Vertical Data Research Articles

Integration of Local Mean Sea Level and Land Vertical Datum over Peninsular Malaysia via Transformation Model

Integration of Local Mean Sea Level and Land Vertical Datum over Peninsular Malaysia via Transformation Model

Evaluating Bicycle Path Roughness: A Comparative Study Using Smartphone and Smart Bicycle Light Sensors

The quality of bicycle path surfaces significantly influences the comfort of cyclists. This study evaluates the effectiveness of smartphone sensor data and smart bicycle lights data in assessing the roughness of bicycle paths. The research was conducted in Hasselt, Belgium, where various bicycle path pavement types, such as asphalt, cobblestone, concrete, and paving tiles, were analyzed across selected streets. A smartphone application (Physics Toolbox Sensor Suite) and SEE.SENSE smart bicycle lights were used to collect GPS and vertical acceleration data on the bicycle paths. The Dynamic Comfort Index (DCI) and Root Mean Square (RMS) values from the data collected through the Physics Toolbox Sensor Suite were calculated to quantify the vibrational comfort experienced by cyclists. In addition, the data collected from the SEE.SENSE smart bicycle light, DCI, and RMS computed results were categorized for a statistical comparison. The findings of the statistical tests revealed no significant difference in the comfort assessment among DCI, RMS, and SEE.SENSE. The study highlights the potential of integrating smartphone sensors and smart bicycle lights for efficient, large-scale assessments of bicycle infrastructure, contributing to more informed urban planning and improved cycling conditions. It also provides a low-cost solution for the city authorities to continuously assess and monitor the quality of their cycling paths.

Ultra-Short-Term Wind Power Forecasting in Complex Terrain: A Physics-Based Approach

This paper proposes a method based on Computational Fluid Dynamics (CFD) and the detection of Wind Energy Extraction Latency for a given wind turbine (WT) designed for ultra-short-term (UST) wind energy forecasting over complex terrain. The core of the suggested modeling approach is the Wind Spatial Extrapolation model (WiSpEx). Measured vertical wind profile data are used as the inlet for stationary CFD simulations to reconstruct the wind flow over a wind farm (WF). This wind field reconstruction helps operators obtain the wind speed and available wind energy at the hub height of the installed WTs, enabling the estimation of their energy production. WT power output is calculated by accounting for the average time it takes for the turbine to adjust its power output in response to changes in wind speed. The proposed method is evaluated with data from two WTs (E40-500, NM 750/48). The wind speed dataset used for this study contains ramp events and wind speeds that range in magnitude from 3 m/s to 18 m/s. The results show that the proposed method can achieve a Symmetric Mean Absolute Percentage Error (SMAPE) of 8.44% for E40-500 and 9.26% for NM 750/48, even with significant simplifications, while the SMAPE of the persistence model is above 15.03% for E40-500 and 16.12% for NM 750/48. Each forecast requires less than two minutes of computational time on a low-cost commercial platform. This performance is comparable to state-of-the-art methods and significantly faster than time-dependent simulations. Such simulations necessitate excessive computational resources, making them impractical for online forecasting.

Ready, Set, Modernize – ASPRS Takes on Preparing the Geospatial Industry for the Modernized National Spatial Reference System (NSRS)

The National Geodetic Survey (NGS) is modernizing the National Spatial Reference System (NSRS) in the United States. The modernization involves significant updates to the official reference frames and vertical datum used across the country, affecting the entire geospatial industry. Key benefits of the Modernized NSRS will include improved accuracies and enhanced interoperability and sustainability of geospatial data and systems. The ASPRS NSRS Modernization Working Group produced this paper to help prepare the geospatial industry for the upcoming changes. It serves as a guide for industry professionals to understand the implications of the Modernized NSRS and recommendations to begin preparing for it. It emphasizes the importance of proactive measures to ensure a smooth transition to the new reference frames and vertical datum.

