Subsurface Properties Research Articles

New, dated small impacts on the South Polar Layered Deposits (SPLD), Mars, and implications for shallow subsurface properties

The Mars Reconnaissance Orbiter (MRO) Context Camera (CTX) imaged two newly formed impact craters on the South Polar Layered Deposits (SPLD) of Mars in 2018 and 2020. These two new craters, the first detected on the SPLD, measure ∼17 m and ∼48 m in diameter. Follow-up observations were conducted with the High Resolution Imaging Science Experiment (HiRISE), showing seasonal and interannual changes, and providing stereo coverage for the production of digital terrain models (DTMs). Mars Climate Sounder (MCS) data were obtained over the region of these new impacts, giving surface temperature information for the time interval before and after the impacts were detected. Taken together, the optical and infrared observations of these sites reveal craters largely consistent with the morphologies of other small, dated impact craters on Mars, and crater ejecta patterns that suggest a more dust/regolith-dominated upper few meters of the SPLD in contrast to mid-latitude buried ice and lobate debris aprons (LDAs). This supports previous conclusions that the SPLD may have an upper surface depleted in water ice relative to the North PLDs, possibly the result of a widespread deflation event.

Artificial Bee Colony Algorithm with Adaptive Parameter Space Dimension: A Promising Tool for Geophysical Electromagnetic Induction Inversion

Frequency-domain electromagnetic induction (FDEMI) methods are frequently used in non-invasive, area-wise mapping of the subsurface electromagnetic soil properties. A crucial part of data analysis is the geophysical inversion of the data, resulting in either conductivity and/or magnetic susceptibility subsurface distributions. We present a novel 1D stochastic optimization approach that combines dimension-adapting reversible jump Markov chain Monte Carlo (MCMC) with artificial bee colony (ABC) optimization for geophysical inversion, with specific application to frequency-domain electromagnetic induction (FDEMI) data. Several solution models of simplified model geometry and a variable number of model knots, which are found by the inversion method, are used to create re-sampled resulting average models. We present synthetic test inversions using conductivity models based on 14 direct-push (DP) EC logs from Greece, Italy, and Germany, as well as field data applications using multi-coil FDEMI devices from three sites in Azerbaijan and Germany. These examples show that the method can effectively lead to solutions that resemble the known DP input models or image reasonable stratigraphic and archaeological features in the field data. Neighboring 1D solutions on field data examples show high coherence along profiles even though each 1D inversion is independently handled. The computational effort for one 1D inversion is less than 120,000 forward calculations, which is much less than usually needed in MCMC inversions, whereas the resulting models show more plausible solutions due to the dimension-adapting properties of the inversion method.

Estimation of Geotechnical Parameters for Coal Exploration from Quasi-3D Electrical Resistivity Measurements

Geotechnical parameters are crucial for mine planning and operation at different stages of development. However, estimating these parameters requires a large number of boreholes and subsequent detailed analysis of the samples, making it a cumbersome exercise. Moreover, even after conducting these studies, it is not possible to cover the entire operational area. To address this issue, this study presents an indirect method of estimating geotechnical parameters through mathematical relations using resistivity data. The present study incorporated 2D and 3D subsurface imaging techniques for exploring coal reserves and analyzing geotechnical parameters that define subsurface soil properties. Electrical resistivity tomography (ERT) was utilized for data acquisition, employing a Dipole–dipole array with a multielectrode ABEM Terrameter LS instrument. Six parallel profiles were conducted, each 400 m in length, with an inter-electrode spacing of 10 m and a spacing of 50 m between profiles. These profiles were combined into a 3D dataset referred to as quasi-3D ERT. The inversion process for both 2D and 3D data was performed using the Res2dinv and Res3dinv programs, respectively. This study overcame the challenges of 2D resistivity sections by evaluating horizontal depth slices in the x-z plane from layers 1 to 10, reaching a depth of 81.2 m. The geotechnical parameters, including cohesion, friction angle, moisture content, and plastic index, were derived from the resistivity data. The ERT method proved to be cost-effective and efficient in determining soil properties over a large area compared with traditional laboratory analysis of borehole samples. Additionally, the variation of geotechnical parameters with resistivity values exhibited unique characteristics. The results from both the 2D and quasi-3D ERT were well correlated with the borehole data. Such studies are valuable for resource exploration and mine planning purposes.

