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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., QC 20240807

In the context of geo-infrastructures and specifically tunneling projects, analyzing the large-scale sensor-based measurement-while-drilling (MWD) data plays a pivotal role in assessing rock engineering conditions. However, handling the big MWD data due to multiform stacking is a time-consuming and challenging task. Extracting valuable insights and improving the accuracy of geoengineering interpretations from MWD data necessitates a combination of domain expertise and data science skills in an iterative process. To address these challenges and efficiently normalize and filter out noisy data, an automated processing approach integrating the stepwise technique, mode, and percentile gate bands for both single and peer group-based holes was developed. Subsequently, the mathematical concept of a novel normalizing index for classifying such big datasets was also presented. The visualized results from different geo-infrastructure datasets in Sweden indicated that outliers and noisy data can more efficiently be eliminated using single hole-based normalizing. Additionally, a relational unified PostgreSQL database was created to store and automatically transfer the processed and raw MWD as well as real time grouting data that offers a cost effective and efficient data extraction tool. The generated database is expected to facilitate in-depth investigations and enable application of the artificial intelligence (AI) techniques to predict rock quality conditions and design appropriate support systems based on MWD data., QC 20240429

Rumslig fördelning av djup till berggrunden (DTB) är en viktig ochutmanande fråga i många geotekniska tillämpningar. På grund av att DTBassocieras med säkerhet och ekonomi i jord- och bergkonstruktionerkan generering av mer precisa modeller vara av avgörande betydelse.Med hjälp av resultat från Jb-sonderingar för ett infrastrukturprojekt iStockholm har vi skapat en optimerad visualiserad 3D -prediktiv DTBmodellvia en automatiserad artificiell intelligent datormetod (AI) ochjämfört den med den metod som ofta används, det geostatistiskaverktyget Ordinary Kriging (OK)., QC 20220208

Uncertainty quantification (UQ) is an important benchmark to assess the performance of artificial intelligence (AI) and particularly deep learning ensembled-based models. However, the ability for UQ using current AI-based methods is not only limited in terms of computational resources but it also requires changes to topology and optimization processes, as well as multiple performances to monitor model instabilities. From both geo-engineering and societal perspectives, a predictive groundwater table (GWT) model presents an important challenge, where a lack of UQ limits the validity of findings and may undermine science-based decisions. To overcome and address these limitations, a novel ensemble, an automated random deactivating connective weights approach (ARDCW), is presented and applied to retrieved geographical locations of GWT data from a geo-engineering project in Stockholm, Sweden. In this approach, the UQ was achieved via a combination of several derived ensembles from a fixed optimum topology subjected to randomly switched off weights, which allow predictability with one forward pass. The process was developed and programmed to provide trackable performance in a specific task and access to a wide variety of different internal characteristics and libraries. A comparison of performance with Monte Carlo dropout and quantile regression using computer vision and control task metrics showed significant progress in the ARDCW. This approach does not require changes in the optimization process and can be applied to already trained topologies in a way that outperforms other models., QC 20230116

Due to associated uncertainties, modelling the spatial distribution of depth to bedrock (DTB) is an important and challenging concern in many geo-engineering applications. The association between DTB, the safety and economy of design structures implies that generating more precise predictive models can be of vital interest. In the present study, the challenge of applying an optimally predictive three-dimensional (3D) spatial DTB model for an area in Stockholm, Sweden was addressed using an automated intelligent computing design procedure. The process was developed and programmed in both C++ and Python to track their performance in specified tasks and also to cover a wide variety of different internal characteristics and libraries. In comparison to the ordinary Kriging (OK) geostatistical tool, the superiority of the developed automated intelligence system was demonstrated through the analysis of confusion matrices and the ranked accuracies of different statistical errors. The results showed that in the absence of measured data, the intelligence models as a flexible and efficient alternative approach can account for associated uncertainties, thus creating more accurate spatial 3D models and providing an appropriate prediction at any point in the subsurface of the study area., QC 20220308

