessibility and complexity of the subsurface. In this regard, developments in parameter estimation techniques and investigations of field experiments are still challenging and necessary to improve our understanding and the prediction of hydrological processes. Here we analyze a conservative tracer test conducted at the Boise Hydrogeophysical Research Site in 2001 in a heterogeneous unconfined fluvial aquifer. Some relevant characteristics of this test include: variable-density (sinking) effects because of the injection concentration of the bromide tracer, the relatively small size of the experiment, and the availability of various sources of geophysical and hydrological information. The information contained in this experiment is evaluated through several parameter estimation approaches, including a grid-search-based strategy, stochastic simulation of hydrological property distributions, and deterministic inversion using regularization and pilot-point techniques. Doing this allows us to investigate hydraulic conductivity and effective porosity distributions and to compare the effects of assumptions from several methods and parameterizations. Our results provide new insights into the understanding of variable-density transport processes and the hydrological relevance of incorporating various sources of information in parameter estimation approaches. Among others, the variable-density effect and the effective porosity distribution, as well as their coupling with the hydraulic conductivity structure, are seen to be significant in the transport process. The results also show that assumed prior information can strongly influence the estimated distributions of hydrological properties

Three-Dimensional Stochastic Estimation of Porosity Distribution: Benefits of Using Ground-Penetrating Radar Velocity Tomograms in Simulated-Annealing-Based or Bayesian Sequential Simulation Approaches

Estimation of the three-dimensional (3-D) distribution of hydrologic properties and related uncertainty is a key for improved predictions of hydrologic processes in the subsurface. However it is difficult to gain high-quality and high-density hydrologic information from the subsurface. In this regard a promising strategy is to use high-resolution geophysical data (that are relatively sensitive to variations of a hydrologic parameter of interest) to supplement direct hydrologic information from measurements in wells (e.g., logs, vertical profiles) and then generate stochastic simulations of the distribution of the hydrologic property conditioned on the hydrologic and geophysical data. In this study we develop and apply this strategy for a 3-D field experiment in the heterogeneous aquifer at the Boise Hydrogeophysical Research Site and we evaluate how much benefit the geophysical data provide. We run high-resolution 3-D conditional simulations of porosity with both simulated-annealing-based and Bayesian sequential approaches using information from multiple intersecting crosshole gound-penetrating radar (GPR) velocity tomograms and neutron porosity logs. The benefit of using GPR data is assessed by investigating their ability, when included in conditional simulation, to predict porosity log data withheld from the simulation. Results show that the use of crosshole GPR data can significantly improve the estimation of porosity spatial distribution and reduce associated uncertainty compared to using only well log measurements for the estimation. The amount of benefit depends primarily on the strength of the petrophysical relation between the GPR and porosity data, the variability of this relation throughout the investigated site, and lateral structural continuity at the site

A micromanipulation setup for comparative tests of microgrippers

A micromanipulation setup allowing comparative tests of manipulation micro tools has been developed. Repeatability measurements of positioning as well as optimization of manipulation conditions can be run with parts of typically 5 to 50μm over a large set of parameters including environment conditions, substrate and tip specifications, and different strategies (robot trajectories at picking and releasing time). The workstation consists of a high precise parallel robot, the Delta3, to position the gripper, linear stages to place the parts in the field of view and two microscopes for the visual feedback and position measurement. The setup is placed in a chamber for controlling relative humidity and temperature. An interface was developed to integrate every kind of tool on the robot. Automated operations and measurement have been carried out based on localization and tracking of micro objects and gripper. Integration of micro tools was successfully accomplished and comparative tests were executed with micro tweezers. Sub micrometer position repeatability was achieved with a success rate of pick and pick operations of 95%

Characterization of micro manipulation tasks operated with various controlled conditions by microtweezers

Micro manipulation tasks with micro tweezers were operated in different configurations. This paper discusses the main issues of pick and place operations with micro tweezers as geometric consideration, grasping force and quality of the contact surfaces. This study is based on positioning repeatability measurements and success rate of the tasks operated automatically on our micro manipulation setup. Results for a MEMS micro gripper show a high reliability of more than 90% of success rate and positioning repeatability under the micrometer

