Differential GPS Research Articles

Designers of Nature's Subterranean Abodes: Insights into the Architecture and Utilization of Burrow Systems of Thomas' Pine Vole, Microtus thomasi (Rodentia: Arvicolinae).

Microtus thomasi (Rodentia: Arvicolinae), a fossorial vole endemic to the SW Balkans, uses a variety of substrates but its underground behavior remains poorly understood. This study examines the architecture and utilization of M. thomasi burrow systems in NW Peloponnese, Greece. In particular, eight burrow systems were meticulously excavated and studied, with comprehensive measurements taken of key characteristics, including length, depth, soil mounds, and surface openings. Key coordinates were recorded using a differential GPS device for detailed mapping and fractal dimension analysis using the box-counting method was employed to assess burrow system complexity. Soil samples were analyzed for particle size and chemical composition, and vegetation types at each site were identified. We did not find statistically significant correlations between size and complexity of the burrow systems and soil composition, altitude, or specific soil components. On the other hand, we did observe statistically significant differences in tunnel diameter between two burrow systems and in tunnel depth between more. Moreover, our study showed that more than one same-sex individual can occupy a single burrow system and not just an adult male-female pair, that was previously recorded, indicating the need for further study of the social behavior of this vole species. This study provides valuable insights into the underground behavior of M. thomasi by providing information on the features of its burrow systems, thus contributing to our understanding of its biology.

Review of the Monothematic Series of Publications Concerning Research on Statistical Distributions of Navigation Positioning System Errors

This review presents the main results of the author’s study, obtained as part of the post-doctoral (habilitation) dissertation entitled “Research on Statistical Distributions of Navigation Positioning System Errors”, which constitutes a series of five thematically linked scientific publications. The main scientific aim of this series is to answer the question of what statistical distributions follow the position errors of navigation systems, such as Differential Global Positioning System (DGPS), European Geostationary Navigation Overlay Service (EGNOS), Global Positioning System (GPS), and others. All of the positioning systems under study (Decca Navigator, DGPS, EGNOS, and GPS) are characterised by the Position Random Walk (PRW), which means that latitude and longitude errors do not appear randomly, being a feature of the normal distribution. The research showed that the Gaussian distribution is not an optimal distribution for the modelling of navigation positioning system errors. A higher fit to the 1D and 2D position errors was exhibited by such distributions as beta, gamma, and lognormal. Moreover, it was proven that the Twice the Distance Root Mean Square (2DRMS(2D)) measure, which assumes a priori normal distribution of position errors in relation to latitude and latitude, was smaller by 10–14% than the position error value from which 95% fixes were smaller (it is known as the R95(2D) measure).

A biomass map of the Brazilian Amazon from multisource remote sensing

The Amazon Forest, the largest contiguous tropical forest in the world, stores a significant fraction of the carbon on land. Changes in climate and land use affect total carbon stocks, making it critical to continuously update and revise the best estimates for the region, particularly considering changes in forest dynamics. Forest inventory data cover only a tiny fraction of the Amazon region, and the coverage is not sufficient to ensure reliable data interpolation and validation. This paper presents a new forest above-ground biomass map for the Brazilian Amazon and the associated uncertainty both with a resolution of 250 meters and baseline for the satellite dataset the year of 2016 (i.e., the year of the satellite observation). A significant increase in data availability from forest inventories and remote sensing has enabled progress towards high-resolution biomass estimates. This work uses the largest airborne LiDAR database ever collected in the Amazon, mapping 360,000 km2 through transects distributed in all vegetation categories in the region. The map uses airborne laser scanning (ALS) data calibrated by field forest inventories that are extrapolated to the region using a machine learning approach with inputs from Synthetic Aperture Radar (PALSAR), vegetation indices obtained from the Moderate-Resolution Imaging Spectroradiometer (MODIS) satellite, and precipitation information from the Tropical Rainfall Measuring Mission (TRMM). A total of 174 field inventories geolocated using a Differential Global Positioning System (DGPS) were used to validate the biomass estimations. The experimental design allowed for a comprehensive representation of several vegetation types, producing an above-ground biomass map varying from a maximum value of 518 Mg ha−1, a mean of 174 Mg ha−1, and a standard deviation of 102 Mg ha−1. This unique dataset enabled a better representation of the regional distribution of the forest biomass and structure, providing further studies and critical information for decision-making concerning forest conservation, planning, carbon emissions estimate, and mechanisms for supporting carbon emissions reductions.

