Accurate Localization Research Articles

Abstract PO3-22-10: 3D-CT mapping with a photo-translucent application using iPad delineates tumor lesions on patient breast skin in breast-conserving surgery: Preliminary results

Abstract Objective: The aim of this study was to evaluate the feasibility and efficacy of a 3D-CT mapping technique with a photo-translucent application using an iPad for tumor localization on the patient's breast skin in breast-conserving surgery (BCS). We also compared the accuracy of tumor localization between conventional methods and 3D-CT mapping. Methods: Between February and July 2023, 20 female patients with early-stage (0 to 2) breast cancer scheduled for BCS were enrolled in this study. Preoperative enhanced CT scans were performed, and a 3D-CT image was generated by incorporating the patient's skin and manually marking the contrast-enhanced lesion using SYNAPSE VINCENT software. The 3D-CT images, adjusted to individual rotation angles to match the horizontal skin position above the tumor, were imported into an iPad equipped with a photo-translucent application. Using a securely fixed iPad holder, the translucent image was superimposed on the patient's breast, and the enhanced lesion was marked on the skin using a blue pen. Conventional tumor marking was performed based on ultrasound imaging, CT, and MRI findings using a black pen. Photographs were taken to simulate BCS based on the 3D-CT blue marking. All patients underwent BCS with resection margins of 1.5-2 cm from the black marking, and intraoperative frozen section analysis was performed to assess the resection margins. Results: The majority of patients had hormone receptor-positive breast cancers, and neoadjuvant chemotherapy was administered in three patients (Table 1). The extent of tumor involvement assessed by pathological assessment and imaging findings showed the least discrepancy with MRI, with an average error of 4.2 mm. MRI also had the lowest rate of missed tumor detection compared to other imaging modalities. The initial positive margin rate was 20%, and the simulation conducted in this study did not alter that value. Furthermore, the simulation demonstrated more effective excision of the tumor's center compared to conventional surgical methods (Table 2). Conclusion: The use of 3D-CT mapping with a photo-translucent application on an iPad offers a promising approach for accurate tumor localization in BCS. This technique provides a simplified and efficient method, and it can also be applied to cases where lesions have completely disappeared after neoadjuvant chemotherapy. XX- XX XX- XX Citation Format: Gou Watanabe, Akihiko Suzuki. 3D-CT mapping with a photo-translucent application using iPad delineates tumor lesions on patient breast skin in breast-conserving surgery: Preliminary results [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO3-22-10.

Abstract 3033: Registration And Segmentation Framework For 3d Instrument Visualization Relative To Preoperative Ct Imaging Via Spatially-tracked Ultrasound: A Phantom Study

Objectives: Surgical navigation guides surgery without radiation by mapping instrument locations onto preoperative CT (pCT). Adoption to vascular surgery remains novel. Feasibility of spatially-tracked ultrasound (US) for surgical registration and wire localization relative to pCT is demonstrated. Methods: pCT was taken (0.6mm) of a carotid artery phantom and a CT artery model (CTM) segmented. An US probe (Philips L17-5), was rigidly attached to an optical tracker and swept along the artery (1000 images). A navigation system (Medtronic StealthStation) acquired the tracker and 2D US image's positions via a spatial calibration (1.6mm accuracy). The artery in each image was segmented and tracking data combined to reconstruct a 3D US artery model (USM) (Fig. 1). The CTM and USM were registered via an iterative closest point algorithm. A Rosen wire (0.035in) was inserted into the artery. Acquiring an US sweep of the wire (500 images), it was segmented in each image via thresholding. Segmentations and tracking were combined into a 3D US wire-model (USWM). Its position was overlaid onto pCT via the USM-CTM registration. Another CT was acquired, registered via a fiducial point-based registration (0.86 mm accuracy), and the ground truth wire position segmented (Fig. 2). A tip-to-tip distance was measured between the USWM and CT wire positions. Results: CTM-to-USM registration error was 0.93 mm (RMSE) between artery point clouds. The tip-to-tip distance between CT and USWM wire models was 0.2 (longitudinal) and 1.4 (radial) mm. Conclusions: A vascular navigation framework capable of patient registration and highly accurate instrument localization is provided using tracked iUS.