ERT data assimilation to characterize aquifer hydraulic conductivity heterogeneity through a heat‐tracing experiment

AbstractGeothermal energy systems, such as heat pumps relying on aquifers, use renewable sources of energy that are accessible in urban areas. It is necessary to characterize the subsurface hydraulic properties prior to the installation of such systems. In this context, a heat‐tracing experiment is a typical field test that can help with the characterization of the subsurface. During a heat‐tracing experiment, monitoring with downhole temperature sensors, water‐level pressure transducers and electrical resistivity tomography (ERT) can be used to help characterize the hydrogeological properties. Previous monitoring tools have shortcomings, such as low‐resolution data and over‐smoothing; thus, they fail to reproduce the heterogeneity of hydrogeological properties. Ensemble Kalman filter (EnKF) is a promising tool that can overcome the over‐smoothing problem to replicate the hydrogeological property heterogeneity. In this work, we proposed a new procedure to assimilate time‐lapse cross‐borehole ERT data into a numerical model of groundwater flow and heat transfer, where the groundwater is extracted and heated water is reinjected into an unconfined sandy‐gravel aquifer. The finite element model (FEFLOW 7.3) of groundwater flow and heat transfer is integrated with petrophysical relationship and electrical forward modelling (ResIPy) to estimate cross‐borehole ERT measurements. Then, the estimated apparent resistivity is assimilated to update the hydraulic conductivity model using EnKF. The results of the application of the proposed approach to an experimental site located in Quebec City (Canada) demonstrate that the heterogeneity of K is correctly reproduced as the updated K model is reasonably consistent with the lithological log. In addition, the proposed approach was able to replicate the cross‐borehole ERT field and temperature measurements. The comparison between prior and posterior distribution of K with slug test results shows that the EnKF made the final (assimilated) distribution of K move towards K values inferred with slug tests.

Application of Post Stack Acoustic Impedance Inversion to Lateral Rock Property Prediction: A Case Study Eti-field Offshore Niger Delta

This study presents the result of a Model-based seismic inversion technique which was used to invert an acoustic impedance structure within a reservoir interval by intergrating well logs and 3D post stack seismic data obtained from Eti-field offshore Niger Delta. The purpose was to delineate lateral and vertical alternations in subsurface rock properties which is caused by difference in lithofacies within the reservoir interval. This would help to define hydrocarbon fairways better and constrain the range of hydrocarbon zones for field development. The inversion workflow used in this study includes forward modelling of reflection coefficients from a low frequency impedance model driven from well logs and convolution of the reflection coeffiecients with a source wavelet derived from the seismic data. P-impedance inversion analysis at the control well location gave a near perfect correlation of 0.993686 (correlation coefficient of ≈ 99.4 %) between the original P-impedance log, initial guess P-impedance model log and inverted P-impedance log. The estimated error observed was 5533.1 which corresponds to (0.112308) about 11.23 %. Seismic inversion analysis realized an acoustic impedance striucture with P-impedance values ranging from 7000 to about 50000 ft/s*g/cc and having a general increase with depth trend. Impedance slice extracted from the impedance volume at top of the reservoir predicted lateral variations in P-impedance at well control and away from well control. This information can be invaluable in delineating more prospective reservoir zones in the field and thereby enhancing optimum field development which aids in reservoir management decisions.

Investigation of the geological and hydrogeological structure of chalk cliffs with visible, thermal infrared and electrical resistivity imaging

This study is focused on assessing the surface and subsurface properties of a coastal cliff undergoing erosion. The investigation takes place along the chalk cliffs located in Sainte-Marguerite-sur-Mer, within the region of Normandy, France. To achieve this, a combination of Electrical Resistivity Imaging (ERI), visible and Thermal Infrared (TIR) imagery, and geotechnical methods (specifically, piezometers) were employed.ERI was chosen due to its high sensitivity to changes in water saturation and salinity within subsurface formations. The research involved two transverse ERI profiles originating from the plateau, crossing the cliff, and extending to the shore platform. Additionally, two longitudinal profiles were conducted on the plateau, running parallel to the coastline, all using Wenner configurations. The primary findings unveiled the distribution of subsurface resistivity, revealing insights into cracks, potential karst formations, and weathered chalky areas. A significant discovery was the identification of a conductive zone at depth, which was associated with saltwater intrusion.The integration of piezometers, intersecting the ERI profiles, allowed for the correlation of resistivity values with the measured salinity data. Areas characterised by brackish water exhibited an apparent resistivity ranging between 4 and 75 Ω.m, commencing at the front of the saline wedge.