Estimation of Terrestrial Water Storage Changes in Brazil From the Joint Inversion of GRACE‐Based Geopotential Difference and GNSS Vertical Displacement Data

AbstractGravity Recovery and Climate Experiment (GRACE) satellite gravimetry and Global Navigation Satellite System (GNSS) surface displacement measurements offer complementary advantages for monitoring terrestrial water storage (TWS) changes. We propose a new joint inversion model based on the combination of GRACE‐based geopotential difference (GPD) observations using the mascon method and GNSS vertical displacements through the Green's function method to obtain reliable TWS changes in Brazil. The performance of the jointly inverted TWS changes is assessed through closed‐loop simulations and comparisons with hydrometeorological data (precipitation‐P, evapotranspiration‐ET, and runoff‐R) using water budget closure (P‐ET‐R) and river water level observations from satellite altimetry. The simulation results indicate that the joint inversion results exhibit higher accuracy and reliability than GRACE GPD‐based mascon (GPD‐mascon) and GNSS solutions, and the standard deviations (STDs) of joint results decrease by ∼6.98 and ∼37.5 mm compared to those from GPD‐mascon and GNSS solutions. The joint inversion of real GRACE and GNSS data demonstrates notably lower uncertainty than that of GPD‐mascon solutions and exhibits significant improvement than GNSS‐only solutions. The STDs and correlation coefficients between monthly R time series derived from three inversion methods (joint inversion, GPD‐mascon and GNSS solutions, combined with P and ET through water budget closure) and in situ observations are 19.607 mm and 0.912, 20.879 mm and 0.904, and 31.370 mm and 0.778, respectively. The joint estimates also yield better correlation with river water level observations compared to GPD‐mascon and GNSS solutions. Furthermore, at the weekly time scale, the joint inversion results show better consistency with P‐ET‐R data than those from GPD‐mascon and GNSS solutions.

Nonstationary adaptive S-wave leakage suppression of ocean-bottom node data

Ocean-bottom nodes (OBNs) are widely used because of their wide azimuth, long-offset, and low-frequency advantages. However, in the vertical component of the OBN geophone, a significant amount of S-wave induced noise may be recorded. This significantly impacts the quality of the vertical component data and may affect the follow-up merging of dual-sensor data. Some commercially available methods apply normal-moveout correction, which requires velocity data. We avoid this with an adaptive matching method, which relies solely on seismic data. Therefore, we develop a novel adaptive subtraction method for S-wave leakage suppression using the horizontal component as the noise model. Our method effectively handles the nonstationarity of the input seismic data in all time and space directions, mitigating instability caused by manual selection of the smoothing radius. A method is introduced for estimating a global nonstationary smoothing radius using the noise model. Compared with commercial and stationary smoothing methods, our method can better suppress S-wave noise while balancing residual noise and signal leakage more effectively. Synthetic and field data examples demonstrate significant improvement with our method.

Understanding the impact of deep structures on the hydrological setting of the Eastern Bahira Basin in Morocco using combined geophysical analysis of gravity, seismic, and electrical resistivity data

In arid regions like the Eastern Bahira Basin, communities mainly rely on groundwater for drinking and irrigation. However, efficiently managing these vital resources requires a deep understanding of the underlying aquifers’ structure and identifying the most suitable areas for exploitation. This presents a significant challenge for the success of water supply and irrigation programs in the Eastern Bahira Basin. This study is based on an integrative approach, combining Electrical Resistivity Tomography data, with the compilation and reinterpretation of pre-existing seismic, gravimetric and vertical electrical sounding data. This approach is based on compiling old gravimetric data and applying advanced processing techniques to determine the horizontal gradient maxima, which helps highlight the major structural alignments in the basin. Furthermore, the approach utilizes seismic data in order to enhance understanding of the deep structure of the basin, reinterpreting it in light of recent drilling data. The interpretation of the gravimetric and seismic data has also been validated by the results of vertical electrical soundings and electrical tomography that we recently acquired in the Eastern Bahira basin. The outcomes of this research provide new insights into the deep structure of the Eastern Bahira Basin and suggest the most promising hydrogeological prospects, thereby contributing to the success of the ongoing drinking water supply and irrigation program in the Eastern Bahira Basin.