Biochemical Separation of Cytoplasmic and Nuclear Fraction for Downstream Molecular Analysis.

Biochemical fractionation is a technique used to isolate and separate distinct cellular compartments, critical for dissecting cellular mechanisms and molecular pathways. Herein we outline a biochemical fraction methodology for isolation of ultra-pure nuclei and cytoplasm. This protocol utilizes hypotonic lysis buffer to suspend cells, coupled with a calibrated centrifugation strategy, for enhanced separation of cytoplasm from the nuclear fraction. Subsequent purification steps ensure the integrity of the isolated nuclear fraction. Overall, this method facilitates accurate protein localization, essential for functional studies, demonstrating its efficacy in separating cellular compartments. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol: Biochemical fractionation Support Protocol 1: Protein quantification using Bradford assay Support Protocol 2: SDS/PAGE and Western blotting.

A Section Location Method of Single-Phase Short-Circuit Faults for Distribution Networks Containing Distributed Generators Based on Fusion Fault Confidence of Short-Circuit Current Vectors

A Section Location Method of Single-Phase Short-Circuit Faults for Distribution Networks Containing Distributed Generators Based on Fusion Fault Confidence of Short-Circuit Current Vectors

Multi-hop graph transformer network for 3D human pose estimation

Accurate 3D human pose estimation is a challenging task due to occlusion and depth ambiguity. In this paper, we introduce a multi-hop graph transformer network designed for 2D-to-3D human pose estimation in videos by leveraging the strengths of multi-head self-attention and multi-hop graph convolutional networks with disentangled neighborhoods to capture spatio-temporal dependencies and handle long-range interactions. The proposed network architecture consists of a graph attention block composed of stacked layers of multi-head self-attention and graph convolution with learnable adjacency matrix, and a multi-hop graph convolutional block comprised of multi-hop convolutional and dilated convolutional layers. The combination of multi-head self-attention and multi-hop graph convolutional layers enables the model to capture both local and global dependencies, while the integration of dilated convolutional layers enhances the model’s ability to handle spatial details required for accurate localization of the human body joints. Extensive experiments demonstrate the effectiveness and generalization ability of our model, achieving competitive performance on benchmark datasets.

Spot scanning imaging calibration method based on deviation model for wafer inspection

The spot scanning defect detection system has good detection performance for wafer defects. However, due to the existence of system assembly errors, the wafer defect inspection system could not scan following the planned trace, which resulted in distortion of the reconstructed image and had a greater impact on defect feature extraction and accurate defect location. The system deviation calibration and adjustment can further facilitate the accuracy of the system position and image reconstruction. To address this issue, the causes and shapes of imaging distortion were analyzed and simulated, and a simple and effective calibration method based on a scanning deviation model for the spot scanning defect detection system was proposed. First, a scanning deviation model was established following the characteristics of the spot scanning system, and different degrees of reconstructed image distortion were simulated and analyzed as the system deviation changes. Then, an 8-inch wafer with a grid was used as the calibrator and scanned using the developed spot scanning system. Serveral grid corners in the reconstructed image are selected and used as the calibration feature points. The constraint function is constructed using the equidistant features between adjacent grid points, and then the quasi-Newton method is used to optimize the parameters of the function to obtain the best deviation estimation of the spot scanning system. To prove the validity of the estimated deviation parameters, another patterned wafer was scanned and the distortion image was used for reconstruction. The experiment demonstrated that an accurate and distortion-free wafer image can be reconstructed using the calibrated deviation parameters, and the root mean square error of the measured distance between two points of interest was only 0.61 pixels, which proved the effectiveness of the proposed calibration method.