Microwave spectra of the leading and trailing hemispheres of Iapetus

The leading hemisphere of Saturn’s synchronous moon Iapetus is covered by a low-albedo material, contrasting with its bright trailing hemisphere. This dichotomy is also apparent in radar and microwave radiometry observations, which are sensitive to the properties of the near subsurface. To better understand the regional properties of Iapetus and their variations with depth, we assemble the microwave spectra of its leading and trailing hemispheres. Pre-existing data are combined with new millimetric and centimetric observations acquired with the IRAM 30-meter dish, IRAM NOEMA interferometer, and VLA interferometer. These data, interpreted with the help of a model with vertically uniform thermal properties, reveal complex variations in structure and/or composition with depth on the leading side. Meanwhile, the trailing side emissivity is found to be especially low at all observed frequencies, indicating efficient scattering processes on subsurface structures, as observed on Saturn’s other icy moons. We also report the first observations of Saturn’s retrograde moon Phoebe at these frequencies, which has an emissivity higher than that of the trailing hemisphere of Iapetus and similar to its dark leading side, consistent with the theory that Phoebe is the source of the dark material on Iapetus.

Study on the subsurface characteristics of textured surfaces under EHL condition

PurposeThe purpose of this study is to investigate the influence of surface texture on the subsurface characteristics of contact interfaces under elastohydrodynamic lubrication condition. As a typical contact form of gears and bearings, the optimization of friction characteristics at the elastohydrodynamic lubrication (EHL) interface has attracted the attention of scholars. Laser surface texturing is a feasible optimization solution, but there have been concerns about whether the surface texture of high-pair parts will affect their fatigue life.Design/methodology/approachTo examine the impact of texture preparation on the subsurface characteristics of high-pair interfaces under EHL conditions, a point contact EHL model is developed that takes into account the effect of textured surface topography. The pressure and thickness of the oil film are calculated as input parameters under different loads and entrainment velocities. The finite element method is used to simulate the impact of textures with varying diameters, densities and depths on the subsurface characteristics of the elastohydrodynamic interface. According to ISO 25178, analyze the relationship between 3D topography parameters and subsurface characteristics and study the trend of friction characteristics and subsurface characteristics based on the results of the ball on disc friction tests.FindingsThe outcomes suggest that under different rotational velocity and load conditions, the textured surfaces exhibit improved friction reduction effects; however, the creation of textures can result in significant subsurface plastic deformation and local peeling. The existence of texture makes the larger stress zone in the subsurface layer closer to the surface, leading to fatigue failure near the surface. Reasonable design parameters can help enhance the attributes of the subsurface. A smaller Sa and a Str greater than 0.5 can achieve ideal subsurface properties on the textured surface.Originality/valueThis paper investigates the influence of surface texture on the friction and subsurface characteristics of EHL interfaces and analyzes the impact of surface texture on interface contact performance while achieving lubrication improvement functional characteristics. The results provide theoretical support for the optimization design and functional regulation of surface texture in EHL interfaces.Peer reviewThe peer review history for this article is https://publons.com/publon/10.1108/ILT-10-2023-0324/

DIFFERENT-SCALE HETEROGENEITIES IN SEGMENTS OF SEISMOGENIC FAULTS AND THEIR INFLUENCE ON SLIP MODES

The paper presents some multidisciplinary research results on the structure of slip surfaces in segmented sections of tectonic faults in the Baikal region and Mongolia. The properties of subsurface (modern) and deep slip planes that came to the earth’s surface as a result of many-kilometer denudation in the upper part of the earth’s crust are studied at different levels-from macroscale to nanocrystals. Other types of heterogeneities characterizing the structure of fault slip zones are also considered. The presented data indicate a heterogeneous structure of tectonic faults. Their slip zones show both low friction in sections where strong mineral phases are replaced by weak minerals, and potentially unstable patches with high frictional resistance. The results obtained in a comprehensive study of geological conditions, under which different-scale heterogeneities emerge in exhumed fault segments, should be taken into account when developing rock mass models suitable for numerical simulation of earthquake preparation processes at the micro-, mesoand macroscales.