Total water level prediction at continental scale: Coastal ocean

We demonstrate recent progress made in the simulation of total water level (TWL) at continental scale, using the coastal ocean of US East Coast/Gulf of Mexico coast as an example. A key difference between the continental-scale and small-scale modeling is that the former requires a more accurate vertical datum. Using a geoid-based datum (xGEOID20b), a satellite altimetry product, and a state-of-the-art 3D unstructured-grid model, we significantly improve the accuracy for TWL both near- and off-shore. The average root-mean-square error at all NOAA stations is 14 cm. The non-tidal signals are found to be sensitive to the representation of a large-scale current system near the boundary and extending the domain extent to accommodate this system improves these signals.

Planning, emphasis and proposal for implementation of gravimetric control network in Israel

AbstractThis study outlines the planning, emphases, and proposed implementation of a gravimetric control network for Israel. The establishment of a comprehensive and accurate gravimetric control network is crucial for various geodetic applications, including geoid determination, gravity field modelling, vertical datum establishment, and geophysical studies. This study aims to provide a framework for the development and implementation of such a network in Israel. The proposed network is divided into two orders. The first order, also known as the absolute order or zero order, comprises four stations situated in geologically stable and tranquil locations. The second order, referred to as the first order, is established through relative gravimetric measurements. This order will encompass 29 stations positioned in areas that are both stable and secure. The zero and first order stations will be evenly distributed throughout the country to ensure homogeneous coverage. The proposed gravimetric network is in conjunction with the requirements to improve the geoid undulation model of Israel. Through the establishment of a small gravimetric network and the implementation of relative gravimetric measurements, we were able to determine the optimal control point density and establish a well-structured methodology for measurement, analysis, and the adjustment process.

GDEMM2024: Global Digital Elevation Merged Model 2024 for surface, bedrock, ice thickness, and land-type masks

Various research topics in geosciences such as gravity modelling, terrain correction and ocean circulation, require high resolution and accuracy global elevations for land topography, bathymetry, and ice thickness that refer to a consistent vertical datum. Unfortunately, most of the existing DEMs do not provide such solutions for Earth relief layers with the same resolution globally. To overcome this deficiency, we merged various DEMs published in the recent years and compiled an up-to-date global solution. We provide 30 arcsecond grid suite for relief layers and land-type masks which have been substantially improved w.r.t. the grids in literature. The quality of the merged surface elevation is assessed against the GNSS heights at about globally distributed 22000 stations. The merged surface model shows a reduction in standard deviation of a factor of three compared to other commonly used DEMs. Other evaluations are performed over land-ice and oceans which supports the advancement of GDEMM2024. The improvements are due to the accuracy and coverage of the original input data, updated land-type masks and merging methodology.

Climatology of Atmospheric Unstable Layers Revisited: A Corrigendum

Abstract We have published a recent paper on differences between temperature fluctuations of various vertical scales in raw and processed U.S. high vertical resolution radiosonde data (HVRRD). In that paper, we note that the small-scale temperature fluctuations in the raw U.S. HVRRD are significantly larger than those in the processed U.S. HVRRD and that those small-scale temperature fluctuations are much larger during daytime that during nighttime. We believe that this is due to the varying amount of solar radiation falling on the radiosonde temperature sensor as the radiosonde instrument swings and rotates. In light of these new results, we present revisions to some of our conclusions about the climatology of atmospheric unstable layers. When we repeat our calculations of atmospheric unstable layers using the processed U.S. HVRRD, we find the following. 1) The 0000/1200 UTC differences in unstable layer occurrences in the lower stratosphere that were noted in our earlier paper essentially disappear. 2) The “notch” in the deep tropics where there is a relative deficiency of thin unstable layers and a corresponding excess of thicker layers is still a feature when processed data are analyzed, but the daytime notch is less marked when the processed data were used. 3) The discontinuity in unstable layer occurrences, when there was a change in radiosonde instrumentation, is still present when processed data are analyzed, but is diminished from what it was when the raw data were analyzed. Significance Statement In a previous paper deriving the climatology of atmospheric unstable layers, we emphasized several findings. We reexamine three of the main points of that paper when processed U.S. high vertical resolution radiosonde data are analyzed instead of the raw data used in that previous paper. We find the 0000/1200 UTC differences virtually disappear in the new analysis. We find that the “notch” feature previously noted at Koror still exists, and we find that the discontinuity in unstable layers, when radiosonde instrumentation is changed, is diminished, but is still present in the new analysis.

