Depth Mapping Research Articles

Snow depth estimation at country-scale with high spatial and temporal resolution

Monitoring snow depth is important for applications such as hydrology, energy planning, ecology, and safety evaluation for outdoor winter activities. Most methods able to estimate snow depth for large regions can only do so in a spatial resolution of up to 1 km ground sampling distance (GSD). This limits their usage in high alpine areas, where this resolution fails to capture local snow distribution patterns caused by the pronounced topographical features. In this work we use a recurrent convolutional neural network to estimate snow depth at high spatial resolution (10 m GSD), weekly, and at large scale based on satellite data sources and elevation maps, without the need for measurement stations on the ground. The proposed method achieves unprecedented results for large-scale, high-resolution snow depth mapping. The resulting maps are evaluated over a period of three years against high-fidelity snow depth maps obtained with airborne photogrammetry. Finally, we also produce well-calibrated uncertainty estimates for every individual snow depth estimate via a probabilistic regression framework.

Evaluation of water resources: A case study of Rohtak district, Haryana

Water is one of the most important natural resources for life. Because of the rapid increase in population, urbanization, and industrialization, the demand for water to meet various needs is constantly increasing. Water that is present below the Earth's surface and fills any gaps in the soil or geologic strata is known as groundwater. It is additionally known as subsurface water. Data related to underground water table were obtained from Central Ground Water Board of the Ministry of Water Resources. Maps of underground water table depth, quality and surface water resources were prepared in Arc GIS software. After the analysis of maps, the depth of the underground water table in the study area ranges from 0 to 20 meters. The maximum area of the study area is 3-10 meters, which covers 48.35% of the study area. The minimum area is 10-20 meters deep and covers 1.43% of the study area. In Rohtak block, the maximum depth is observed in the villages of Brahmanvas, Chamaria, Aryanagar, and Bohar. The quality of groundwater varies from fresh to saline. Fresh underground water quality occupies the largest area, accounting for 59.8% of the study area. Saline underground water quality accounts for 0.5% of the study area. In terms of surface water resources, this district lacks a perennial river. The main irrigation channels in this area are artificial drain no. 8 and sub drains, major canals, distributaries, and minor canals. This research paper will make a better understanding of water resources in Rohtak district. It will help to analyse and understand the current status of underground water depth, quality and surface water to build up any water resource management plan. A water resource management plan will ensure the sustainable use of water resources which is very important for our ecosystem.

Modelling snowpack bulk density using snow depth, cumulative degree‐days, and climatological predictor variables

AbstractSnowpack water equivalent (SWE) is a key variable for water resource management in snow‐dominated catchments. While it is not feasible to quantify SWE at the catchment scale using either field surveys or remotely sensed data, technologies such as airborne LiDAR (light detection and ranging) support the mapping of snow depth at scales relevant to operational water management. To convert snow depth to water equivalent, models have been developed to predict SWE or snowpack density based on snow depth and additional predictor variables. This study builds upon previous models that relate snowpack density to snow depth by including additional predictor variables to account for (1) long‐term climatologies that describe the prevailing conditions influencing regional snowpack properties, and (2) the effect of intra‐ and inter‐year variability in meteorological conditions on densification through a cumulative degree‐day index derived from North American Regional Reanalysis products. A non‐linear model was fit to 114 506 snow survey measurements spanning 41 years from 1166 snow courses across western North America. Under spatial cross‐validation, the predicted densities had a root‐mean‐square error of 47.1 kg m−3, a mean bias of −0.039 kg m−3, and a Nash‐Sutcliffe Efficiency of 0.70. The model developed in this study had similar overall performance compared to a similar regression‐based model reported in the literature, but had reduced seasonal biases. When applied to predict SWE from simulated depths with random errors consistent with those obtained from LiDAR or Structure‐from‐Motion, 50% of the SWE estimates for April and May fell within −45 to 49 mm of the observed SWE, representing prediction errors of −15% to 20%.

Stereopsis without correspondence.