Twenty-Two Years of GPS Monitoring at Rabaul Caldera, a Narrative History

It has long been recognised that volcanoes deform as fluids migrate, or change pressure in fractures and reservoirs within the volcano or in the crust below and around them. Calderas in particular have been shown to deform in complex and often major ways. The Rabaul Caldera is a type example of a caldera that undergoes complex and occasionally rapid deformation. This was first recognised by visual observations, and by the 1970s these movements were being monitored by traditional surveying techniques. Between 1972 and 1994, the centre of the caldera was uplifted by approximately 2 m. Following the 1994 eruption, it was indirectly found that parts of the caldera were uplifted ~6 m in the final hours before the eruption. It was realized that ‘real-time’ monitoring of the uplift may have given a better warning that an eruption was imminent. Traditional surveying techniques are time consuming; in the late 1990s, the only option for real-time monitoring was a Global Positioning System (GPS). By early 2000, a real-time GPS system was working at Rabaul Volcanological Observatory (RVO). Twenty-two years of continually recording differential GPS or Global Navigational Satellite System (GNSS) has proven the technique to be of immense importance. Often it has been the only parameter showing that unrest is happening. At times, inflation and deflation have warned of impending activity or recorded the emptying of the system; at other times, patterns of deformation have been more difficult to interpret. The technique has proven its worth in monitoring the status or general ‘health’ of the caldera, but for more precise forecasts it can only form part of an integrated monitoring system. Current testing of much cheaper receivers and improvements in telemetry mean the technique may soon be available for the more remote volcanoes of Papua New Guinea.

Geospatial Approach to Route Optimisation for Security Architecture Improvement

Understanding the complexity of road networks is tiring, especially in finding the optimum routes for emergency response. Dynamic route optimisation is a decision-making process powered by mathematical models to formulate a strong basis for predicting the best path between any two points in a road network. Exploring the numerous interconnected routes in and out of a road network provides a strong foothold for securing lives and properties. This study presents the findings of route optimisation at the Federal University of Technology Minna, Gidan Kwano Campus. High Target Differential Global Positioning System receiver (DGPS), Handheld GPS (Garmin GPSmap 60Cx), Quick Bird Satellite Imagery, and Google Earth Images were used for data acquisition purposes. An ArcGIS network analyst for optimum route identification based on Dijktra’s algorithm was utilised for analysis. Five different optimisation tests were done and the optimised routes were compared with alternative routes from the network. The total travel distances for alternative and optimum routes are 11852.414 m and 16614.156 m respectively while the total travel times for alternative and optimum routes are 2135.35 s and 2993.25 s respectively. The study revealed all optimum routes are faster cumulatively by (11.7%) and are best for security and emergency responses. Ten unauthorised access roads to the campus were identified. The number of routes paving access to the campus shows that efforts towards ensuring security must be increased.