Fatigue life analysis of deep rolled bearing inner rings

The deep rolling process can influence the surface and subsurface of hardened steels, such as the bearing steel AISI 52100, due to mechanical loading and elastoplastic deformation. By intentionally adjusting the surface and subsurface properties, the fatigue life of bearing inner rings can be increased. This is among others attributed to the strengthening of surface and subsurface. Residual stresses induced by deep rolling and modifications of the material microstructure also contribute to this effect. In the investigations presented the deep rolling process parameters of rolling pressure, overlap ratio, and ball diameter were specifically selected based on previous works. Fatigue life investigations were conducted on honed and deep rolled bearing inner rings to enhance the understanding of failure mechanisms and to quantify the influence of the deep rolling process on fatigue life. It was found that the deep rolled bearing inner rings exhibit higher compressive residual stresses in the subsurface than honed rings and also showed longer fatigue life under rolling loads. Optical analyses of bearing rings that failed due to fatigue were performed to detect the failure mechanisms. The tested bearings showed classical fatigue, where cracking is initiated below the surface and propagates to the surface under further stress. Residual stresses can influence both the crack initiation and propagation.

Influence of the strain rate on the Surface Integrity on 42CrMo4 generated by machine hammer peening

Machine hammer peening is as well as deep rolling known to smoothen the surface, strain hardening and for inducing high compressive residual stresses in high depths in the surface and subsurface layer of steel. Although the beneficial effects of this manufacturing technology are well described, the influence of the strain rate on the surface and subsurface properties of machine hammer peened steel has yet to be examined. In this study three different kinetic impact energy levels and three different hammer diameters – resulting in nine different strain rates - are applied. For specimens made of hardened 42CrMo4 (AISI 4140), the analysis of the surface and subsurface properties by means of metallographic cross-sections and XRD measurements indicate that a variation of the strain rate mainly affects the depth effect at high levels of compressive residual stresses.

The use of passive seismic interferometry for the monitoring of subsurface fluids – from shallow groundwater to native or storage gas reservoirs

Passive (ambient noise) seismic interferometry provides multiple ways to gather information about the subsurface seismic properties using recordings of the seismic ambient noise signal. While the first developments and applications of this method showed a useful capacity to either image geological contrasts or monitor the structural properties of the soil, an increasing momentum is observed toward applications related to fluid monitoring of different types (liquid, gas), at all the scales of the subsurface (from meters to kilometers). In this paper we summarize the existing possibilities and technics of seismic interferometry analysis for subsurface fluid detection and characterization and elaborate on their respective deployment in different contexts. We also present a new approach based on estimating and continuously measuring seismic attenuation proxy within interferometric-based surface wavefields, which show a high sensitivity to fluid dynamics and the associated petrophysical variations. The method is illustrated through a field case study related to geological gas storage monitoring, and we elaborate on its potential respective deployment at the industrial scale and for different applications.

Enhancing Orebody Knowledge using Measure-While-Drilling Data: A Machine Learning Approach

Due to limited resource definition and grade control drilling, conventional bench-scale geochemical exploration is frequently plagued by uncertainty, resulting in diminished processing plant efficiencies. The Measure-While-Drilling (MWD) system comprises a set of sensors designed to gather data pertaining to the performance of mining blast hole drill rigs. Previous MWD research primarily relied on penetration rate to identify rock type and estimate grade. This investigation uses Data Fusion and Machine Learning (ML) algorithms to characterize geochemical orebody quality values, such as iron percentage, phosphorous, sulfur, alumina, and silica content, based on MWD data collected at the field-scale, thereby facilitating efficient mine planning and excavation. Feature importance algorithms identified novel significant MWD variables, such as force on bit and ratio of bit air pressure to penetration rate, which provide valuable insights into subsurface geochemical properties. The performance of various ML algorithms was compared, with the Random Forest algorithm demonstrating the highest coefficients of determination (up to 0.96), indicating accurate field or laboratory results prediction. As a result, this work demonstrates that MWD data can be used to obtain high-resolution orebody knowledge prior to mining, improving subsurface geochemistry prediction. This knowledge can optimize the excavation of high-grade material by minimizing dilution from lower-grade or waste rock entering the processing plant.

