Error Propagation Research Articles

Exploring the power of data-driven models for groundwater system conceptualization: a case study of the Grazer Feld Aquifer, Austria

AbstractDeveloping a reliable conceptual model is crucial for analyzing groundwater systems. An essential part of the aquifer conceptualization is the identification of the hydrological stresses that control the hydraulic head fluctuations. By effectively capturing and understanding these stresses, the propagation of potential errors and uncertainties through subsequent modeling steps can be minimized. This study aims to test data-driven models as screening models for conceptualizing a groundwater system. The case study is applied to the Grazer Feld Aquifer in southeast Austria. Time series models are applied to: (1) identify the stresses likely influencing the observed head fluctuations and their spatial variability; (2) identify locations where a lack of understanding of head fluctuations exists; and (3) discuss the limitations and opportunities associated with data-driven models to support system conceptualization. Time series models were created for 144 monitoring wells where sufficient head observations were available during the calibration period (2005–2015). A total of 576 models were developed, incorporating the combinations of stresses: recharge, river level, and a step trend. Following the model selection process, each model was categorized based on its performance and divided into four groups. At 88 sites, recharge and river level variations were identified as the primary controlling stresses influencing head fluctuations. The inclusion of the step trend was found to be necessary at five sites to accurately simulate heads due to dam construction. The application of data-driven models in this study enhanced the identification of key aquifer stresses, facilitating a more informed understanding of the groundwater system.

Small object change detection in UAV imagery via a Siamese network enhanced with temporal mutual attention and contextual features: A case study concerning solar water heaters

Small object change detection (SOCD) based on high-spatial resolution (HSR) images is of significant practical value in applications such as the investigation of illegal urban construction, but little research is currently available. This study proposes an SOCD model called TMACNet based on a multitask network architecture. The model modifies the YOLOv8 network into a Siamese network and adds structures, including a feature difference branch (FDB), temporal mutual attention layer (TMAL) and contextual attention module (CAM), to merge differential and contextual features from different phases for the accurate extraction and analysis of small objects and their changes. To verify the proposed method, an SOCD dataset called YZDS is created based on unmanned aerial vehicle (UAV) images of small-scale solar water heaters on rooftops. The experimental results show that TMACNet exhibits strong resistance to image registration errors and building height displacement and prevents error propagation from object detection to change detection originating from overlay-based change detection. TMACNet also provides an enhanced approach to small object detection from the perspective of multitemporal information fusion. In the change detection task, TMACNet exhibits notable F1 improvements exceeding 5.96% in comparison with alternative change detection methods. In the object detection task, TMACNet outperforms the single-temporal object detection models, increasing accuracy with an approximately 1–3% improvement in the AP metric while simplifying the technical process.

Measurement and optimization method for aero-engine rotors based on binocular multi-line laser sensing and virtual assembly

During the assembly of aero-engine rotors, eccentricity and tilt errors along the radial and axial direction result in uneven loading along the mating surface, which significantly impacts the operational lifespan of the rotor. This paper proposes a three-dimensional contour measurement system based on binocular multi-line laser sensing and conducts an aircraft engine rotor stacking virtual assembly technology system, to response the practical requirements of aircraft engine rotor unit production assembly engineering. We combine multi-line structured light active projection device with the binocular stereo vision model to construct the contour measurement system, which enhances the surface features of the projected object by adhering to the common constraint of structured line and stereo vision. Furthermore, the error propagation model for multi-stage rotor assembly to investigate the mechanism of error impact is established aiming to facilitate quantitative research on virtual assembly mechanisms. By manipulating the rotor phases and evaluating the coaxiality under various conditions, effective prediction of phase variation can be achieved, the multi-objective benchmark virtual assembly method based on the error propagation model of multi-stage rotor coaxiality is presented. The experimental results indicate that the proposed measurement system achieves the maximum disparity between the spatial data points procured by the proposed measurement system and the predetermined value of 2.1 μm, and the maximum disparity between the target plane’s average movement distance and the preset value of 3.7 μm. The precision provided by this system effectively meets the objective of assembly phase optimization for multi-stage rotors. In the empirical experiment conducted on the three-stage rotors, both systems yielded identical optimized phases: 0° for the first-stage rotor, 270° for the second-stage rotor, and 120° for the third-stage rotor, thereby validating the efficacy of the proposed measurement and optimization method for aero-engine multi-stage rotors with virtual assembly.