Demonstration of the Feasibility of the Korean eLoran System as a Resilient PNT in a Testbed

With the increasing utilization of location information, attempts to improve the safety of absolute positioning coordinates, which have depended on global navigation satellite systems (GNSSs), such as the Global Positioning System (GPS), are underway. Among these, enhanced long range navigation (eLoran) is the most technically developed system. In Korea, related technologies have been developed since 2016, and a testbed for eLoran performance evaluation, which is currently in operation as a pilot service, was completed in 2021. We analyze the position accuracy of the eLoran pilot service to use it as an alternative when GNSS usage is challenging within Korea’s eLoran testbed. We evaluated the accuracy of the absolute position using the eLoran system by sailing up to 160 km away from the Incheon testbed transmitter according to four navigation stages (inland waterway, port approach, coastal, and ocean) classified by the International Maritime Organization (IMO). To validate the eLoran positioning performance in which an additional secondary factor (ASF) map is not provided, we propose a differential GPS (DGPS) position-based ASF estimation technique. Based on this study, Korea’s eLoran system can calculate the absolute position with an accuracy of approximately 15 m with 95% probability at the port-approach stage.

Evaluating the effectiveness and robustness of machine learning models with varied geo-environmental factors for determining vulnerability to water flow-induced gully erosion

Assessing and mapping the vulnerability of gully erosion in mountainous and semi-arid areas is a crucial field of research due to the significant environmental degradation observed in such regions. In order to tackle this problem, the present study aims to evaluate the effectiveness of three commonly used machine learning models: Random Forest, Support Vector Machine, and Logistic Regression. Several geographic and environmental factors including topographic, geomorphological, environmental, and hydrologic factors that can contribute to gully erosion were considered as predictor variables of gully erosion susceptibility. Based on an existing differential GPS survey inventory of gully erosion, a total of 191 eroded gullies were spatially randomly split in a 70:30 ratio for use in model calibration and validation, respectively. The models’ performance was assessed by calculating the area under the ROC curve (AUC). The findings indicate that the RF model exhibited the highest performance (AUC = 89%), followed by the SVM (AUC = 87%) and LR (AUC = 87%) models. Furthermore, the results highlight those factors such as NDVI, lithology, drainage, and density were the most influential, as determined by the RF, SVM, and LR methods. This study provides a valuable tool for enhancing the mapping of soil erosion and identifying the most important influencing factors that primarily cause soil deterioration in mountainous and semi-arid regions.

Water depth estimation from Sentinel-2 imagery using advanced machine learning methods and explainable artificial intelligence

The estimation of water depth in coastal areas and shallow waters is crucial for marine management and monitoring. However, direct measurements using fieldwork methods can be costly and time-consuming. Therefore, remote sensing imagery is a promising source of geospatial information for coastal planning and development. To this end, this study investigates advanced machine learning (ML) methods and redesigned morphological profiles for water depth estimation using high-resolution Sentinel-2 satellite imagery. The proposed framework involves three main steps: (1) morphological feature generation, (2) model training using several ML methods (Decision Tree, Random Forest, eXtreme Gradient BOOSTing, Light Gradient Boosting Machine, Deep Neural Network, and CatBoost), and (3) model interpretation using eXplainable Artificial Intelligence (XAI). The performance of the proposed method was evaluated in two different coastal areas (port and jetty) with reference data from accurate hydrographic data (Echo-sounder and differential global positioning systems). The statistical analysis revealed that the proposed method had high efficiency for depth estimation of the coastal area, achieving a best R2 value of 0.96 and Root Mean Square Error (RMSE) of 0.27 m in water depth estimation in the shallow water of Chabahar Bay in the Oman Sea. Additionally, the higher impact and interaction of the morphological features were verified using XAI for water depth mapping.

Selecting and refining suitable methods of developing digital elevation models to represent geomorphic features and characteristics of smaller wetlands in data-scarce environments

