@misc{MoghadasBadorreck, author = {Moghadas, Davood and Badorreck, Annika}, title = {Characterization of soil electrical conductivity from Chicken Creek Catchment using deep learning inversion of geophysical data}, series = {EGU General Assembly 2020, Online, 4-8 May 2020}, journal = {EGU General Assembly 2020, Online, 4-8 May 2020}, doi = {10.5194/egusphere-egu2020-2664}, language = {en} } @inproceedings{MoghadasVrugt, author = {Moghadas, Davood and Vrugt, Jasper A.}, title = {Non-invasive characterization of soil conductivity structure using probabilistic inversion and dimensionality reduction approach}, series = {European Geosciences Union, General Assembly 2018, Vienna, Austria}, booktitle = {European Geosciences Union, General Assembly 2018, Vienna, Austria}, publisher = {European Geophysical Society}, address = {Katlenburg-Lindau}, abstract = {Low frequency loop-loop electromagnetic induction (EMI) is widely used for monitoring soil electrical conductivity and water content. As a non-invasive geophysical technique, EMI allows for rapid and real-time electrical conductivity measurements. However, EMI has not yet been used much to back out the vertical (depth profile) conductivity structure due to problems with the inversion of measured apparent electrical conductivity (ECa) data. In this study, we used Bayesian inference with the MT-DREAM(ZS) algorithm to infer the electrical conductivity layering of the subsurface from EMI data.We test and evaluate our methodology using apparent electrical conductivity data measured along two transects in the H{\"u}hnerwasser catchment in Lusatia, Germany. These measurements were made using CMD-Explorer, a multi-configuration sensor with three inter-coil spacings and two antenna orientations. Three offsets and two antenna modes lead to six measurement depths. Electrical Resistivity Tomography (ERT) measurements were also carried out to provide reference conductivity values and to calibrate the EMI data. Such calibration is necessary for quantitative interpretation of the ECa values and to enable multi-layered inversion. The Discrete Cosine Transform (DCT) was used to reduce the number of unknown parameters, and different likelihood functions were used to evaluate the sensitivity of the posterior parameter distribution to residual assumptions. DCT-based inversion equates to a quasi-two-dimensional framework which incorporates all data along the profile and results in a low-dimensional over-determined optimization problem. Results demonstrated that although appropriate selection of the low frequency DCT coefficients is important, the definition of the likelihood function plays a crucial role in the estimation of parameter and predictive uncertainty. The use of a Gaussian likelihood function introduces artifacts in DCT-based inversion of EMI data. The use of a more flexible likelihood function results in more accurate results of the DCT-inversion. Integration of the DCT with the MT-DREAM(ZS) algorithm and a flexible generalized likelihood function appears promising for the inversion of low frequency loop-loop EMI data. The proposed approach promises accurate and high resolution estimation of subsurface hydrogeophysical properties from EMI data.}, language = {en} } @inproceedings{Moghadas, author = {Moghadas, Davood}, title = {High-Resolution Soil Electrical Conductivity Imaging from EMI D Based Probabilistic Inversion}, series = {24th European Meeting of Environmental and Engineering Geophysics, 9-13 September Porto, Portugal}, booktitle = {24th European Meeting of Environmental and Engineering Geophysics, 9-13 September Porto, Portugal}, abstract = {Electromagnetic induction (EMI) sensors allow for non-invasive soil characterizations. Proximal soil sensing using EMI hindered due to the problems related to the inversion of apparent electrical conductivity (ECa) data. In this study, I used Bayesian inference to obtain the electrical conductivity layering of the subsurface from multi-configuration EMI data. In this respect, generalized formal likelihood function was used to more accurately describe the sensitivity of the posterior parameter distribution to residual assumptions. Discrete Cosine Transform (DCT) was employed as a model compression technique to reduce the number of unknown parameters in the inversion. I considered apparent electrical conductivity pseudosection as a training image (TI) in multiple-point statistical simulations. Information from TI realizations were utilized to determine dominant DCT coefficients, as well as prior probability density functions for the subsequent probabilistic inversions. The potentiality of the proposed approach was examined through an experimental scenario. The results demonstrated that this methodology allows for soil electrical conductivity imaging with high resolution. This strategy permits to incorporate summary metrics from ensemble of ECa pseudosection realizations in the inversion without resorting to any complimentary source of information. The proposed approach ensures accurate and high resolution characterization of soil conductivity layering from measured ECa values.}, language = {en} } @inproceedings{NasutiNasutiMoghadas, author = {Nasuti, Aziz and Nasuti, Yasin and Moghadas, Davood}, title = {Enhancing Potential Field Data Using TDY Filter}, series = {Near Surface Geoscience Conference \& Exhibition, 9-12 September 2018, Porto, Portugal}, booktitle = {Near Surface Geoscience Conference \& Exhibition, 9-12 September 2018, Porto, Portugal}, abstract = {Potential field methods produce anomaly maps with different magnitudes and depths that are typically contaminated by noise, making them hard to interpret. In order to highlight edges of the anomalies with different depths and magnitudes, data filtering techniques have received a great attention, in particular for mineral explorations. Filtering approaches render to explore more details from potential field data maps. In this respect, high pass filters are commonly used for enhancing the anomaly edges all of which utilize gradients of the potential field. In order to apply different filters on the potential field data, major attempts have been made to make a balance between noise and the signal obtained from a filtered image (Cooper \& Cowan, 2006).}, language = {en} } @misc{MoghadasSchaafGerwinetal., author = {Moghadas, Davood and Schaaf, Wolfgang and Gerwin, Werner and Badorreck, Annika and H{\"u}ttl, Reinhard F.