Utilizing geophysical methods for assessing groundwater resources in the Dijil River Catchment, Northwestern Ethiopia

This study utilizes geophysical methods to assess groundwater resources in the Dijil River catchment near Debremarkos Town, Northwestern Ethiopia. Recent alluvial deposits and volcanic rocks of varying ages characterize the area. The aim is to map subsurface formations and evaluate groundwater potential. Two hundred twenty-eight magnetic data were collected, mainly oriented in NE-SW, NW-SE, E-W, and N-S directions, and twenty-four Vertical Electrical Sounding data utilizing the Schlumberger configuration. The results of the magnetic data reveal lineaments in different directions in the study area. Most of the Geoelectric sections show three layers, of which the second or the third layers are aquifers having minimum and maximum resistivity of 6 and 155 Ohm.m, respectively, and average resistivity range of 13–50 Ohm.m with a thickness ranging 24–200m. The layers represent alluvial deposits, highly weathered and fractured basalt (major aquifer zone), and moderately to slightly weathered and fractured basalt. Shallow and deeper low resistivity horizons, indicating groundwater saturation zones, are visible. The integrated geophysical survey aligns well with available borehole data.

Application of vertical seismic profile multi-component data to tight sandstone gas reservoirs

Abstract Given the complex geological characteristics of the tight gas sandstone in the Shaximiao Formation of the Jurassic in the central Sichuan Basin, including its wide distribution, narrow width, and thin thickness, conventional surface seismic methods face significant challenges in reservoir delineation. To address this challenge, a vertical seismic profile (VSP) survey was conducted in the region in 2022, along with the simultaneous collection of 3D3C surface seismic data. This article mainly elaborates on a case study of using multi-component VSP technology to delineate tight sandstone gas reservoirs. By comparing the processed VSP imaging volume with the surface seismic imaging volume, it was discovered that the frequency width of the VSP image was 8 to 12 Hz higher than that of the surface seismic image. Utilizing the multi-component information in VSP data, it is feasible to more precisely delineate tight gas sandstones, thereby reducing the uncertainty of data interpretation. Based on the conventional data interpretation of these images, an innovative application suggests using the centroid frequency ratio of PSv to PP waves as an indicator for tight sandstone. The results show that this attribute has high sensitivity to this type of reservoir. Combining geological, drilling, and logging data, an interpretation template was established based on VSP data to interpret 3D3C surface seismic data and propose new drilling well locations. The conclusion is that the successful application of VSP technology cannot be achieved without the integration of multiple disciplines. The combined exploration technology of multi-component VSP and 3D3C surface seismic has been demonstrated to have high application value through practical application, significantly improving the success rate of gas wells and providing effective support for the exploration and development of tight sandstone gas reservoirs.

Enhancing visual seismocardiography in noisy environments with adaptive bidirectional filtering for Cardiac Health Monitoring

BackgroundWearable sensors have revolutionized cardiac health monitoring, with Seismocardiography (SCG) at the forefront due to its non-invasive nature. However, the substantial motion artefacts have hindered the translation of SCG-based medical applications, primarily induced by walking. In contrast, our innovative technique, Adaptive Bidirectional Filtering (ABF), surpasses these challenges by refining SCG signals more effectively than any motion-induced noise. ABF leverages a noise-cancellation algorithm, operating on the benefits of the Redundant Multi-Scale Wavelet Decomposition (RMWD) and the bidirectional filtering framework, to achieve optimal signal quality.MethodologyThe ABF technique is a two-stage process that diminishes the artefacts emanating from motion. The first step by RMWD is the identification of the heart-associated signals and the isolating samples with those related frequencies. Subsequently, the adaptive bidirectional filter operates in two dimensions: it uses Time-Frequency masking that eliminates temporal noise while engaging in non-negative matrix Decomposition to ensure spatial correlation and dorsoventral vibration reduction jointly. The main component that is altered from the other filters is the recursive structure that changes to the motion-adapted filter, which uses vertical axis accelerometer data to differentiate better between accurate SCG signals and motion artefacts.OutcomeOur empirical tests demonstrate exceptional signal improvement with the application of our ABF approach. The accuracy in heart rate estimation reached an impressive r-squared value of 0.95 at − 20 dB SNR, significantly outperforming the baseline value, which ranged from 0.1 to 0.85. The effectiveness of the motion-artifact-reduction methodology is also notable at an SNR of − 22 dB. Consequently, ECG inputs are not required. This method can be seamlessly integrated into noisy environments, enhancing ECG filtering, automatic beat detection, and rhythm interpretation processes, even in highly variable conditions. The ABF method effectively filters out up to 97% of motion-related noise components within the SCG signal from implantable devices. This advancement is poised to become an integral part of routine patient monitoring.