Delineatingand mapping the bedrockand overlaid deposits due to 8complex spatial patterns, associated uncertainties and sparse data is a vital diffi-9cult task in geo-engineering applications. Modern computing techniques such as 10artificial intelligence-based models (AIM) are appropriate alternative to over-11come the deficiencies of previous methods. The objective of this study is to in-12vestigatethe feasibility of AIM in prediction of 3D spatial distribution of subsur-13face bedrock in a large area in Stockholm, Sweden. The predictive artificial in-14telligence models were developed using 1968 processed soil-rock soundings 15comprising the geographical coordinates and ground surface elevation. The opti-16mum topology was captured through the examining of wide variety of internal 17characteristics.It was observed that in sparse dataset, the developed AIMs effi-18ciently can provide much more accurate prediction than traditionally applied 19techniques such as geostatistical approaches. This implies that the developed 20AIM due to significant capacities and acceptable predictability level can decrease 21the residuals between the predicted and measured data., QC 20200930

Three-dimensional radio magnetotelluric (RMT), controlled-source radio magnetotelluric (CSRMT), electrical resistivity tomography (ERT), and high-resolution reflection seismic data were acquired at a quick-clay landslide site close to the Göta River in southwest Sweden. These data were collected with the main objectives of evaluating the performance of each method and delineating different subsurface materials down to the bedrock, which in turn control a peculiar and hazardous retrogressive-type landslide. These materials consist of mainly marine clays, quick clays, sand to gravel and the crystalline bedrock. The RMT, CSRMT and ERT data were inverted separately using 3D inversion schemes and the independent results from 3D processing of reflection seismic data together with the existing geotechnical data were used for their interpretations. The 3D CSRMT resistivity model shows the highest depth penetration and well resolves the geometry of the bedrock. The RMT and ERT models, however, show better resolution closer to the surface because of their denser data coverage compared with the CSRMT data. Considerable correlations in resolving a coarse-grained layer and the bedrock were observed between the CSRMT model and the 3D reflection seismic data. Because an Occam-type regularization was used in the inversion, the lateral and depth extent of the quick clays cannot accurately be determined just by using the resistivity models. The depth to the top of the resistive crystalline bedrock is to some extent uncertain. Comparisons with the borehole data suggest that the resistivity models are poor in resolving the sharp boundary between the quick clays and their underlying more resistive coarse-grained materials. The upper boundary of the more conductive marine clays underlain by the coarse-grain materials was well modeled by the CSRMT data. This study illustrates the potentials of 3D geophysical data especially when different types are collected and particularly for better si

Very Low Frequency (VLF) electromagnetics (EM) has been widely and successfully applied in mineral exploration and ground water exploration in the past decades. Many radio transmitters with strong signal to noise ratios are scattered in the Very Low Frequency (VLF) band and the Low Frequency (LF) band. Based on the experiences gained from ground measurements with the Radio Magnetotelluric (RMT) technique operating in the frequency interval 1-250 kHz, broad band magnetometers have been utilized to cover both the VLF (3-30 kHz) and LF (30-300 kHz) bands to increase the resolution of the near surface structure. The metallic aircraft as a conductive body will distort the magnetic signal to some extent, and thus an investigation of the interference of the aircraft on the electromagnetic signal is important. Noise studies due to the rotation of the aircraft and the aircraft itself as a metallic conductive body has been carried out by three different methods: 3D wave polarization, determination of transmitter direction and full tipper estimation. Both VLF and LF frequency bands were investigated. The results show that the magnetic field is independent of the aircraft at low frequencies in the VLF and part of the LF bands (below 100 kHz). At high frequencies (above 100 Hz), the signals are more influenced by the aircraft and the wave polarization directions are more scattered as seen when the aircraft turns. Some aircraft generated noise which is mixed with the radio transmitter signals are detected as 'dummy' signals by the 3D wave polarization method. The estimated scalar magnetic transfer functions have dependence on the aircraft flight directions at high frequencies which is caused by the aircraft interference. The aircraft eigen response in the transfer functions (tippers) between vertical and horizontal magnetic field components can be compensated for in the real part of the estimated tippers, but some unknown effect is still observed in the imaginary parts.