Sonic Log Prediction Based on Extreme Gradient Boosting (XGBoost) Machine Learning Algorithm by Using Well Log Data

Sonic log is an important aspect that provides a detailed description of the subsurface properties associated with oil and gas reservoirs. The problem that frequently occurs is the unavailability of sonic log data for various reasons needs to be given an effective solution. The alternative approach proposed in this research is sonic log prediction based on Extreme Gradient Boosting (XGBoost) machine learning algorithm, using available log data to build a reliable sonic log prediction model. In this research, the predicted DT log type is the Differential Time Shear Slowness (DTSM) log, which is the velocity of shear waves propagating in a formation. Log features used for training include gamma ray (GR), density (RHOB), porosity (NPHI), resistivity (RS and RD) logs with DTSM log as the prediction target. To optimise the performance and generalisation of the XGBoost algorithm in predicting log DTSM, hyperparameter tuning was applied using grid search technique to obtain optimal parameters for the prediction model. Based on the experimental results, this research found that hyperparameter tuning using grid search technique improved the accuracy of sonic log (DTSM) model prediction based on XGBoost algorithm, as proven by the decrease of RMSE and MAPE values to 19.699 and 7.713%. The results also pointed out the need for methods other than listwise deletion to handle missing values as an alternative to improving model accuracy. This research highlighted the need for continuous improvement in data processing methods and algorithm optimization to advance the application of machine learning in geophysical exploration.

Experimental Investigation on the Surface Integrity in Micromilling AISI H11 Tool Steel

The surface integrity of machined components affects their fatigue performance and service life. Thus, with a view to sustainability and resource efficiency, aspects that influence the nature of the workpiece surface and sub-surface are increasingly considered in the process design, such as the resulting microhardness or the residual stress state. In this context, micromilling offers the possibility to positively influence the sub-surface properties due to the low thermal impact as well as concentrated mechanical load during machining. In order to gain a deeper understanding of the potential of micromilling for surface conditioning, experimental investigations were carried out on samples of the hot work tool steel AISI H11 in hardened condition. Cutting forces and surface topography were measured at different feed per tooth. The depth-dependent microhardness and residual stresses were determined after machining and analyzed taking into account the engagement situation and cutting forces. As the results show, micromilling can induce high compressive residual stresses as well as increase surficial microhardness while achieving low roughness topographies. This represents a great potential for improving component properties regarding tribological performance as well as fatigue behavior.

Integrative Geospatial Analysis: Unveiling Insights through GIS Modeling and Statistical Evaluation of SPT‐N and Soil Types Data of New Kabul City, Afghanistan

The precise evaluation of subsurface soil information is paramount for effective infrastructure design and planning. Geotechnical soil maps (GSMs) play a pivotal role in estimating subsurface properties. As Kabul City, Afghanistan’s largest metropolitan area, undergoes rapid expansion and the demand for safer infrastructure rises, the necessity for precise geotechnical information becomes increasingly urgent. Nevertheless, there is a lack of comprehensive studies on urban geotechnical zoning maps in Afghanistan. Therefore, to this end, this study utilizes inverse distance weighting (IDW) interpolation approaches for the construction and characterization of GSMs using a traditional geographic information system (GIS). Data about soil types and standard penetration (N‐value) up to 30 m depth were extracted from 130 locations in New Kabul City, Afghanistan. GSMs incorporating N‐value and soil types were constructed utilizing the IDW technique within the ArcGIS platform. The outcomes reveal that the subjected region was dominated by cohesive soil with N‐values varying from 7 to 45 in three different sections. The findings exhibit strong empirical correlations between N‐values and depth, with an R2 value of 0.95 and an RMSD value of 0.71. Moreover, the correlation coefficient to predict soil type classification is 97%, and it stands at 95% for N‐value prediction. These results facilitate the rapid evaluation of subsoil strength and stiffness, offering valuable insights for project planners and feasibility researchers.