Land surface geomorphology plays an important role in water and sediment dispersal processes in wetlands. For wetland practitioners and researchers to engage with these processes in time and space, they require topographic data in order to derive wetland surface gradient, cross-sectional shape and area, surface and subsurface hydrological connectivity, and hydraulic characteristics. A range of data options, with varying spatial resolutions, are available, ranging from free national and global resources (e.g. contour data and global elevation models) to project-specific high-resolution surveys (e.g. Differential Global Positioning Systems (DGPS), Photogrammetry, Light Detection And Ranging (LiDAR)). Due to the scarcity of high-resolution and high-accuracy data, especially in developing countries, data gathering and processing costs can be significant. This paper presents a commentary on a range of topographic data and processing options for a relatively small (~ 40 ha) floodplain wetland in the Eastern Cape, South Africa. It critically reviews the usefulness and shortfalls of various wetland-related applications ranging from gradient calculations to more detailed hydraulic modelling, and the data resolution required for each application. Free, low-resolution, datasets have a limited representation of geomorphology at this scale due to the relatively low-resolution and large vertical error. Field-based surveys (using survey-grade equipment such as a DGPS) have the benefit of providing accurate terrain results in areas with dense vegetation and surface water, while photogrammetry and LiDAR data are useful to represent the higher resolution morphology across the wetland, despite shortcomings regarding the penetration of dense vegetation and surface water. However, combining DGPS data with LiDAR proves to yield the best model for detailed process modelling for wetlands at the local scale.

Holocene Erosional Processes in a Highly Exposed Intertidal Sandstone Reef Inferred from Remote Sensing Data

An intertidal sandstone reef, named barra de Las Canteras, protects the western coast of Las Palmas de Gran Canaria city (Canary Islands, Spain). The beach-reef system of Las Canteras constitutes one of the most valuable coastal geomorphological sites in the archipelago. Stratigraphic studies have identified the formation of the reef in the Last Interglacial (MIS 5e) in a coastal sedimentary paleo-environment. The rock structure is highly exposed to the Atlantic swell and consists mainly of a sandstone beachrock with a medium resistance to erosional processes. However, the historical and current erosion rates and the original extent of the reef are not known to date. This paper explores the geomorphological structure of the reef by combining a topo-bathymetric analysis (obtained by differential GPS, multibeam echosounder and hyperspectral sensor) and the analysis of geomorphological features on high-resolution images, obtained with a hyperspectral camera mounted on a UAV. The results provide a comprehensive, high-resolution image of the subaerial and submerged morphology of the reef. The structure reflects the distribution of erosional fronts and the existence of collapsing submarine blockfields and nearshore, uneroded, remnant reliefs. Detailed analysis of these features allows to estimate the probable original extent of the sandstone reef and to relate the erosional retreat processes to the sea-level dynamics during the Holocene.

Geomorphic and environmental controls on microbial mat fabrics on Little Ambergris Cay, Turks and Caicos Islands

AbstractTo interpret microbially influenced paleoenvironments in the sedimentary record, it is crucial to understand what processes control the development of microbial mats in modern environments. This article reports results from a multiyear study of Little Ambergris Cay, Turks and Caicos Islands, an uninhabited island floored by broad tracts of well‐developed microbial mats on the wind‐dominated and wave‐dominated Caicos Platform. Uncrewed aerial vehicle‐based imaging, differential global positioning system topographic surveys, radiocarbon data, and in situ sedimentological and microbial ecological observations were integrated to identify and quantify the environmental factors that influence the distribution and morphologies of Little Ambergris Cay microbial mats, including their response to large storm events. Based on these data, this study proposes that Little Ambergris Cay initially developed from the accretion and rapid lithification of carbonate sediment delivered by converging wave fronts in the lee of adjacent Big Ambergris Cay. Broad tracts of microbial mats developed during late Holocene time as the interior became restricted due to beach ridge development. Minor elevation differences regulate subaerial exposure time and lead to three categories of microbial mats, differentiated by surface texture and morphology: smooth mats, polygonal mats and blister mats. The surface texture and morphology of the mats is controlled by subaerial exposure time. In addition to elevation, the spatial distribution of mats is largely controlled by hydrodynamics and sediment transport during large storm events. This detailed assessment of the controls on mat formation and preservation at Little Ambergris Cay provides a framework within which to identify and understand the interactions between microbial communities and sediment transport processes in ancient high‐energy carbonate depositional systems.