Contact localization from soft tactile array sensor using tactile image

PurposeThis paper aims to estimate contact location from sparse and high-dimensional soft tactile array sensor data using the tactile image. The authors used three feature extraction methods: handcrafted features, convolutional features and autoencoder features. Subsequently, these features were mapped to contact locations through a contact location regression network. Finally, the network performance was evaluated using spherical fittings of three different radii to further determine the optimal feature extraction method.Design/methodology/approachThis paper aims to estimate contact location from sparse and high-dimensional soft tactile array sensor data using the tactile image.FindingsThis research indicates that data collected by probes can be used for contact localization. Introducing a batch normalization layer after the feature extraction stage significantly enhances the model’s generalization performance. Through qualitative and quantitative analyses, the authors conclude that convolutional methods can more accurately estimate contact locations.Originality/valueThe paper provides both qualitative and quantitative analyses of the performance of three contact localization methods across different datasets. To address the challenge of obtaining accurate contact locations in quantitative analysis, an indirect measurement metric is proposed.

Fast and Fault-Tolerant Passive Hyperbolic Localization Using Sensor Consensus

The accuracy of passive hyperbolic localization applications using Time Difference of Arrival (TDOA) measurements can be severely compromised in non-line-of-sight (NLOS) situations. Consensus functions have been successfully used to provide robust and accurate location estimates in such challenging situations. In this paper, a fast branch-and-bound computational method for finding the global maximum of consensus functions is proposed and the global convergence property of the algorithm is mathematically proven. The performance of the method is illustrated by simulation experiments and real measurements.

LAND SNAILS AND SLUGS (MOLLUSCA: GASTROPODA) IN THE INTERNATIONAL LOADING AND UNLOADING AREAS OF PANAMA

This study aims to identify the species of land snails and slugs that may invade Panama through the loading and unloading sites of national and international merchandise in Panama City and Colon, along with their probable places of origin. The investigation also considers the ecological, economic, and health impacts of these potential invasive species. Between September 2008 and September 2009, sampling was conducted at the main seaports and airports of Panama City and Colon, including their containers, warehouses, and surrounding natural habitats. The study utilized GPS for accurate location marking, and sampling occurred twice a week. Specimens were collected, with live material fixed in 70% alcohol, and slugs dissected for identification through sexual organs. Taxonomic identification involved conchological characteristics and expert consultation, with the data analyzed for species abundance correlation with precipitation and humidity. A total of 2379 individuals were collected, identifying seven species across nine genera and four taxonomic families, with the Subulinidae family being the most abundant. Notably, Praticolella griseola, an agricultural pest, and Hapiella w. f. decolorata were new records for Panama. The study found species richness and abundance correlated with rainfall but not relative humidity. The research highlighted the quarantine importance of several species due to their roles as pests and vectors of diseases like Angiostrongylus cantonensis and A. costarricensis. Panama hosts a moderate ecological diversity of gastropods, with certain species posing significant risks to agriculture, public health, and native ecosystems. The study underscores the necessity for ongoing surveillance and management of non-native mollusk introductions, particularly in a global trade hub like Panama. Identifying and understanding the distribution of invasive gastropods are crucial for developing effective control measures and mitigating their impact on the environment and human health.

Adaptive module and accurate heatmap translator for multi-person human pose estimation

Human pose estimation is an important task in computer vision and an essential step for computers to understand human motion and behavior. However, accurate localization of keypoints for small individuals in multi-person pictures is often ignored by current methods, resulting in limited improvements in accuracy. In addition, the current mainstream methods of translating a predicted heatmap into a coordinate in the original image space is too coarse. This coarseness also affects the localization of keypoints. To address these challenges, we propose an adaptive human body size module(AHBZM), spatial selective attention module(SSAM) and more accurate heatmap translator(MAHT) for human pose estimation. The proposed AHBZM utilizes trainable parameters to select a more appropriate multi-scale fusion method to further refine the localization of keypoints for different body sizes. To further improve keypoints localization, SSAM is used to capture target spatial information during feature fusion. The proposed MAHT will more accurately add the pixel offsets when translating heatmap coordinates into original image coordinates, while more closely associating the global maximum value in the heatmap with the surrounding local maximum values. The experimental results show that the proposed method has achieved good results on the two benchmark datasets of COCO and MPII. Our code is available at: https://github.com/illusory2333/Adaptive-module-and-heatmap-translator.