Efficient and low-drift point-cloud mapping method for GNSS weak and obstructed areas

Abstract Road modelling based on point-cloud data often encounters signal-rejection areas, where the absence of absolute position constraints leads to rapid error accumulation in the constructed map. To mitigate error growth, the manual setting of control points is necessary, which compromises automation in the mapping process and causes a challenging trade-off between efficiency and accuracy. To address these issues, in the present study, we designed a fast and efficient map-construction method that provides absolute pose constraints for simultaneous localisation and mapping (SLAM) based on the bidirectional filter position of global navigation satellite system (GNSS)/inertial measurement unit (IMU) integrated navigation. First, we designed an absolute position constraint factor (BF_Factor) updated at each epoch according to the reference-position difference and time-delay error, which can significantly mitigate error divergence during mobile mapping in a GNSS-rejected region. An adaptive noise model was then constructed for the above factors so that the weights of different factors could be adjusted automatically to achieve a more accurate mapping result. Finally, we conducted experimental tests on the proposed mapping approach in two scenarios that included multiple GNSS-rejected areas. Even when several hundred metres of GNSS signal-obstructed areas were present in the scene, the relocation results indicated that the three-dimensional position error obtained using point-cloud maps was <0.15 m and the pitch/roll error was better than 2°. Compared with Fastlio2 and Fastlio2-SC (Fastlio2 with Scan Context), the proposed method had a better mapping effect and higher mapping accuracy in the aforementioned scenarios. The method proposed in this paper can reduce the human cost in traditional modeling and can provide more rapid and accurate map for related industry applications. The method proposed in this paper can reduce the human cost in traditional modeling and can provide more rapid and accurate map for related industry applications.

High accuracy patient-specific 3D coronary reconstruction from angiography and intravascular optical coherence tomography.

The integration of digital subtraction angiography (DSA) with intravascular optical coherence tomography (IVOCT) offers a comprehensive 3D arterial model, which is invaluable for the analysis of vascular anatomy and biomechanics. However, the process of image fusion is often hindered by the challenge of accurately orienting IVOCT images. This paper introduces a novel, to our knowledge, dual-path 3D reconstruction method that leverages the guidewire and the vessel's centerline to establish cross sectional direction vectors within the IVOCT images and spatial direction vectors along the guidewire's trajectory. This approach minimizes the accumulation of reconstruction errors by ensuring the precise orientation of each vascular cross section. The efficacy of the proposed method is validated through vascular phantom experiments and the reconstruction of patient-specific 3D coronary models.

Entity and relation extractions for threat intelligence knowledge graphs

Advanced persistent threats (APTs) represent a complex challenge in cybersecurity as they infiltrate networks stealthily to conduct espionage, steal data, and maintain a long-term presence. To combat these threats, security professionals increasingly rely on cyber knowledge graphs (CKGs), which provide scalable solutions to analyze and structure vast amounts of cyber threat intelligence (CTI) from diverse sources in real-time, enabling the automation of proactive security measures. Developing CKGs requires extracting entity and their relationships from unstructured CTI reports. However, existing approaches face significant limitations, such as difficulties with the nuances of cybersecurity language, diverse threat terminologies, and high rates of error propagation, resulting in low accuracy and poor generalizability. This paper introduces a novel Threat Intelligence Knowledge Graph (TiKG) pipeline designed to address these challenges. The TiKG framework leverages SecureBERT, a domain-specific transformer-based model optimized for cybersecurity, and integrates it with an attention-based BiLSTM to capture the context and nuances of security texts, reducing error propagation and improving extraction accuracy. Additionally, the pipeline incorporates a domain-specific ontology and inference model to ensure precise relation mapping in relation extraction. Using three large-scale TI open-source datasets (DNRTI, STUCCO, and CYNER) and a curated CTI dataset, extensive evaluations demonstrate the effectiveness of our framework, showing significant improvements over existing methods in detecting and linking cyber threats. These contributions provide a robust platform for security professionals to analyze and predict potential attacks, develop effective defenses, and enhance the strategic capabilities of cybersecurity operations.

An integrated positioning method with IMU/UWB based on geometric constraints of foot-to-foot distances

Zero velocity update aided inertial navigation system (INS) and ultra-wideband (UWB) technology are widely used in pedestrian positioning. However, INS has significant cumulative error and the application environment of UWB positioning system is limited. In this study, an integrated positioning method with IMU/UWB based on geometric constraints of foot-to-foot distances is proposed. The gait of pedestrian is divided into three phases: stance, swing, and stride. Then, stance phase correction model, swing phase ranging model and stride phase size model are established, respectively. The magnetic induction intensity, accelerations, velocities, and the foot-to-foot distances measured by UWB are used to construct the constraints. Extended Kalman filter (EKF) and error state Kalman filter (ESKF) are utilized to estimate the attitudes and positions optimally. Several experiments in various environments are carried out to verify the proposed method. The results show that the proposed method has high accuracy and good robustness to the environments.

