Radio magnetotellurics (RMT), electrical resistivity tomography (ERT), and high-resolution reflection seismic data were collected along four lines to characterize the geometry and physical properties of geologic structures at a quick-clay landslide site in southwest Sweden. The site is situated in the Göta River valley where the normally consolidated materials mainly consist of glacial and postglacial sediments. Geotechnical data suggest the presence of quick clays above coarse-grained layers. These layers play a key role in the formation of quick clays and landslide triggering. The RMT and ERT data were individually and jointly inverted in 2D to study the resolution of resulting models for each data set. The resistivity models from the joint inversions demonstrate superior resolution and accuracy compared with individual ones. The geometry and location of shallower structures resolved in the 2D resistivity models from joint RMT&ERT inversions correlated well with those imaged in the reflection seismic data and observed in the existing geotechnical boreholes. The models were poor in resolving deeper resistive bedrock at locations where the thickness of the conductive overburden exceeds a certain limit. However, information from the reflection seismic data could be used to estimate the depth to the top of the bedrock along all the four lines. Comparison between the geotechnical data and the resistivity models suggested that quick clays overlying the coarse-grained layer have higher electrical resistivity than the marine clays. We further validated and refined the obtained results by performing synthetic tests. We showed that integration of ERT and RMT data with reflection seismic data is ideal for quick-clay landslide studies especially when the clay materials are thick.

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There is an increasing interest in creating high-resolution 3D subsurface geo-models using multisource retrieved data, i.e., borehole, geophysical techniques, geological maps, and rock properties, for emergency managements. However, dedicating meaningful, and thus interpretable 3D subsurface views from such integrated heterogeneous data requires developing a new methodology for convenient post-modeling analyses. To this end, in the current paper a hybrid ensemble-based automated deep learning approach for 3D modeling of subsurface geological bedrock using multisource data is proposed. The uncertainty then was quantified using a novel ensemble randomly automated deactivating process implanted on the jointed weight database. The applicability of the automated process in capturing the optimum topology is then validated by creating 3D subsurface geo-model using laser-scanned bedrock-level data from Sweden. In comparison with intelligent quantile regression and traditional geostatistical interpolation algorithms, the proposed hybrid approach showed higher accuracy for visualizing and post-analyzing the 3D subsurface model. Due to the use of integrated multi-source data, the approach presented here and the subsequently created 3D model can be a representative reconcile for geoengineering applications.