}, title = {A web-based platform for terrestrial data repository from Chicken Creek catchment}, series = {Earth Science Informatics}, volume = {12}, journal = {Earth Science Informatics}, number = {4}, issn = {1865-0481}, doi = {10.1007/s12145-019-00385-0}, pages = {671 -- 684}, abstract = {Exploring hydrological and ecological processes plays a key role in understanding ecosystem developments. In this respect, the constructed catchment, Chicken Creek, has been established for fundamental and interdisciplinary scientific research. Since 2005, an ongoing monitoring program has been launched to measure hydrological, biological, meteorological, and pedological parameters during the ecological development of the site. This comprehensive and multidisciplinary monitoring program has produced a diverse large data set. Handling such complex data for research purposes can be a cumbersome task. Consequently, we developed an online data portal (https://www.b-tu.de/chicken-creek/apps/datenportal/) to efficiently handle the data from Chicken Creek catchment. The portal was constructed using Shiny package of the R programming language. This platform provides a web-based data repository allowing for data discovery, download, visualization, and analysis. The data include time series of different parameters from installed sensors, data from laboratory analyses, vegetation data, data from sampling campaigns, and aerial photos. This platform demonstrates the relevancy and potentiality of the R-Shiny for constructing an online data portal to be used for multidisciplinary scientific purposes. The Chicken Creek data portal thus provides a comprehensive and reliable database to give scientists a fast and easy access to all collected data.}, language = {en} } @misc{BadorreckSchaafGerwinetal., author = {Badorreck, Annika and Schaaf, Wolfgang and Gerwin, Werner and Moghadas, Davood and H{\"u}ttl, Reinhard F.}, title = {Long-term monitoring program at the constructed catchment "Chicken Creek"}, series = {Geophysical Research Abstracts}, volume = {21}, journal = {Geophysical Research Abstracts}, pages = {1}, abstract = {Ecosystems are characterized by high complexity and are dynamically changing by abiotic and biotic drivers such as climate, vegetation and soil fauna. Due to feedback processes between compartments, ecosystems show an inherent degree of locally heterogeneous properties and structures at multiple spatial and temporal scales. The constructed catchment "Chicken Creek" represents a unique and outstanding site to study an ecosystem at the initial stage of development. The catchment is located about 20 km south of the city of Cottbus (Germany). The 6-ha catchment area was built in 2005 of coarse-textured quaternary sediments from the adjacent lignite mine and forms a back- and foot-slope that flattens out to a pond. A clay liner of 2-3 m thickness seals the catchment at the bottom, allowing for the formation of a local water body fed by precipitation. After construction the site was left to an unrestricted and unmanaged succession to elucidate the role of structures und processes during the initial establishment of an ecosystem. To study processes of ecosystem development and newly emerging structures the Chicken Creek catchment is equipped with a comprehensive ecological monitoring network. A network of more than 40 wells and two weirs allow for recording groundwater levels, surface runoff and discharge. Meteorological parameters are recorded at three weather stations. Annual aerial photos and vegetation monitoring reveal the floral colonisation of the area. Here, we present the 12 years of diverse and extensive monitoring program which has been launched to measure hydrological, biological, meteorological, and pedological parameters during the ecological development of the catchment area.}, language = {en} } @misc{MoghadasSchaafGerwinetal., author = {Moghadas, Davood and Schaaf, Wolfgang and Gerwin, Werner and Badorreck, Annika and H{\"u}ttl, Reinhard F.}, title = {Chicken Creek data portal: a web-based R-Shiny platform for terrestrial data repository}, series = {Geophysical Research Abstracts}, volume = {21}, journal = {Geophysical Research Abstracts}, pages = {1}, abstract = {Exploring hydrological and ecological processes plays a key role in understanding ecosystem development. In this respect, the constructed catchment, Chicken Creek (H{\"u}hnerwasser), has been established to promote fundamental and interdisciplinary scientific research. Since 2005, an on-going monitoring program has been established in this catchment to measure hydrological, biological, meteorological, and pedological parameters during the ecological development of the site. This comprehensive and multidisciplinary monitoring program has produced a large and diverse data set. Managing and exploring such a complex data set for research purposes can be a cumbersome task. As a consequence, we developed an online data portal https://www.b-tu.de/chicken-creek/apps/datenportal/ to efficiently handle the data from Chicken Creek catchment. The portal was constructed using R programming language, Shiny package, and the accompanying local Linux server. This platform allows for efficient data discovery, download, visualization, and analysis. Data visualizations are freely available, while data storage is limited to the authorized users. In comparison with the commonly used data base tools, R-Shiny offers several advantages. As an open source package, this platform allows for constructing a web data platform in an interactive way taking into account the complexity and diversity of the data. The flexibility of this routine enables to make an efficient user-demand data portal rather than relying on predetermined outputs. It also offers flexibility in data handling by using many different R packages, rendering versatile and extensive functionalities. R-Shiny is particularly powerful in terms of statistical analyses. Although the Chicken Creek online data portal is complete and available, new features, and extended capabilities are under active development. The Chicken Creek data portal provides a comprehensive and reliable database to give scientists a fast and easy access to all collected data. Consequently, R-Shiny offers a great potential for future development of the web-based data portals to efficiently handle scientific data collected at different scales.}, language = {en} }