Research on novel hybrid structure AACMM based on 3-RPS parallel mechanism

Abstract The articulated arm coordinate measuring machine (AACMM), widely utilized in the field of industrial measurement, is assessed based on key performance indicators such as measurement accuracy, measurement space, and flexibility. To enhance these aspects of the AACMM, this paper introduces a novel type of AACMM. The new model is based on a 3-RPS (revolute joint R, prismatic joint P, spherical joint S) parallel mechanism (3-RPSPM). The working principle of the 3-RPSPM is detailed, and a corresponding computational model is established. An algorithm is proposed based on this mathematical model to determine the theoretical achievable accuracy of the 3-RPSPM. Additionally, a spatial error analysis of the 3-RPSPM is conducted to verify its compliance with AACMM accuracy requirements and to demonstrate a certain level of accuracy improvement. Through simulation analysis, the differences in measurement accuracy, measurement space, and flexibility between the new type of AACMM based on the 3-RPS parallel mechanism (3-RPSAACMM) and traditional AACMMs are compared. The results indicate that the 3-RPSAACMM, with its novel parallel structure free from series accumulation errors, not only maintains measurement flexibility but also achieves improvements in measurement accuracy and space. The hybrid structure adopted by the new 3-RPSAACMM offers a new direction for the future development of AACMMs, presenting significant research value and application prospects.

Development and verification of real-time hybrid model test delay compensation method for monopile-type offshore wind turbines

The real-time hybrid model (RTHM) test is adept at addressing the scale contradiction, the lack of fidelity in wind modelling in hydrodynamic testing facilities and spatial constraints inherent in conventional monopile-type offshore wind turbine (OWT) model testing methods, thus emerging as an effective avenue for conducting physical model tests of Monopile-type OWTs. This method entails the reproduction of aerodynamic loads or platform motions using loading device or vibration tables. Time delays in the physical attributes of the loading device and signal transmission processes within the system can result in error accumulation, with the potential to impact overall system stability. Moreover, time delay compensation algorithms for hybrid model test systems with force control loading can easily generate excessive noise, leading to system divergence. As a result, time delay has emerged as a technical challenge in the RTHM test. To address this issue, this paper has developed second-order and third-order polynomial extrapolation algorithms, alongside an adaptive compensation algorithm. The adaptive compensation algorithm employs the least squares method to identify parameters of the loading system, enabling it to address variations in the time delay of the experimental system caused by the nonlinearity of the loading system and changes in the physical properties of the model. The feasibility and effects of time delay compensation for various algorithms are validated through numerical simulation. Results indicate that the adaptive compensation algorithm surpasses second and third-order polynomial extrapolation compensation algorithms in terms of accuracy and compensation effectiveness. To validate the applicability of the adaptive compensation algorithm, a RTHM test was conducted. Across rotor thrust force (RotThrust) and tower top displacement, there was an average reduction of approximately 5 % and 9 % in the maximum and minimum synchronization errors, respectively. This highlights the efficacy of the delay compensation algorithm in practical applications, notably diminishing time delay errors within the experimental system. The adaptive compensation algorithm continuously adjusts and updates parameters, enhancing the adaptability of the compensation process to time-varying systems.

OGSS: An Ontology-Guided and Scheduled-Sampling Approach for Overlapping Event Extraction

Event extraction is a complex and challenging task in the field of information extraction. It aims to identify event types, triggers, and argument information from the text. In recent years, overlapping event extraction has attracted the attention of researchers because of its higher challenge and practicability, and some work has carried out in-depth research on overlapping event extraction and achieved remarkable results. But these works (1) ignore the role of ontology knowledge in event extraction; (2) use the same semantic encoding for multi-stage models, lacking consideration for the independent characteristics of extraction tasks such as event types, triggers, and arguments; and (3) face issues in the training process of multi-stage models, such as error cascading and slow convergence. To address the above issues, we propose an ontology-guided and scheduled-sampling approach for overlapping event extraction, termed as OGSS. First, we design a symmetric matrix for event ontology knowledge representation and integrate it into the semantic encoding process, infusing ontology knowledge into event extraction. Second, for extraction targets such as event types, triggers, and arguments, we process the semantic encoding according to the characteristics of each extraction target, obtaining semantic representations tailored for each subtask. Finally, we view multi-stage predictions as sequential outputs of a joint model, using a scheduled sampling strategy between subtasks to effectively mitigate the cascading propagation of errors during training and accelerate model convergence. We conduct extensive experiments on the FewFc event extraction benchmark dataset. The results show that OGSS achieves significant improvements in overlapping event extraction tasks compared to previous methods.