Seamless Fusion: Multi-Modal Localization for First Responders in Challenging Environments.

In dynamic and unpredictable environments, the precise localization of first responders and rescuers is crucial for effective incident response. This paper introduces a novel approach leveraging three complementary localization modalities: visual-based, Galileo-based, and inertial-based. Each modality contributes uniquely to the final Fusion tool, facilitating seamless indoor and outdoor localization, offering a robust and accurate localization solution without reliance on pre-existing infrastructure, essential for maintaining responder safety and optimizing operational effectiveness. The visual-based localization method utilizes an RGB camera coupled with a modified implementation of the ORB-SLAM2 method, enabling operation with or without prior area scanning. The Galileo-based localization method employs a lightweight prototype equipped with a high-accuracy GNSS receiver board, tailored to meet the specific needs of first responders. The inertial-based localization method utilizes sensor fusion, primarily leveraging smartphone inertial measurement units, to predict and adjust first responders' positions incrementally, compensating for the GPS signal attenuation indoors. A comprehensive validation test involving various environmental conditions was carried out to demonstrate the efficacy of the proposed fused localization tool. Our results show that our proposed solution always provides a location regardless of the conditions (indoors, outdoors, etc.), with an overall mean error of 1.73 m.

Preoperative Angiographic Protocol for the Surgical Management of Spinal Dural Arteriovenous Fistulas: A Technical Nuance for Safe Minimally Invasive Surgery

BackgroundInterruption of the fistoulus point is the goal of the treatment of spinal dural arteriovenous fistula (dAVFs). Microsurgery still represents a highly efficient treatment in terms of complete occlusion with the lowest risk of recurrence rate. It is reported that the hardest step consists in finding the fistulous site itself, potentially extending surgical access and time, and increasing potential post-operative surgical-related complications. The accurate preoperative detection of the shunt and spinal level together is crucial for guiding optimal, fast and safe microsurgical treatment. MethodsIn the present paper, the authors describe a preoperative angiographic protocol for achieving a safe and simple resection of spinal dAVFs based on a six-year Institutional experience of forty-two patients who underwent minimally invasive. Two illustrative cases are also included in order to support the technical descriptions. ResultsThe suspected artery associated to the vascular malformation of interest is studied in our angiographic protocol through non-subtracted selective acquisitions in the lateral projection. The resulting frames are reconstructed with 3D Rotational Angiography. The implementation of the preoperative angiographic protocol allowed 100% of intraoperative identification of the fistoulus point in all cases with the use of minimally invasive approach. ConclusionsNowadays, neurosurgeons advocate for minimally invasive procedures and low morbidity risk procedures for the treatment of spinal dAVFs.Our preoperative approach for accurate angigoraphic localization of the fistoulus point through non-subtracted and 3D reconstructed angiography allowed to achieve safe and definitive occlusion of the shunt.

Image-guided navigation in posterior orbital tumour surgery: a comparative cohort study

ABSTRACT Purpose The posterior orbit is a confined space, harbouring neurovascular structures, frequently distorted by tumours. Image-guided navigation (IGN) has the potential to allow accurate localisation of these lesions and structures, reducing collateral damage whilst achieving surgical objectives. Methods We assessed the feasibility, effectiveness and safety of using an electromagnetic IGN for posterior orbital tumour surgery via a comparative cohort study. Outcomes from cases performed with IGN were compared with a retrospective cohort of similar cases performed without IGN, presenting a descriptive and statistical comparative analysis. Results Both groups were similar in mean age, gender and tumour characteristics. IGN set-up and registration were consistently achieved without significant workflow disruption. In the IGN group, fewer lateral orbitotomies (6.7% IGN, 46% non-IGN), and more transcutaneous lid and transconjunctival incisions (93% IGN, 53% non-IGN) were performed (p = .009). The surgical objective was achieved in 100% of IGN cases, with no need for revision surgery (vs 23% revision surgery in non-IGN, p = .005). There was no statistically significant difference in surgical complications. Conclusion The use of IGN was feasible and integrated into the orbital surgery workflow to achieve surgical objectives more consistently and allowed the use of minimal access approaches. Future multicentre comparative studies are needed to explore the potential of this technology further.