Top to bottom interactive velocity analysis using travel-time ellipses and first break times from walkaway vertical seismic profiling data: a synthetic study

Top to bottom interactive velocity analysis using travel-time ellipses and first break times from walkaway vertical seismic profiling data: a synthetic study

An Artificial Neural Network approach to assess road roughness using smartphone-based crowdsourcing data

Monitoring road surface conditions is a crucial task for road authorities to develop effective infrastructure maintenance programs. Despite smartphones have been introduced as cost-effective and real-time solution for this purpose, several challenges must be addressed before their real-world application. This study investigates the utilization of smartphone-based crowdsourcing data and Artificial Neural Networks (ANN) to enhance the precision of road surface condition estimation. Initially, data are collected from four different smartphone models mounted in various vehicles, including vertical acceleration, geographic location, and speed. The root mean square of the vertical acceleration data, along with vehicle speed, is then employed as input features for the ANN, while the true International Roughness Index (IRI) values serve as the corresponding output features. Comparative analysis between ANN and regression models based on statistical metrics such as Mean Squared Error (MSE) and Pearson correlation revealed that ANN outperforms regression models. The obtained MSE and Pearson correlation values for ANN (0.56 and 0.91) surpass those of regression models (0.72 and 0.88). Moreover, results indicated that utilizing crowdsourcing smartphone data yielded superior outcomes compared to using a single smartphone for this purpose.

Improving Atmospheric Temperature and Relative Humidity Profiles Retrieval Based on Ground-Based Multichannel Microwave Radiometer and Millimeter-Wave Cloud Radar

Obtaining temperature and humidity profiles with high vertical resolution is essential for describing and predicting atmospheric motion, and, in particular, for understanding the evolution of medium- and small-scale weather processes, making short-range and near-term weather forecasting, and implementing weather modifications (artificial rainfall, artificial rain elimination, etc.). Ground-based microwave radiometers can acquire vertical tropospheric atmospheric data with high temporal and spatial resolution. However, the accuracy of temperature and relative humidity retrieval is still not as accurate as that of radiosonde data, especially in cloudy conditions. Therefore, improving the observation and retrieval accuracy is a major challenge in current research. The focus of this study was to further improve the accuracy of atmospheric temperature and humidity profile retrieval and investigate the specific effects of cloud information (cloud-base height and cloud thickness) on temperature and humidity profile retrieval. The observation data from the ground-based multichannel microwave radiometer (GMR) and the millimeter-wave cloud radar (MWCR) were incorporated into the retrieval process of the atmospheric temperature and relative humidity profiles. The retrieval was performed using the backpropagation neural network (BPNN). The retrieval results were quantified using the mean absolute error (MAE) and root mean square error (RMSE). The statistical results showed that the temperature profiles were less affected by the cloud information compared with the relative humidity profiles. Cloud thickness was the main factor affecting the retrieval of relative humidity profiles, and the retrieval with cloud information was the best retrieval method. Compared with the retrieval profiles without cloud information, the MAE and RMSE values of most of the altitude layers were reduced to different degrees after adding cloud information, and the relative humidity (RH) errors of some altitude layers were reduced by approximately 50%. The maximum reduction in the RMSE and MAE values for the retrieval of temperature profiles with cloud information was about 1.0 °C around 7.75 km, and the maximum reduction in RMSE and MAE values for the relative humidity profiles was about 10%, which was obtained around 2 km.