Surface pressure reconstruction from LPT data with boundary conforming grids

Abstract The coupling between fluid-structure interactions is governed by the pressure distribution over the interaction surface between the fluid and solid domains. The capabilities of non-intrusive optical techniques, such as particle image velocimetry and Lagrangian particle tracking (LPT), have been proven to provide accurate velocity and acceleration information within the flow field while simultaneously tracking the corresponding structural deformations. However, scattered data from LPT measurements are typically mapped onto Cartesian grids, independently of the shape of the solid objects in the measurement domain. The use of Cartesian grids poses challenges for the determination of the surface pressure because the velocity gradients close to the object’s surface are not captured accurately. Therefore, an alternative surface pressure reconstruction scheme utilizing LPT data based on the arbitrary Lagrangian–Eulerian approach is proposed to mitigate the error propagation associated with the use of uniform grids. The introduced method provides an exact surface conformation utilizing boundary fitted coordinate systems and radial basis function based mesh deformations, which eliminates the need to use extrapolations to obtain surface pressure distributions. The introduced approach is assessed by means of a synthetic hill surface probing a three-dimensional analytical flow field; its practical applicability is demonstrated through an experimental characterization of turbulent boundary layer interactions with a steadily and unsteadily deforming elastic membrane.

D-BJH: The Intrinsic Model for Characterizing the Pore Size Distribution of Porous Materials.

A novel approach, the differential Barrett-Joyner-Halenda model (D-BJH), is proposed to address the limitations of the traditional BJH model in determining the pore size distribution (PSD). This method integrates multilayer adsorption and capillary condensation processes using advanced numerical techniques, notably, a Galerkin-based framework for solving the differential BJH equations. The D-BJH model offers a precise analytical framework, establishing itself as a benchmark in adsorption theory. It enables accurate PSD calculations and provides robust theoretical support for interpreting adsorption isotherms. Additionally, a simplified D-BJH method allows for direct point-by-point PSD calculation, reducing computational complexity and eliminating cumulative errors inherent in traditional methods like BJH, DH, CI, and VBS. The D-BJH model demonstrates that PSD is directly related to the differential function of the isotherm and pore size concerning relative pressure. Comparative analyses with BJH, DH, and NLDFT models show that D-BJH effectively explains fundamental adsorption mechanisms, such as the "spurious peak" observed in desorption branches. This work expands the applicability of the BJH model, proving that both the D-BJH model and its simplified method are accurate and comprehensive for characterizing the pore structures of microporous and mesoporous materials.

A dual-stage coverage path planning method for bathymetric survey using an AUV in graph-based SLAM framework considering positioning uncertainty

Autonomous underwater vehicles (AUVs) are becoming increasingly popular for seabed coverage survey tasks but suffer from a serious problem of positioning error accumulation, which leads to low path tracking accuracy and a low coverage rate of the target area. A dual-stage AUV path planning (DSPP) approach was proposed within a simultaneous localization and mapping (SLAM) framework for coverage bathymetric surveys. In the first stage, a global coverage path is designed in a lawnmower style. Crossed paths are added to generate periodic loop closures in SLAM, which can improve the efficiency of online replanning. In the second stage, with a probabilistic neural network (PNN) used for feature-rich bathymetric sub-map identification, an entropy-based online path replanning approach was implemented using 20 templates to adjust the global path to produce accurate loop closures for positioning error correction. Two bathymetric datasets were used to test the proposed DSPP method using a self-developed experimental simulation system. Experiments verified that DSPP can significantly improve the positioning accuracy and coverage rate with acceptably longer paths. In a 6000 m× 5000 marea,the DSPP approach reduced the positioning error by 76% compared with the pure dead reckoning method, and the coverage ratio reached 98.15%.