Stereopsis has traditionally been considered a complex visual ability, restricted to large-brained animals. The discovery in the 1980s that insects, too, have stereopsis, therefore, challenged theories of stereopsis. How can such simple brains see in three dimensions? A likely answer is that insect stereopsis has evolved to produce simple behaviour, such as orienting towards the closer of two objects or triggering a strike when prey comes within range. Scientific thinking about stereopsis has been unduly anthropomorphic, for example assuming that stereopsis must require binocular fusion or a solution of the stereo correspondence problem. In fact, useful behaviour can be produced with very basic stereoscopic algorithms which make no attempt to achieve fusion or correspondence, or to produce even a coarse map of depth across the visual field. This may explain why some aspects of insect stereopsis seem poorly designed from an engineering point of view: for example, paying no attention to whether interocular contrast or velocities match. Such algorithms demonstrably work well enough in practice for their species, and may prove useful in particular autonomous applications. This article is part of a discussion meeting issue 'New approaches to 3D vision'.

Canadian Spine Society

# Abstract 57. Radiographic reporting in adolescent idiopathic scoliosis: Is there a discrepancy comparing radiologists’ reports and surgeons’ assessments? {#article-title-2} Cobb angle measurement is the standard method for quantification of scoliosis in adolescent idiopathic scoliosis (AIS),

Activation-induced layered structure in NiCoAl by atomic modulation for energy storage application

The surface activation of alloys favors their electrochemical interactions, ion diffusivity, and the rapid kinetics of ions and electrons, leading to the formation of self-supported layered double hydroxides (LDHs) in them. However, the formation of LDHs at different depths in the alloy upon activation, their electronic/atomic structures, and their electrochemical charge storage mechanism, have not been thoroughly explored. Herein, Ni ion-substituted CoAl alloys are prepared by arc melting and activated by KOH electrolyte, which is responsible for the modulation of the atomic configuration as confirmed by XRD. Raman depth mapping demonstrates how the LDHs vary with depth upon activation and that the octahedral and tetrahedral symmetry sites of CoO and Co3O4 are responsible for the formation of the layered structures of CoOOH and Co(OH)2, respectively. The activated Ni10Co85Al5 has a superior volumetric capacitance of 4.15 F/cm3 at 0.5 mA/g, which is 38.6 times that of an unactivated one, and excellent cyclic stability up to 5000 cycles, and a voltage of 0.54 V generated from a fabricated supercapacitor cell. X-ray Absorption Spectroscopy (XAS) analysis indicates greater charge transfer by Co than by Ni and the modulation of the local atomic structures facilitates electrochemical charge storage in Ni10Co85Al5. This work presents an easy route for the development of advanced LDHs, and the mechanism of electrochemical charge storage in them.

A method for creating a depth map based on a three-level fuzzy model

A new fuzzy method for creating a depth map is presented in the article. It is based on a combination of Canny detector with a three-level fuzzy system and is designed to improve the accuracy of depth mapping. The first fuzzy model was developed to eliminate the errors inherent in Canny detector. The article presents examples to illustrate that Canny filter is not very sensitive to changes in the shape of the gradient. The second fuzzy model makes it possible to eliminate white color artifacts that appear due to the presence of identical large areas on a stereopair, for instance, the background. The third level of the fuzzy model refines the values of disparity obtained using the second level of the fuzzy model and the distances to the near left and right edges of the images obtained during four passes over the stereopair. It was found during the study that the following factors affect the accuracy of the resulting depth map: labels of membership functions of input variables of a three-level fuzzy system; the combination of t and s-norms in the compositional rules of fuzzy inference. The calculation of the root mean square error values made it possible to evaluate the proposed fuzzy model in relation to similar models. It has been established that value of Root Mean Square Error is minimal with the combination of MEAN-MEAN in the structure of fuzzy inference. And three-level fuzzy system for creating a depth map proposed in the article has a significant advantage over existing analogs.

Flood Analysis Using HEC-RAS and HEC-HMS: A Case Study of Khazir River (Middle East—Northern Iraq)

Floods frequently threaten villages near the Khazir River’s floodplains, causing crop losses and threatening residential areas. We used flood-related hydrological software, including WMS and HEC-HMS, to study this issue and determine how to reduce the recurrence of flooding. The software can be used to calculate a hydrograph of torrential flows in a river drainage basin and estimate the volume of torrential water and its flow rates on the Earth’s surface. The depth of rain has been evaluated and calculated in the SCS Unit Hydrograph for different return periods of 2, 5, 10, 20, 50, and 100 years. According to our study’s findings, the volume of the river’s drainage basin floods ranged between 29,680 and 2,229,200 m3, and the maximum flow value ranged between 10.4 and 66.4 m3/sec during various reference periods. To analyze and model the flood risks of the Khazir River, the HEC-RAS model was combined with the HEC-GeoRAS extension in ArcGIS. The floods were the focus of two study periods, 2013 and 2018, and were based on the digital elevation model and river discharge during the floods. According to the classification map of the flood depths, the areas of flood risk varied from low to very low (80.31%), medium (16.03%), and high to very high (3.8%). The analysis of the results revealed that the villages closest to the river’s mouth were more affected by the floods than other villages further downstream. HEC-HMS and HEC-RAS have been shown to have a strong correlation in evaluating flood risks and reliably forecasting future floods in the study area.