Micro-gripper à haute dynamique

Dans le monde microscopique, la force de gravité devient négligeable par rapport aux forces d’adhésion (capillarité, Van der Waals). Ce projet vise à utiliser ces forces pour la prise de bille d’une taille caractéristique de 50[μm]. La dépose quand à elle s’effectue de manière dynamique, en utilisant l’inertie de la bille soumise à une forte accélération. Le but de ce projet est de caractériser la prise (taux de succès) et la dépose (seuil d’accélération, taux de succès, précision et répétabilité). La prise dépend essentiellement du type de matériaux utilisés ; sa caractérisation a été faite pour un gripper en silicium et un gripper en verre dans l’air ambiant (20% humidité relative) et l’azote (3%). Pour fournir une accélération, un piézoélectrique est utilisé (cristal qui se déforme lorsqu’une tension est appliquée à ses bornes). L’avantage principal est que la déformation est bien contrôlable en intensité et direction. La dépose quand à elle a été caractérisée dans les mêmes conditions que la prise et en excitant le piézoélectrique avec des sinus continus ou en créant une seule impulsion Au final, il a été montré que cette technique est viable pour la micromanipulation. Toutefois des points clés nécessaires à un bon contrôle des opérations ont été identifiés, notamment au niveau de la rigidité du substrat pour éviter l’écrasement des objets et au niveau de la qualité des surfaces de contact

Coupled land surface–subsurface hydrogeophysical inverse modeling to estimate soil organic carbon content and explore associated hydrological and thermal dynamics in the Arctic tundra

Quantitative characterization of soil organic carbon (OC) content is essential due to its significant impacts on surface–subsurface hydrological–thermal processes and microbial decomposition of OC, which both in turn are important for predicting carbon–climate feedbacks. While such quantification is particularly important in the vulnerable organic-rich Arctic region, it is challenging to achieve due to the general limitations of conventional core sampling and analysis methods, and to the extremely dynamic nature of hydrological–thermal processes associated with annual freeze–thaw events. In this study, we develop and test an inversion scheme that can flexibly use single or multiple datasets – including soil liquid water content, temperature and electrical resistivity tomography (ERT) data – to estimate the vertical distribution of OC content. Our approach relies on the fact that OC content strongly influences soil hydrological–thermal parameters and, therefore, indirectly controls the spatiotemporal dynamics of soil liquid water content, temperature and their correlated electrical resistivity. We employ the Community Land Model to simulate nonisothermal surface–subsurface hydrological dynamics from the bedrock to the top of canopy, with consideration of land surface processes (e.g., solar radiation balance, evapotranspiration, snow accumulation and melting) and ice–liquid water phase transitions. For inversion, we combine a deterministic and an adaptive Markov chain Monte Carlo (MCMC) optimization algorithm to estimate a posteriori distributions of desired model parameters. For hydrological–thermal-to-geophysical variable transformation, the simulated subsurface temperature, liquid water content and ice content are explicitly linked to soil electrical resistivity via petrophysical and geophysical models. We validate the developed scheme using different numerical experiments and evaluate the influence of measurement errors and benefit of joint inversion on the estimation of OC and other parameters. We also quantify the propagation of uncertainty from the estimated parameters to prediction of hydrological–thermal responses. We find that, compared to inversion of single dataset (temperature, liquid water content or apparent resistivity), joint inversion of these datasets significantly reduces parameter uncertainty. We find that the joint inversion approach is able to estimate OC and sand content within the shallow active layer (top 0.3 m of soil) with high reliability. Due to the small variations of temperature and moisture within the shallow permafrost (here at about 0.6 m depth), the approach is unable to estimate OC with confidence. However, if the soil porosity is functionally related to the OC and mineral content, which is often observed in organic-rich Arctic soil, the uncertainty of OC estimate at this depth remarkably decreases. Our study documents the value of the new surface–subsurface, deterministic–stochastic inversion approach, as well as the benefit of including multiple types of data to estimate OC and associated hydrological–thermal dynamics

Characterization of an inertial micro gripper based on adhesion forces

Adhesive forces become predominant in the micro world comparing to the gravity effect implying the development of new micro manipulation strategies. This paper presents the design and conception of a gripper that use the inertial principle for the release (applying a high acceleration, in the order of 10’000g) and the adhesion for catching a micro part of 50μm with the goal of precisely control the position after release. Experiments were conducted and showed a positioning repeatability of 2μm to 6μm depending on the relative humidity with a success rate of more than 90%