Comparing frequentist and Bayesian uncertainty quantification in two‐step constitutive model calibration

AbstractThe calibration of constitutive models using experimental observations is a well‐established task in computational solid mechanics. Especially for complex constitutive models, multistep calibrations are regularly applied. While this approach is advantageous for reducing the complexity of the calibration problem, the quantification of parameter uncertainties becomes more challenging. In this study, we perform two‐step statistical parameter inference for an elasto‐plastic constitutive model utilizing a frequentist approach and Bayesian inference. The uncertainties are quantified by different methods, in particular, we compare an asymptotic normality analysis and Gaussian error propagation in the frequentist setting with sampling‐based Bayesian inference. Although frequentist and Bayesian parameter inference differ significantly in the way parameters are calibrated, both methods yield similar estimates. Additionally, the uncertainties quantified by asymptotic normality considerations and Gaussian error propagation are quite similar. However, the uncertainties of the elasticity parameters in the frequentist approach are found to be significantly smaller compared to Bayesian inference, due to the underlying approximations in the frequentist setting. Thus, uncertainties can be estimated in both settings, but it is crucial to be aware of the inherent assumptions and simplifications when interpreting the results.

Data-Driven Strategies for Carbimazole Titration: Exploring Machine Learning Solutions in Hyperthyroidism Control.

University Hospitals Dorset (UHD) has over 1,000 thyroid patient contacts annually. These are primarily patients with autoimmune hyperthyroidism treated with Carbimazole titration. Dose adjustments are made by a healthcare professional (HCP) based on the results of thyroid function tests, who then prescribes a dose and communicates this to the patient via letter. This is time-consuming and introduces treatment delays. This study aimed to replace some time-intensive manual dose adjustments with a machine learning model to determine Carbimazole dosing. This can in the future serve patients with rapid and safe dose determination and ease the pressures on HCPs. Data from 421 hyperthyroidism patients at UHD were extracted and anonymised. A total of 353 patients (83.85%) were included in the study. Different machine-learning classification algorithms were tested under several data processing regimes. Using an iterative approach, consisting of an initial model selection followed by a feature selection method the performance was improved. Models were evaluated using weighted F1 scores and Brier scores to select the best model with the highest confidence. The best performance is achieved using a random forest (RF) approach, resulting in good average F1 scores of 0.731. A model was selected based on a balanced assessment considering the accuracy of the prediction (F1 = 0.751) and the confidence of the model (Brier score = 0.38). To simulate a use-case, the accumulation of the prediction error over time was assessed. It was determined that an improvement in accuracy is expected if this model was to be deployed in practice.

The Zero-Velocity Correction Method for Pipe Jacking Automatic Guidance System Based on Fiber Optic Gyroscope.

The pipe jacking guidance system based on a fiber optic gyroscope (FOG) has gained extensive attention due to its high degree of safety and autonomy. However, all inertial guidance systems have accumulative errors over time. The zero-velocity update (ZUPT) algorithm is an effective error compensation method, but accurately distinguishing between moving and stationary states in slow pipe jacking operations is a major challenge. To address this challenge, a "MV + ARE + SHOE" three-conditional zero-velocity detection (TCZVD) algorithm for the fiber optic gyroscope inertial navigation system (FOG-INS) is designed. Firstly, a Kalman filter model based on ZUPT is established. Secondly, the TCZVD algorithm, which combines the moving variance of acceleration (MV), angular rate energy (ARE), and stance hypothesis optimal estimation (SHOE), is proposed. Finally, experiments are conducted, and the results indicate that the proposed algorithm achieves a zero-velocity detection accuracy of 99.18% and can reduce positioning error to less than 2% of the total distance. Furthermore, the applicability of the proposed algorithm in the practical working environment is confirmed through on-site experiments. The results demonstrate that this method can effectively suppress the accumulated error of the inertial guidance system and improve the positioning accuracy of pipe jacking. It provides a robust and reliable solution for practical engineering challenges. Therefore, this study will contribute to the development of pipe jacking automatic guidance technology.

A Bayesian Record Linkage Approach that Adjusts for Variables in One File

In many healthcare and social science applications, information about units is dispersed across multiple data files. Linking records across files is necessary to estimate associations between variables exclusive to each of the files. Common record linkage algorithms only rely on similarities between linking variables common to all the files. Moreover, analysis of linked files often ignores errors that may arise from incorrect or missed links. Bayesian record linking methods allow for natural propagation of linkage errors, by jointly sampling the linkage structure and the model parameters. We extend an existing Bayesian record linkage approach to integrate associations between variables exclusive to each file being linked. We show analytically, and using simulations, that the proposed method improves the linkage process, and results in accurate statistical inferences. We apply the proposed method to link Meals on Wheels (MOW) recipients to Medicare Enrollment records, and examine the relationship between activities of daily living and healthcare utilization among MOW recipients.