Multivariate hyperspectral data analytics across length scales to probe compositional, phase, and strain heterogeneities in electrode materials

The origins of performance degradation in batteries can be traced to atomistic phenomena, accumulated at mesoscale dimensions, and compounded up to the level of electrode architectures. Hyperspectral X-ray spectromicroscopy techniques allow for the mapping of compositional variations, and phase separation across length scales with high spatial and energy resolution. We demonstrate the design of workflows combining singular value decomposition, principal-component analysis, k-means clustering, and linear combination fitting, in conjunction with a curated spectral database, to develop high-accuracy quantitative compositional maps of the effective depth of discharge across individual positive electrode particles and ensembles of particles. Using curated reference spectra, accurate and quantitative mapping of inter- and intraparticle compositional heterogeneities, phase separation, and stress gradients is achieved for a canonical phase-transforming positive electrode material, α-V2O5. Phase maps from single-particle measurements are used to reconstruct directional stress profiles showcasing the distinctive insights accessible from a standards-informed application of high-dimensional chemical imaging.

A Comprehensive Assessment of Floodwater Depth Estimation Models in Semiarid Regions

AbstractSimple models continue to be important for continental‐scale floodwater depth mapping due to the prohibitively expensive cost of calibrating and applying hydrodynamic models. This paper investigates the accuracy of three simple models for floodwater depth estimation from remote sensing derived water extent and/or Digital Elevation Models (DEMs) in semiarid regions. The three models are Height Above Nearest Drainage (HAND; Nobre et al., 2011, https://doi.org/10.1016/j.jhydrol.2011.03.051), Teng Vaze Dutta (TVD; Teng et al., 2013, http://hdl.handle.net/102.100.100/97033?index=1), and Floodwater Depth Estimation Tool (FwDET; Cohen, Brakenridge, et al., 2018, https://doi.org/10.1111/1752-1688.12609). The model accuracy and nature of errors are established using industry's best practice hydrodynamic models as benchmarks in three regions in eastern Australia. The overall results show that FwDET tends to underestimate (by 0.32 m at 50th percentile) while HAND and TVD overestimate floodwater depth for almost all floods (by 0.97 and 0.98 m, respectively). We quantify how switching DEM from 5 m LiDAR to national or global data sets DEM‐H (Gallant et al., 2011, https://ecat.ga.gov.au/geonetwork/srv/eng/catalog.search#/metadata/72759), MERIT (Yamazaki et al., 2019, https://doi.org/10.1029/2019WR024873), or FABDEM (Hawker et al., 2022, https://doi.org/10.1088/1748-9326/ac4d4f) can affect different models differently; and we evaluate model performance against reach geomorphology and magnitude of flood events. The findings emphasize the importance of choosing a model that is fit for the intended application. By describing the applicability, advantages, and limitations of these models, this paper assists practitioners to choose the most appropriate model based on characteristics of their study area, type of problems they try to solve, and data availability.

English

Vertical electrical sounding (VES) and 2D electrical resistivity tomography (ERT) employing the Schlumberger and Wenner configurations respectively were combined for groundwater exploration in parts of Enugu metropolis. The area is underlain by Enugu Shale and Mamu Formation. A total of twenty (20) VES and four (4) 2D ERT traverses were acquired in the study area. VES and 2D ERT data were processed and interpreted using INTERPEX 1X1D and RES2DINV respectively. Sounding curves shows dominant decrease in resistivity with depth while 2D ERT inverse model shows lateral variation in resistivity of the rock layers with lower resistivity at increasing depth. VES geoelectric layer sections show top sideritic clay - clay/shale sequence. Resistivity curve analysis also shows predominance of Q and K curve types. 2D map of aquifer layer resistivity, thickness and depth were constructed. Aquifer layer resistivity range from 2.00 to 190 ?m, while the thickness range from 10 to 75 m and a depth range of 23 to 150 m. The 2D ERT shows layer resistivity range of 0.1 to 250 ?m. Based on the combination of the two techniques for groundwater exploration in the study area, a water table depth of 18 m with a maximum drill depth of about 100 m was determined. However, it is recommended that the selection of point of drill should be on the low saturated regolith as observed on the 2D ERT sections.   Key words: Aquifer, groundwater, VES, 2D ERT, resistivity.