Unmanned Aerial Vehicle-Based Structure from Motion Technique for Precise Snow Depth Retrieval—Implication for Optimal Ground Control Point Deployment Strategy

Unmanned aerial vehicle (UAV)-based snow depth is mapped as the difference between snow-on and snow-off digital surface models (DSMs), which are derived using the structure from motion (SfM) technique with ground control points (GCPs). In this study, we evaluated the impacts of the quality and deployment of GCPs on the accuracy of snow depth estimates. For 15 GCPs in our study area, we surveyed each of their coordinates using an ordinary global positioning system (GPS) and a differential GPS, producing two sets of GCP measurements (hereinafter, the low-accuracy and high-accuracy sets). The two sets of GCP measurements were then incorporated into SfM processing of UAV images by following two deployment strategies to create snow-off and snow-on DSMs and then to retrieve snow depth. In Strategy A, the same GCP measurements in each set were used to create both the snow-on and snow-off DSMs. In Strategy B, each set of GCP measurements was divided into two sub-groups, one sub-group for creating snow-on DSMs and the other sub-group for snow-off DSMs. The accuracy of snow depth estimates was evaluated in comparison to concurrent in-situ snow depth measurements. The results showed that Strategy A, using both the low-accuracy and high-accuracy sets, generated accurate snow depth estimates, while in Strategy B, only the high-accuracy set could generate reliable snow depth estimates. The results demonstrated that the deployment of GCPs had a significant influence on UAV-based SfM snow depth retrieval. When accurate GCP measurements cannot be guaranteed (e.g., in mountainous regions), Strategy A is the optimal option for producing reliable snow depth estimates. When highly accurate GCP measurements are available (e.g., collected by differential GPS in open space), both deployment strategies can produce accurate snow depth estimates.

The HAInich: A multidisciplinary vision data-set for a better understanding of the forest ecosystem

We present a multidisciplinary forest ecosystem 3D perception dataset. The dataset was collected in the Hainich-Dün region in central Germany, which includes two dedicated areas, which are part of the Biodiversity Exploratories - a long term research platform for comparative and experimental biodiversity and ecosystem research. The dataset combines several disciplines, including computer science and robotics, biology, bio-geochemistry, and forestry science. We present results for common 3D perception tasks, including classification, depth estimation, localization, and path planning. We combine the full suite of modern perception sensors, including high-resolution fisheye cameras, 3D dense LiDAR, differential GPS, and an inertial measurement unit, with ecological metadata of the area, including stand age, diameter, exact 3D position, and species. The dataset consists of three hand held measurement series taken from sensors mounted on a UAV during each of three seasons: winter, spring, and early summer. This enables new research opportunities and paves the way for testing forest environment 3D perception tasks and mission set automation for robotics.

Study of dynamics in surface ice flow rate of glaciers in Hunza basin, Karakoram.

Glaciers in the Karakoram region are widely recognized for their historical surging phenomenon. Accurate field-based glacier monitoring is challenging in the Karakoram due to the presence of mixed-nature glaciers that are advancing, receding, and surging. Many geographers came to the opinion that surging is a thermally controlled activity in the Karakoram as opposed to a hydrologically controlled activity as a result of characteristics including high-altitude warmth, precipitation, and accumulation patterns of these glaciers. But the main surge mechanism is still a mystery. The current study used Landsat multispectral satellite datasets to examine and investigate the glaciers' vulnerability to surging activity in the Hunza basin based on the annual surface ice flow rate and frontal snout advancement of the glaciers from 1990 to 2021. Around 80 glaciers in the Hunza basin have been researched, and based on interannual surface flow rates, it has been determined that Batura, Hassanabad II, Barpu, Gharesa, Hispar, Khurdopin, Minapin, Virjerab, Yazgil, and Ghulkin glaciers are more vulnerable to surging. The findings show that during the research period, these glaciers had surged and advanced along their snouts. The frontal snout of these glaciers advances, and moraines are deposited closer to the glacier terminus as a consequence of active surge points over the ablation region. The Hunza basin's topography, precipitation, and thermal regimes regulate the glaciers' surging phenomena causing successive acceleration in the glaciers. Field-based measurements made with a differential global positioning system are used to corroborate the obtained results.