Decomposing high uncertainty in greenhouse gas mitigation pathways in emerging regions: An approach to evaluate risks toward carbon neutrality

In the context of ambitious greenhouse gas (GHG) mitigation strategies in emerging regions, addressing the prevailing uncertainty and its key influential factors is crucial. While uncertainty analysis of GHG mitigation pathways has been extensively explored, the systematic identification and quantification of pivotal factors has been notably absent. This study introduces a novel methodology that combines Quasi-Monte Carlo simulation with Sobol variance decomposition, which identifies key influential factors and traces the flow of uncertainties. Applied in Anhui Province, a rapidly developing region in China, our findings indicate an uncertain GHG emission proportion of 6.2 % by 2030, escalating to 68.6 % by 2060, with a 95 % confidence interval. This uncertainty results in a cumulative projection error of 2.1 billion tons of CO2e from 2020 to 2070. The per capita GDP factor emerges as the predominant influence, alongside the increasing impact of renewable energy factor and UHV import electricity factor. In our uncertainty flow analysis, the energy transformation sector is identified as the principal contributor to total uncertainty, driven significantly by economic and energy-related factors. These results underscore the critical need for policymakers in emerging regions to incorporate uncertainty decomposition analysis into their strategic planning to mitigate risks in GHG reduction efforts.

Robust methods, tricky materials: Challenges in dating high common-Pb perovskite from Cretaceous Brazilian kimberlites

Kimberlites and related rocks are the focus of research and exploration in the Alto Paranaiba Igneous Province (APIP; Brazil) due to their potential correlation with adjacent diamond fields. New U-Pb perovskite ages were obtained using recommended protocols for error propagation and data interpretation. Despite the utility of perovskites for dating, their incorporation of significant common-Pb (common-Pb of new APIP data: 52–84 % for in situ analyses, 75–95 % for TIMS concentrate analyses) limits the age resolution of this dating approach. Consequently, the APIP U-Pb ages neither confirm nor discredit previously proposed geodynamic models that invoke age progression or distinct magmatic episodes. Comparing the new ID-TIMS and LA-ICP-MS ages of the same intrusions, the LA-ICP-MS data have lower uncertainties, largely due to accessing a greater spread in common-Pb, improving regressions, and tend to yield older ages. For perovskite grains collected from weathering profiles, we found evidence of Pb-loss. This process leads to higher uncertainties in individual analyses, increased data scatter, and consequently higher uncertainties in age estimates. For fresh samples with different degrees of crustal contamination from a single intrusion, we found an indication of some variability in 207Pb/206Pb and 238U/206Pb ratios of the perovskite data, with a tendency towards older ages for more contaminated sample. For almost fresh intrusions with geochemically and isotopically similar mantle sources, related to the same event and in a close geographic area, we prefer to use a unique local 207Pb/206Pb ratio as anchoring for each Tera-Wasserburg plot. This approach is particularly preferred when perovskite data of an intrusion are too clustered and the 207Pb/206Pb ratio from the unanchored T-W differs from that predicted from theoretical Pb-isotope evolution model. Despite the high-common Pb in APIP perovskite crystals, our new data using these strategies clearly indicate that APIP magmatism is restricted to the Cretaceous, with most cases crystallized in Upper Cretaceous, but some from Early Cretaceous, such as the diamondiferous occurrence of Catalão-1b (120.6 ± 6.7 Ma). Different triggers of APIP magmatism are related to both episodes, mainly attributed to supercontinent break-up stages.

Extending geodetic networks for geo-monitoring by supervised point cloud matching

Abstract In this paper we propose a monitoring method that allows the inclusion of point clouds into a geodetic monitoring network. Consequently, network adjustment and a rigorous deformation analysis can be performed allowing consistent error propagation and trustful significance calculation. We introduce a supervised pipeline based on ICP-matching of small-scale laser scan patches. It is specially designed for geo-monitoring applications and allows to improve the spatial resolution as well as the network geometry of monitoring networks in inaccessible hazardous areas in the mountains, where the installation of a sufficient number of targets is difficult. We apply our method to two datasets. The first is a monitoring setup in the laboratory, where we establish the parameters for the supervised patch selection and demonstrate how the network geometry is improved. Second is the real case study of Mt. Hochvogel, where the proposed method helps to clearly improve the spatial resolution of deformation vectors.