DRIVER DROWSINESS DETECTION AND CREATING SAFETY MEASURES USING OPENCV,PYTHON

Accidents due to driver drowsy driving is significantly increasing in high ratio now days. So we proposed an AI based solution to reduce the driver drossy driving accident by alerting the driver when they fall asleep. This is an brief abstract that explains system working and principles. This paper present a novel, Real-Time Driver Drowsiness Detection System(DDDS) that utilizes a multi-model approach for fatigue assessment. The system incorporates computer vision and machine learning techniques to analyze facial features, eye movements, and physical signal(if applicable) captured through a webcam processing techniques enhance data processing techniques enhanced data quality for better analysis.Facial landmarks are identified to locate the eyes, and features like Eye Aspect Ratio(EAR) are used to detect signs of fatigue. The system employs a multimodal approach, utilizing computer vision techniques to analyze facial features and eye movements captured through a webcam. Specifically, the DDDS leverages the Haar cascade algorithm for efficient facial landmark detection and the Histogram of Oriented Gradients (HOG) algorithm for feature extraction from facial regions of interest. This combination allows for accurate localization and analysis of key features like eyes. Additionally, the system explores the potential of physiological data (e.g., heart rate variability) for a more comprehensive analysis (if applicable). Machine learning techniques (if applicable) can be employed on the combined features, including those extracted using Haar and HOG, for enhanced fatigue classification. Upon detecting drowsiness, the DDDS triggers customizable alerts (visual, audio, or haptic) to warn the driver. The system additionally explores the potential of physiological data (e.g., heart rate variability) for fatigue detection, offering a more comprehensive approach (if applicable).A machine learning model can be employed on the combined features for robust fatigue classification. Upon detection of drowsiness,the DDDS triggers customizable alerts ( visual, audio, or haptic) to warn the driver. This system addresses limitations of existing solution by: 1.Real-Time processing:Ensuring immediate detection and response to fatigue signs. 2.Multi feature analysis: Combining facial features, eye movements,and physiological data for robust detection. 3.Customizable alerts: Providing drivers control over alert type for a personalized experience. The evaluation methodology employs a dataset of [data type e.g.., image, videos] to access the system’s performance.Metrics like accuracy,precision,recall,and F1-score will be used to evaluate the effectiveness of the DDDS in detecting drowsy driving. This research aims to contribute to safer roads by developing a comprehensive and adaptable Driver Drowsiness Detection System. II.KEYWORDS: Drowsy Driving, Driver Monitoring,Computer Vision, Machine Learning,Eye Tracking, Fatigue Detection,Physiological Signs(if applicable)

Comprehensive analysis of bond stress transfer mechanisms in glass fiber-reinforced polymer-reinforced concrete beams using acoustic emission monitoring

