Forschungszentrum Landschaftsentwicklung und Bergbaulandschaften (FZLB)
Refine
Year of publication
- 2020 (6) (remove)
Document Type
Language
- English (6)
Keywords
- ecosystem development (1)
- morphometry (1)
- post-mining area (1)
- primary succession (1)
Institute
In recent years, probabilistic solution to the inversion of electromagnetic induction (EMI) data has been progressively developed for non-invasive subsurface characterization. However, Bayesian inversion of EMI data using forward solvers based on full solution ofMaxwell's equation is associated with computationally expensive modelings, particularly for large-scale surveys. Here, we incorporated artificial neural network (ANN) with Bayesian inference to obtain subsurface electromagnetic conductivity image (EMCI) fromEMI data down to 10mdepth. In this respect, a complex EMI forward model was replaced by a trained neural network (ANN proxy forward function) that can be evaluated comparably rapidly. The accuracy of the ANN-based forward solver was examined using different synthetic subsurface models. The proposed methodology was applied on EMI data measured with a DUALEM-421 s sensor from 10 ha study site in the Alken Enge area of Denmark. We compared the inversely estimated EMCI with the counterpart obtained from a quasi-three-dimensional (quasi-3D) spatiallyconstrained deterministic algorithm as a standard code. The network training procedure was performed within fewminutes, and once itwas trained, the ANN-based forward solver returned roughly 150,000 model responses per second. This value for the EMI forward solverwas around 400, demonstrating the computational efficiency of the ANN proxy forward function. The theoretical simulations demonstrated that the ANN-based forward solver accurately mimics the EMI response within the training range. Moreover, the proposed inversion strategy successfully delineated the subsurface EMCI from Alken Enge area. This approach thus facilitates rapid and accurate subsurface conductivity imaging using Bayesian inversion of multi-configuration EMI data, which is particularly pertinent for large-scale measurements.
Conventional geophysical inversion techniques suffer from several limitations including computational cost, nonlinearity, non-uniqueness and dimensionality of the inverse problem. Successful inversion of geophysical data has been a major challenge for decades. Here, a novel approach based on deep learning (DL) inversion via convolutional neural network (CNN) is proposed to instantaneously estimate subsurface electrical conductivity (σ) layering from electromagnetic induction (EMI) data. In this respect, a fully convolutional network was trained on a large synthetic data set generated based on 1-D EMI forward model. The accuracy of the proposed approach was examined using several synthetic scenarios. Moreover, the trained network was used to find subsurface electromagnetic conductivity images (EMCIs) from EMI data measured along two transects from Chicken Creek catchment (Brandenburg, Germany). Dipole–dipole electrical resistivity tomography data were measured as well to obtain reference subsurface σ distributions down to a 6 m depth. The inversely estimated models were juxtaposed and compared with their counterparts obtained from a spatially constrained deterministic algorithm as a standard code. Theoretical simulations demonstrated a well performance of the algorithm even in the presence of noise in data. Moreover, application of the DL inversion for subsurface imaging from Chicken Creek catchment manifested the accuracy and robustness of the proposed approach for EMI inversion. This approach returns subsurface σ distribution directly from EMI data in a single step without any iterations. The proposed strategy simplifies considerably EMI inversion and allows for rapid and accurate estimation of subsurface EMCI from multiconfiguration EMI data.
Open-cast coal-mining creates a severe disturbance of ecological systems, resulting in post-mining areas that have lost their soils entirely and are exposed to extreme conditions. Nonetheless, these areas allow us to investigate the process of primary succession. We studied the first 12 years of soil tardigrade succession at ‘Chicken Creek’, an experimental site within the open-cast coal-mining area in Welzow Süd, Germany. A total of ten tardigrade genera and 13 species or species groups were identified at the site. Over time, the organic carbon content, proportion of silt and nutrient availability in the soil increased, and the soil particle size distribution and pH value changed significantly. The vegetation cover and nematode abundance increased, whereas tardigrade densities peaked in 2008 and decreased thereafter, as did their mean body size. Species replacement was obvious within this 12 year study period, and tardigrade species diversity increased, indicating that ecological niche diversity also increased throughout this time. Vegetation cover, soil particle size distribution and time were the most important factors influencing the tardigrade community. We also discuss further potential factors and the ability of terrestrial tardigrades to persist despite many environmental constraints, such as fluctuations in pH and moisture, and to colonize new habitats faster than most other animal groups.