A new mooring failure detection approach based on hybrid LSTM-SVM model for semi-submersible platform

A reliable and stable mooring system is of vital significance to warrant the continuous operation and personnel heath on offshore floating platforms. The failure detection technique for the mooring line can provide an early warning and response for people so that action can be taken earlier to avoid more severe consequences. This paper proposed a novel approach based on Long short-term memory neural networks and support vector machine (LSTM-SVM) to detect the mooring line failure. Two combined failure signals can judge the actual mooring failure occurrence. The preliminary mooring failure signals can be identified based on the significant increase of errors in the LSTM prediction of three platform motions. Besides, a SVM classifier is constructed based on three parallel sea state lines to distinguish the impact of motions between sea state change and mooring failure and capture the secondary failure signal. Five time-domain features are extracted from heave motion, and well-trained SVM model with 132 group cases data from three sea state lines can achieve characteristic recognition of sea state change and mooring failure. According to four new cases with continuously changing sea states, the proposed method indicates the feasibility of mooring failure detection when platforms suffer the influence of rapid environmental changes under a broader range of sea states, and the LSTM-SVM can still distinguish the sea state change and actual mooring failure. In this study, motion data acquisition relies on the hydrodynamic simulation of a semi-submersible platform (SEMI). For practical application, the proposed method can utilize the mature Differential Global Positioning System (DGPS) to obtain the data and realize the mooring failure detection in real time.

Investigation into high-performance tire characteristics at the limits of handling

This paper presents an investigation into high-performance tire characteristics at handling limits. Analysis of the tire characteristics has been conducted using the data obtained from indoor test rigs and vehicle tests. The test rig utilised a flat track test platform to obtain tire forces with precise controllability and measurability. As for the vehicle tests, driving data of a professional driver was obtained for repeatability through a high-performance test vehicle equipped with a wheel force transducer (WFT) at each wheel and a Differential Global Positioning System (DGPS). This study is primarily focused on obtaining a deeper insight into vehicle control through an analysis of tire force characteristics at handling limits. The analysis of indoor test data utilised the Magic Formula to obtain an appropriate model capable of describing real-world tire characteristics. An optimisation-based friction ellipse has been proposed to describe the tire force limits in particular. The solution of the proposed optimisation problem allows for an envelope to be generated that accurately reflects the tire force limits. Additionally, a normalised friction circle model has been proposed to quantify tire friction usage based on the proposed friction ellipse, providing a methodology to monitor the friction use of each wheel.

An Improved Intersection Method for Positioning and Its Application to Dalk Glacier, East Antarctica

Stake measurement is a significant ground-observation method used to monitor glacier dynamics in the cryosphere and validate the reliability of remotely sensed investigations. However, some stake observation records obtained by total stations have not been efficiently utilized via the classic forward intersection method. The forward intersection method requires a pair of cognominal observation records and cannot function if stakes are only observed from one control point at a stake measurement, which commonly occurs in measurements. Therefore, we propose an improved intersection method that combines stake trajectories and sightlines, the trajectory-sightline intersection method, which requires only a single observation record. In the processing of stake identification, location and elevation from the trajectory-sightline intersection method are constrained by the velocity and surface altitude of the glacier. Then, the trajectory-sightline intersection method is applied to the stake measurement of Dalk Glacier in East Antarctica. The results show that the trajectory-sightline intersection method extracted an additional 44 intersections, thus increasing the data utilization ratio by 20.35%. Moreover, leave-p-out cross-validation indicated that the trajectory-sightline intersection method, with an average offset of 0.88±0.54 m in the horizontal position and 0.26±0.12 m at the elevation, has similar accuracy to the differential global positioning system (DGPS) applied to Livingston Island. The ice flow velocities of Dalk Glacier calculated from trajectory-sightline intersections agree with those from forward intersections (the mean velocity difference is 0.47 m·year−1). Therefore, the trajectory-sightline intersection method is an effective method for improving data utilization and obtaining stake locations and glacier velocities.