The present study investigates the critical bond characteristics between glass fiber-reinforced polymer (GFRP) bars, which is essential for the application bars as corrosion-resistant components in structural concrete. The interfacial bond deterioration and stress transfer mechanisms have been investigated in the present study by employing acoustic emission (AE) monitoring in hinged-type GFRP-reinforced concrete (GFRP-RC) beam specimens. The bond strength of the test specimens, governed by chemical adhesion, mechanical interlocking, and frictional resistance, has been intricately examined. Various AE signal parameters as well as historic and severity indices derived from the AE signal parameters have been meticulously evaluated and correlated with bond stress and rebar slip variations. The present study has also introduced an innovative approach of using the sentry function for the assessment of bond deterioration and associated mechanisms. The results of the present study demonstrate the reliability of AE parameters in monitoring debonding and bond strength variation, the effectiveness of the sentry function in evaluating damage progression, and the utility of historic and severity indices in capturing micro-mechanism transformations. The accurate localization of debonding and the qualitative distribution of bond stress have also been demonstrated by through correlations between cumulative AE parameters and rebar slip, Additionally, the present study also highlights the significance of peak frequency variations in AE signals, serving as a crucial tool for quantifying the mechanical interlocking and interfacial friction. The results contribute significantly to precise assessments and development of effective strategies for mitigating bond deterioration in GFRP-RC elements.

UViT-Seg: An Efficient ViT and U-Net-Based Framework for Accurate Colorectal Polyp Segmentation in Colonoscopy and WCE Images.

Colorectal cancer (CRC) stands out as one of the most prevalent global cancers. The accurate localization of colorectal polyps in endoscopy images is pivotal for timely detection and removal, contributing significantly to CRC prevention. The manual analysis of images generated by gastrointestinal screening technologies poses a tedious task for doctors. Therefore, computer vision-assisted cancer detection could serve as an efficient tool for polyp segmentation. Numerous efforts have been dedicated to automating polyp localization, with the majority of studies relying on convolutional neural networks (CNNs) to learn features from polyp images. Despite their success in polyp segmentation tasks, CNNs exhibit significant limitations in precisely determining polyp location and shape due to their sole reliance on learning local features from images. While gastrointestinal images manifest significant variation in their features, encompassing both high- and low-level ones, a framework that combines the ability to learn both features of polyps is desired. This paper introduces UViT-Seg, a framework designed for polyp segmentation in gastrointestinal images. Operating on an encoder-decoder architecture, UViT-Seg employs two distinct feature extraction methods. A vision transformer in the encoder section captures long-range semantic information, while a CNN module, integrating squeeze-excitation and dual attention mechanisms, captures low-level features, focusing on critical image regions. Experimental evaluations conducted on five public datasets, including CVC clinic, ColonDB, Kvasir-SEG, ETIS LaribDB, and Kvasir Capsule-SEG, demonstrate UViT-Seg's effectiveness in polyp localization. To confirm its generalization performance, the model is tested on datasets not used in training. Benchmarking against common segmentation methods and state-of-the-art polyp segmentation approaches, the proposed model yields promising results. For instance, it achieves a mean Dice coefficient of 0.915 and a mean intersection over union of 0.902 on the CVC Colon dataset. Furthermore, UViT-Seg has the advantage of being efficient, requiring fewer computational resources for both training and testing. This feature positions it as an optimal choice for real-world deployment scenarios.

A Novel Active RFID and TinyML based system for livestock Localization in Pakistan

Localization of livestock is a vital component of good livestock management in Pakistan. This abstract describes a unique method for livestock localization in Pakistan that makes use of Active RFID technology and Tiny Machine Learning (TinyML) approaches. The incorporation of Active RFID technology allows for precise and long-range livestock tracking, while TinyML provides on-device analysis and decision-making. This method has a number of advantages, including high precision, real-time localization, and less reliance on external infrastructure. Accurate triangulation-based localization is obtained by putting Active RFID tags on cattle and carefully positioning Active RFID anchors in specific regions. TinyML integration on resource-constrained microcontrollers within Active RFID tags allows for efficient on-device analysis of Active RFID signals. The suggested system has the potential to significantly improve livestock management practices in Pakistan, including animal tracking and monitoring, behavior analysis, and increased animal welfare. To realize the full potential of this unique Active RFID and TinyML-based livestock localization system in Pakistan, further research should focus on optimizing localization algorithms, enhancing TinyML models, and exploring interaction with upcoming technologies