Mapping snow depth and volume at the alpine watershed scale from aerial imagery using Structure from Motion

Time series mapping of snow volume in the mountains at global scales and at resolutions needed for water resource management is an unsolved challenge to date. Snow depth mapping by differencing surface elevations from airborne lidar is a mature measurement approach filling the observation gap operationally in a few regions, primarily in mountain headwaters in the Western United States. The same concept for snow depth retrieval from stereo- or multi-view photogrammetry has been demonstrated, but these previous studies had limited ability to determine the uncertainties of photogrammetric snow depth at the basin scale. For example, assessments used non-coincident or discrete points for reference, masked out vegetation, or compared a subset of the fully snow-covered study domain. Here, using a unique data set with simultaneously collected airborne data, we compare snow depth mapped from multi-view Structure from Motion photogrammetry to that mapped by lidar at multiple resolutions over an entire mountain basin (300 km2). After excluding reconstruction errors (negative depths), SfM had lower snow-covered area (∼27%) and snow volume (∼16%) compared to lidar. The reconstruction errors were primarily in areas with vegetation, shallow snow (< 1 m), and steep slopes (> 60°C). Across the overlapping snow extent, snow depths compared well to lidar with similar mean values (< 0.03 m difference) and snow volume (± 5%) for output resolutions of 3 m and 50 m, and with a normalized median absolute deviation of 0.19 m. Our results indicate that photogrammetry from aerial images can be applied in the mountains but would perform best for deeper snowpacks above tree line.

PRO-VAS: utilizing AR and VSLAM for mobile apps development in visualizing objects

The development of mobile apps with augmented reality (AR) would enhance the capability in visualizing the scene or environment. Any apps supported by computer aided design versions with 3D models makes the design more realistic, such as in the form of websites or mobile apps. However, the current features for online platforms for shopping are quite limited and lack 3D visualization features. This paper presents the development of a mobile application, pro-visualizer app called PRO-VAS, that utilizes AR for scanning and visualizing the environment. PRO-VAS acts as a product visualizer that applies visual simultaneous localization and mapping (VSLAM) for localization of the product in AR based systems. The main components of PRO-VAS are ARCore from Google for interactive purposes, and the depth mapping from red green blue depth (RGB-D) phone camera with point plane generator and markerless tracking method. The last component of the app is the set of objects from the unity store, which can be chosen in PRO-VAS for the scanned scene area. The app was tested in various environments involving different objects and has shown competitive results. In the future, more features and products can be added to the apps.

Depth to sodicity constraint mapping of the Murray-Darling Basin, Australia

The Murray-Darling Basin is a highly significant agricultural region in Australia, contributing AUD $24 billion to the Australian economy each year through food and fibre production. While the region provides favourable conditions for agriculture, it faces a multitude of challenges, including climate change, drought, and soil degradation. The occurrence of excess sodium ions in the soil, referred to as ‘sodicity’, is a form of soil degradation which is also a significant constraint to production. Maps of sodicity exist in the Murray-Darling Basin, but only cover portions of the region and most are only measures of surface Exchangeable Sodium Percentage (ESP), rather than at depth. Therefore, the current study sought to produce 3D maps of ESP at 10 cm depth increments to 100 cm, and maps of depth to ESP constraints across the Murray-Darling Basin. Random Forests served as the model algorithm, and our results provide known measures of accuracy and precision for a transparent and reliably assessed product. We aimed to produce a more parsimonious model, avoiding the inclusion of a superfluous number of covariates, through a simplified recursive feature elimination procedure, which resulted in a reduction of 20 to 9 covariates for modelling. Following model tuning, independent testing showed the 3D model was of a moderate quality (LCCC = 0.66, r2 = 0.49, RMSE = 5.99 %, Bias = 0.14 %). Spatial cross-validation demonstrated that the model did not predict so well at under-represented locations (LCCC = 0.45, r2 = 0.29, RMSE = 7.19 %, Bias = 0.77), producing a substantially lower fit compared to the independent test fit. Area of Applicability analysis supported this notion, identifying that the model was only applicable to small pockets across the region and suggesting we were extrapolating too far beyond the feature space of the training points. Although there is room for improvement, providing transparency via measures of accuracy, precision, and applicability, provides end users with a starting point for further analyses and exploration.