Up-glacier propagation of surface lowering of Yala Glacier, Langtang Valley, Nepal Himalaya

AbstractWe quantify the surface elevation changes along Yala Glacier in Langtang Valley, Nepal Himalaya, since 1981 using geodetic methods to understand the recent evolution and current state of small debris-free glaciers across the region. We analyse differential global positioning system measurements and aerial stereo imagery that were acquired along Yala Glacier in 2007, 2009, 2012 and 2015 to generate digital elevation models for each calculation period. Continuous surface lowering has mainly been observed across the down-glacier area during the calculation periods, although a large degree of variability exists, with this lowering trend propagating up-glacier in recent years. The area-weighted glacier mass balances range from −0.98 ± 0.27 to −0.26 ± 0.30 m w.e. a−1 for the five calculation periods (1981–2007, 2007–2009, 2009–2012, 2012–2015 and 2007–2015). These calculated mass-balance data reveal that Yala Glacier has undergone accelerated mass loss since the late 2000s, which is consistent with the results of previous in situ measurement and remote-sensing studies.

Maritime DGPS System Positioning Accuracy as a Function of the HDOP in the Context of Hydrographic Survey Performance

The Differential Global Positioning System (DGPS) is a marine navigation system operating at frequencies of 283.5–325 kHz, which is now the primary method for locating vessels in coastal shipping, as well as hydrography and mapping systems worldwide. Its positioning accuracy is determined by the following: the pseudorange error to Global Positioning System (GPS) satellites, the age of pseudorange corrections, and the value of the Horizontal Dilution of Precision (HDOP), which, in terms of accuracy, is crucial in positioning using GPS satellites. In 2020, the International Hydrographic Organization (IHO) introduced a new (the highest) order of hydrographic surveys, i.e., the Exclusive Order, which requires a positioning system to provide an accuracy of 1 m (p = 0.95). The aim of this article is to provide an answer to the question as to whether the maritime DGPS system, whose positioning accuracy is constantly increasing with that of the GPS system, fulfils the requirements for the hydrographic surveys of harbours. To this end, an extensive experimental study on the maritime DGPS system, involving a total of nearly 3.5 million fixes, was conducted. Statistical analyses showed that when ensuring the HDOP values range from 0.8 to 1.4, the DGPS system can be used in hydrographic surveys of harbours.

Removal of Intra-Array Statics in Seismic Arrays Due to Variable Topography and Positioning Errors

A receiver array is an arrangement of geophones used to enhance the signal-to-noise ratio (S/N) of seismic data. However, deviations from ideal array conditions can lead to the non-optimal performance of the array. This study investigates, quantitatively, the array performance in the presence of topographic variations and positioning errors using 2D seismic data acquired in eastern Saudi Arabia. A receiver array was laid over a sand dune with variable topography underlain by a flat sabkha that has a very shallow water table. The topographic variations and position errors were calculated from Differential Global Positioning System (D-GPS) measurements of source and receiver positions and elevations. The errors in receiver positions, measured relative to the ideal receiver spacing, gave a mean and standard deviation of about 0.35% and 1%, respectively. On the other hand, elevation errors (topographic variations) from a horizontal datum gave a mean and standard deviation of about 25% and 13%, respectively. The ideal array response was found by removing both elevation and position errors. The first-arrival energy of the array was calculated after removal of elevation and position errors separately and compared to the ideal-array energy. Comparison showed a 64% enhancement in the first-arrival energy after correcting for elevation errors alone and almost no enhancement after correcting for position errors alone. The proposed approach can be used to calculate accurate static corrections for seismic reflection processing and to generate high-resolution subsurface images for engineering applications.