Context Aggregation Network for Remote Sensing Image Semantic Segmentation

In recent years, remote sensing technology has been widely applied in various industries, and semantic segmentation of remote sensing images has attracted much attention. Due to the complexity and special characteristics of remote sensing images, multi-scale object detection and accurate object localization are important challenges in remote sensing image semantic segmentation. Therefore, this paper proposes a context aggregation network (CANet). The design of CANet is influenced by advanced technologies such as attention mechanisms and feature fusion and enhancement. This network first introduces nested dilated residual module (NDRM), which can fully utilize the features extracted by the backbone network. Then, improved integrated successive dilation module (IISD) is proposed to effectively aggregate a series of contextual information scales. Next, Swim Transformer module is embedded to provide global contextual information. Finally, multi-resolution fusion module (MRFM) is proposed, allowing the comprehensive fusion of feature layers from different stages of the encoder, preserving more semantic and detailed information. The experimental results show that CANet outperforms other advanced models on the Potsdam and Vaihingen datasets.

A New Trajectory Reduction Method for Mobile Devices Operating Both Online and Offline

AbstractHighly accurate location data have become essential in nearly all contemporary global applications, including but not limited to route planning, processing traffic data, identifying common routes, map matching, and enhancing agricultural productivity. However, the abundance of unnecessary and redundant data leads to various challenges, especially concerning storage, processing, and transmission. Despite the existence of numerous studies aimed at addressing these GNSS data management challenges, the reduction problem is either partially resolved, or enhancements are made to existing solutions in nearly all of them. In this study, a novel reduction method is introduced, offering both a high reduction rate and accuracy, suitable for operation on mobile devices in both offline and online modes. The proposed method uses windowing with reference points during the decision phase to decrease the number of points. By utilizing the angle and its threshold between the decision point and reference points, we achieved a method characterized by low algorithmic complexity and a high reduction rate, suitable for online operation on mobile devices. Experiments and comparisons revealed that the proposed method had a 91.01% reduction in GNSS data which is 7.73% lower, a 5.8744e$$-$$ - 04 RMSE error which is 2e+7 times better, and a 14.54 ms running time which is 25% faster than RDP algorithm. The results indicate that incorporating the proposed method into current methodologies could be beneficial, particularly in scenarios where real-time, high-precision location data are essential.

Local earthquake tomography of the Aegean crust: Implications for active deformation, large earthquakes, and arc volcanism

Three-dimensional velocity models can enable accurate earthquake location, improved seismic hazard assessment, and can enhance our understanding of geodynamic processes. This is particularly true for areas such as the Aegean, where the crust is marked by active volcanoes as well as shear and rift zones, all of which make this region highly heterogeneous. This work describes the application of local earthquake tomography in order to elucidate the crustal structure of the Aegean. We utilized a dataset of 2,135,625 P arrivals and 1,095,515 S arrivals from 178,676 events recorded across 324 stations. Results indicate that the boundary separating the slow Vp from the fast anomalies at 20–40 km depth is well correlated with the Aegean Moho obtained from receiver functions. A slow Vp (−5%) zone at depths >30 km along the Hellenic arc is likely associated with basal underplating that forms the base of the crust. On the other hand, fast Vp and moderate to high Vp/Vs (1.77–1.92) in the upper-middle crust may indicate evolving metamorphic core complexes. Steeply dipping clusters of earthquakes could highlight pathways of fluid migration from the base to the middle/upper crust, albeit more detailed seismological studies are needed to confirm this interpretation. The 3D velocity model can be also utilized in order to investigate the amount of melt fraction beneath active volcanoes and the influence of fluids on the rupture zone of large earthquakes. Our results show a melt fraction between 4 and 10% beneath active volcanoes, with the largest volume of melt present beneath Santorini caldera. The ruptures zones of 10 large earthquakes (Mw ≥ 6.0), show slow Vp anomalies and moderate to high Vp/Vs (> 1.77), suggesting that these events were likely triggered by weakening of their source zones by fluid activity.