Unconventional free fall measurements of g: involving the Pythagorean Theorem and inverse tangent function

Abstract The current work presents unconventional but instructionally useful approaches to data analysis and modelling that allow for measurements of the acceleration due to gravity, g, using free-falling motion and the popular Tracker software. The two techniques detailed in this article illustrate the employability of the Pythagorean Theorem and the inverse tangent function in modelling free-falling motion that likewise, expectedly, permit for the determination of g. These two mathematical concepts are brought to relevance by strategically situating the origin of the coordinate system at an appropriate point. The models returned values of the gravitational acceleration that are comparable to that by the traditional technique, the one directly based on the vertical position-time data. Additionally, the unconventional free-fall data analyses presented in this paper suggest extra and insightful activities for the students, exercises on a notably less-automated nonlinear curve fitting on a spreadsheet (MS Excel) at most. Ultimately, by applying familiar mathematical concepts associated with a right triangle to nontraditional contexts like free-fall, this laboratory or lecture activity is perceived to spark greater interest of the students and enhance learning by unifying theory and experimental data.

Differences among the total electron content derived by radio occultation, global ionospheric maps and satellite altimetry

In recent years, significant progress has been in ionospheric modeling research through data ingestion and data assimilation from a variety of sources, including ground-based global navigation satellite systems, space-based radio occultation and satellite altimetry (SA). Given the diverse observing geometries, vertical data coverages and intermission biases among different measurements, it is imperative to evaluate their absolute accuracies and estimate systematic biases to determine reasonable weights and error covariances when constructing ionospheric models. This study specifically investigates the disparities among the vertical total electron content (VTEC) derived from SA data of the Jason and Sentinel missions, the integrated VTEC from the Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) and global ionospheric maps (GIMs). To mitigate the systematic bias resulting from differences in satellite altitudes, the vertical ranges of various VTECs are mapped to a standardized height. The results indicate that the intermission bias of SA-derived VTEC remains relatively stable, with Jason-1 serving as a benchmark for mapping other datasets. The mean bias between COSMIC and SA-derived VTEC is minimal, suggesting good agreement between these two space-based techniques. However, COSMIC and GIM VTEC exhibit remarkable seasonal discrepancies, influenced by the solar activity variations. Moreover, GIMs demonstrate noticeable hemispheric asymmetry and a degradation in accuracy ranging from 0.7 to 1.7 TECU in the ocean-dominant Southern Hemisphere. While space-based observations effectively illustrate phenomena such as the Weddell Sea anomaly and longitudinal ionospheric characteristics, GIMs tend to exhibit a more pronounced mid-latitude electron density enhancement structure.

An radicals construction technique based on dual quaternions and hierarchical transformers

At present, the exploration of Knowledge Graph Embedding (KGE) mainly focuses on static KGE models. Due to the diversity and complexity of vertical domain data, it is difficult for existing KGE models to achieve excellent performance. Specific KGE models must be constructed for research on vertical domain knowledge graphs. This paper explores the relationships between the composition of Chinese characters and their sounds, meanings, and forms by constructing the Chinese Radical Knowledge Graph (CRKG). CRKG was constructed from a unique perspective according to the characteristics of Chinese radicals and knowledge graphs. The minor components of Chinese characters are separated and constructed into the fourth dimension of the knowledge graph. In this paper, we propose a new Chinese Radical Knowledge Graph Embedding (CRKGE) model, RotAL, which introduces dual quaternions and hierarchical transformers into KGE. RotAL performs a translation-rotation operation on the head entity with the relation as a parameter, then does a translation operation on the head entity with the minor component as a parameter to be close to the tail entity. Finally, we feed the trained quaternions into hierarchical transformers to jointly learn entity-related composition and relational contextualization based on a head entity’s neighborhood. We demonstrate that our method can model and infer various relational patterns of CRKG, such as symmetric/antisymmetric, reflexive, inversion, and multiple. The experimental results on four CRKG datasets demonstrate that the RotAL model achieves the highest Hit@1 score, outperforming existing KGE models in accurately identifying the target entity on the first attempt. Furthermore, the RotAL model consistently achieves superior performance across other evaluation metrics, substantiating its significant advantage over existing models.