Deep learning for high-resolution multichannel seismic impedance inversion

Seismic impedance inversion can obtain subsurface physical properties and plays an important role in hydrocarbon and mineral exploration. Due to the inaccurate and insufficient seismic data, the inverse problem is ill posed as characterized by unreliability and nonuniqueness of solutions. Regularization techniques relying on certain prior information often are introduced to force the inverse problem to obtain stable results with predetermined characteristics. However, for complex geologic conditions, these methods usually have difficulty achieving satisfactory accuracy and resolution. We develop a deep-learning (DL)-based multichannel impedance inversion method that flexibly incorporates prior information by training with numerous realistic structural 2D impedance models based on the features of field data. The DL framework is supplemented by the attention mechanism and residual block to automatically learn more features and details from training data. A novel hybrid loss function, combining [Formula: see text] loss and multiscale structural similarity loss, is introduced to enhance the network’s capacity for learning structural features. Synthetic and field data examples demonstrate that our method can effectively produce inversion results with high resolution, good lateral continuity, and enhanced structural features compared with traditional methods.

A process-reliable tailoring of subsurface properties during cryogenic turning using dynamic process control

Considering the current demands for resource conservation and energy efficiency, innovative machining concepts and increased process reliability have a significant role to play. A combination of martensitic hardening of the subsurface and near-net-shape manufacturing represent a great potential to produce components with wear-resistant subsurfaces in an energy- and time-saving way. Within the scope of the present study, the influence of cryogenic machining of metastable austenitic steel on the martensitic transformation and surface quality was investigated. Different cooling strategies were used. A soft sensor based on eddy current in-process measurements was used to determine and subsequently affect the martensitic transformation of the subsurface. The feed rate and component temperature were identified as significant factors influencing the martensitic transformation. However, a high feed rate leads to an increase in surface roughness, and thus to a reduction in component quality. For this reason, a roughing process for achieving maximum martensitic transformation was carried out first in the present study and then a reduction in the surface roughness by maintaining the martensitic subsurface content was aimed for by a subsequent finishing process. With the knowledge generated, a dynamic process control was finally set up for designing the turning process of a required subsurface condition and surface quality.

Enhancing knowledge discovery from unstructured data using a deep learning approach to support subsurface modeling predictions.

Subsurface interpretations and models rely on knowledge from subject matter experts who utilize unstructured information from images, maps, cross sections, and other products to provide context to measured data (e. g., cores, well logs, seismic surveys). To enhance such knowledge discovery, we advanced the National Energy Technology Laboratory's (NETL) Subsurface Trend Analysis (STA) workflow with an artificial intelligence (AI) deep learning approach for image embedding. NETL's STA method offers a validated science-based approach of combining geologic systems knowledge, statistical modeling, and datasets to improve predictions of subsurface properties. The STA image embedding tool quickly extracts images from unstructured knowledge products like publications, maps, websites, and presentations; categorically labels the images; and creates a repository for geologic domain postulation. Via a case study on geographic and subsurface literature of the Gulf of Mexico (GOM), results show the STA image embedding tool extracts images and correctly labels them with ~90 to ~95% accuracy.

Reservoir Characterization Using Seismic Inversion Based on Sparse Layer Reflectivity and Hybrid Genetic Algorithms: A Comparative Case Study of Blackfoot, Canada

This research paper introduces a comparative case study on reservoir characterization through seismic inversion techniques. The study specifically explores sparse layer reflectivity and a hybrid approach involving genetic algorithms and pattern search. The research assesses the effectiveness of these methodologies in delineating subsurface properties, with a particular focus on acoustic impedance. Through meticulous analysis, the paper aims to identify the strengths and limitations of each method, considering factors such as parameter estimation precision, computational efficiency, and adaptability to complex geological structures. The findings contribute valuable insights for selecting optimal seismic inversion techniques in reservoir characterization, advancing our understanding of how the integration of sparse layer reflectivity and hybrid genetic algorithms can enhance subsurface imaging accuracy and reliability. The results obtained from our inversion process significantly enhance the interpretation of seismic data by providing detailed insights into the subsurface. Both the sparse layer reflectivity (SLR) and hybrid genetic algorithm (HGA) algorithms have exhibited outstanding performance when applied to real datasets. The inverted impedance section reveals notable low acoustic impedance ranging from 8000 to 8500 m/s g/cc. This distinct zone, identified as a reservoir (sand channel), is located within the time interval of 1040–1065 ms. Our observations indicate that HGA demonstrates superior correlation results not only in the vicinity of well locations but also over a broader spatial range, suggesting its potential to provide higher-resolution outcomes compared to SLR.