Robust skin microrelief depth estimation using a mobile stereo system.

The skin surface becomes wrinkled and rough due to various internal and external factors. A three-dimensional (3D) analysis of the skin is required to improve skin conditions. Stereophotogrammetry, a noninvasive 3D analysis method, is easy to install and use, but most stereo systems have a fixed baseline and scale. Previous stereo systems are not suitable for observing micro-range skin features. Therefore, we suggest the optimal conditions and methods for the 3D analysis of skin microrelief using a multi-conditioned stereo system. We constructed a nonconvergence model using a mobile device and acquired stereo images under multiscale and multi-baseline conditions. We extracted 3D information of the skin through our process: preprocessing, skin feature extraction, feature matching, and actual depth mapping. We improved the accuracy of the 3D analysis of the skin by using disparity values instead of disparity maps. We compared and analyzed the performances of six local feature detector and descriptor algorithms. In addition, we suggested depth-mapping formulas to estimate the actual depth of the skin microrelief. We confirmed that stereo images with a working distance of 70-75mm and a baseline of 4-8mm are effective for the 3D analysis of skin microrelief. In addition, accelerated KAZE exhibited the best performance for features extraction and stereo matching. Finally, the extracted 3D information was converted to the actual depth, and the performance of the 3D analysis was verified. The proposed system and method that provide texture information are effective for 3D skin disease analysis and evaluation.

Identification of Groundwater Bearing Zones Using Geoelectrical and Electromagnetic Techniques at Tourah Area, South of Cairo - Egypt

The complex geology of Tourah area, south Cairo is a serious challenge for groundwater exploration, where the risk of unsuccessful groundwater drilling excavation for industrial purposes is well dressed, raising the need for better geophysical subsurface detection and characterization approaches in terms of water bearing and aquitard zones. Datasets from the study area were acquired using integrated geophysical techniques comprising of one Dimensional Vertical Electrical Soundings (1D VES) and one Dimensional Transient Electromagnetic (1D TEM) as well as two-Dimensional Electrical Imaging (2D ERI) which was restricted to the available spreading space as well as subsurface infrastructure noise. The results of 1D VES and 1D TEM soundings detected three to five geoelectric layers used to generate geoelectrical cross sections and maps of resistivity, thickness, and depth to water bearing zone. Moreover, the 2D ERI inversion profiles were able to image the first three layers and the depth of the water bearing zone [A] with significantly greater resolution due to their higher lateral and vertical resolutions compared to the traditional 1D VES and TEM interpolated cross section. The integration of the three geophysical methods displayed a smooth distribution of both marly limestone and fractured shaly limestone and claystone bands water bearing zones [A and B]. Resistivity values of the water bearing zone [A] ranges from 0.1 to 24 Ω m, with average thickness of 4 to 12 m, and depth to water of 1.5 to 13.6 m. In addition, [zone B] is the main water bearing zone of fractured shaly limestone and claystone bands (2 to 14 Ω m) which is detected at deep depth of 40 and 75 m. Two normal fault systems inferred from 1D interpretation are considered responsible for local recharging of the main Eocene aquifer over the study area. In conclusion, the outlined water bearing thickness as well as limited sources of groundwater recharge indicate that these water bearing zones may not provide a sustainable supply for the industrial development purposes in the future.

Mapping snow depth and spatial variability using SFM photogrammetry of UAV images over rugged mountainous regions of the Western Himalaya