Radio Magnetotellurics (RMT), Electrical Resistivity Tomography (ERT) and high-resolution reflection seismic data were collected along four lines to image the geometry and physical properties of geological 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 post-glacial sediments. Geotechnical data suggest 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 than individual ones. The geometry and location of shallower structures resolved in the 2D resistivity models from joint RMT+ERT inversions correlate well with those imaged in the reflection seismic data and observed in the existing geotechnical boreholes. The models are 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 suggests that quick clays overlying the coarse-grained layer have higher electrical resistivity than the marine clays. The obtained results were further validated and refined by performing synthetic tests. This study shows 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.

Landslides are one of the most commonly occurring naturaldisasters. They claim hundreds of human lives and costbillions of dollars every year. In order to providegeophysical tools and techniques to better characterize sitesprone to slide, we have been carrying out and evaluatingpotential utility of several geophysical surveys over a quickclay landslide site in southwest Sweden since 2011. Themeasurements include 2D and 3D P- and S-wave highresolution surface seismics, radio- and controlled-sourceelectromagnetics, geoelectrics, ground gravity andmagnetic surveys. A particular focus here is given to theseismic studies in the site. Combined with downholegeophysical and geotechnical measurements, we show thatmajority of reflections correlate well with sandy-siltyformations in the site. Quick clays often occur above theseformations, which may be an indication of the role ofcoarse-grained formations to not only partly form quickclays but also triggering them when pore-water pressure issignificantly increased.

We have collected 3D tensor Controlled Source- and RadioMagnetotelluric data at a quick-clay site in southwesternSweden with the aim to image the geological structures thathave given rise to retrogressive landslides in the area. The3D resistivity models show layering within the glacial andpost-glacial sediments including marine clay, quick-clayand coarser grain sands. Because of the smoothingregularization used in the inversion, the lateral and depthextents of the quick-clays cannot be determined just byusing the resistivity model. The depth to the top of theresistive crystalline bedrock is to some extent uncertain.However, the geometry of the sediments and bedrock seenin the 3D resistivity model correlates reasonably well withthe results from the high resolution reflection seismic datacollected along the same lines in the site. The results showthat the bedrock deepens towards the river in the northernpart of the site.

The idea of measuring three-component magnetic fields on board an aircraft or on a moving device in a frequency band from 30 Hz to 1000 Hz was tested. A rigid frame was constructed carrying the three magnetic sensors together with accelerometers and angle sensors. Two test measurements with the frame moving were carried out by putting it on a trailer behind a car and on board an aircraft. From both data sets we observed strong engine noise. Three stability test measurements were made by putting the magnetic sensors in parallel and we concluded that uncorrelated signals are generated during movements even when moving the frame at lower walking speed. It was finally decided to measure the three magnetic field components when the frame is at rest on the ground. Additionally two short electric field sensors were set up and five components electromagnetic fields were measured. The new data acquisition technique is based on the traditional magnetotelluric (MT) method but more efficient in several ways. We name it Moving Magnetotellurics (MMT) and did tests in different areas with different earth conditions. We found that the MMT method suffers from the same noise disturbances as the other MT methods do. Man-made noise such as power lines or railways reduce the data quality to a high degree, but its influence can be reduced using data analysis. Recordings at night time generally are less disturbed from the man-made noise. The data quality is much better when the measurement is carried out in areas with low resistivity. The MMT method is proven to be a applicable method in shallow depth studies, especially in areas where normal MT measurements are inconvenient and/or too expensive to carry out.