Accurate snow depth observations are one of the most important inputs in the process of avalanche hazard estimation, forecasting, and mitigation. This study presents the high-resolution snow depth mapping of avalanche-prone regions of the Manali–Dhundi area, Himachal Pradesh (HP), India, using, repeated UAV Photogrammetric surveys. The images captured by an RGB camera mounted on a fixed-wing UAV (e-Bee X) were used to generate the high-resolution Digital Surface Model (DSM) and Orthophotos of the study area. Mapping of snow depth of the study area is crucial for avalanche hazard assessment and safe movement of the personnel and vehicles passing through the highways in this area during winters. A total of 12 km2 area covering six major avalanche sites was surveyed. A snow depth map was generated by differencing co-registered snow-covered DSM and bare surface DSM. UAV-derived snow depth values have been validated with the point snow depth measurements of Wireless Sensor Networks (WSN), manual observation station, and snow depth around a structure of known height in the survey area. The UAV-retrieved snow depth values were found in good correlation with field-measured snow depth (R = 0.95). The maximum and minimum observed snow depth values at different locations on open slopes and flat areas were 1.62 m and 0.79 m. The estimated Mean Error (ME) and RMSE based on these measured values were found 0.15 and 0.21 m, respectively. The study reveals that the snow depth map generated from the UAV survey provides reliable estimates of snow depth values for flat areas and open slopes but the method is not suitable for forested areas. The study also infers that the statistical measures (i.e., µ, σ, RMSE, etc.) derived from the complete survey dataset without considering topographic factors and features of the region will not provide realistic estimates of snow depth and its accuracy. The study suggests that regular monitoring (before and after every snow storm) of the snow depth using a UAV-based method is quick, cost-effective, and provides accurate snow conditions for operational use and planning in snow-bound regions.

Satellite-Derived Bottom Depth for Optically Shallow Waters Based on Hydrolight Simulations

The bottom depth of coastal benthic habitats plays a vital role in the coastal ecological environment and navigation. In optically shallow waters (OSWs), seafloor reflectance has an impact on the remotely sensed data, and thus, water depth can be retrieved from the remote sensing reflectance (Rrsλ) values provided by satellite imagery. Empirical methods for depth estimation are mainly limited by field measurements coverage. In addition, owing to the diverse range of water bio-optical properties in coastal regions, the high-precision models that could be applied to all OSWs are insufficient. In this study, we developed a novel bottom-depth retrieval method based on Hydrolight simulated datasets, in which Rrsλ were generated from radiative transfer theory instead of actual satellite data. Additionally, this method takes into consideration the variable conditions of water depth, chlorophyll concentrations, and bottom reflectance. The bottom depth can be derived from Rrsλ using a data-driven machine learning method based on the random forest (RF) model. The determination coefficient (R2) was greater than 0.98, and the root mean squared error (RMSE) was less than 0.4 m for the training and validation datasets. This model shows promise for use in different coastal regions while also broadening the applications that utilize satellite data. Specifically, we derived the bottom depth in three areas in the South China Sea, i.e., the coastal regions of Wenchang city, Xincun Bay, and Huaguang Reef, based on Sentinel-2 imagery. The derived depths were validated by the bathymetric data acquired by spaceborne photon-counting lidar ICESat-2, which was able to penetrate clean shallow waters for sufficient bottom detection. The predicted bottom depth showed good agreement with the true depth, and large-scale mapping compensated for the limitations resulting from along-track ICESat-2 data. Under a variety of circumstances, this general-purpose depth retrieval model can be effectively applied to high spatial resolution imagery (such as that from Sentinel-2) for bottom depth mapping in optically shallow waters.

Potential Obstacle Detection Using RGB to Depth Image Encoder-Decoder Network: Application to Unmanned Aerial Vehicles.

In this work, a new method is proposed that allows the use of a single RGB camera for the real-time detection of objects that could be potential collision sources for Unmanned Aerial Vehicles. For this purpose, a new network with an encoder–decoder architecture has been developed, which allows rapid distance estimation from a single image by performing RGB to depth mapping. Based on a comparison with other existing RGB to depth mapping methods, the proposed network achieved a satisfactory trade-off between complexity and accuracy. With only 6.3 million parameters, it achieved efficiency close to models with more than five times the number of parameters. This allows the proposed network to operate in real time. A special algorithm makes use of the distance predictions made by the network, compensating for measurement inaccuracies. The entire solution has been implemented and tested in practice in an indoor environment using a micro-drone equipped with a front-facing RGB camera. All data and source codes and pretrained network weights are available to download. Thus, one can easily reproduce the results, and the resulting solution can be tested and quickly deployed in practice.

Mapping of subsurface geological structures and depth to the top of magnetic basement in Bornu Basin and its environs, NE Nigeria, for possible hydrocarbon presence

Mapping of subsurface geological structures and depth to the top of magnetic basement in Bornu Basin and its environs, NE Nigeria, for possible hydrocarbon presence