Search Results (191)

Hydraulic fracturing of unconventional reservoirs requires accurate fracture monitoring for treatment optimization. Low-frequency distributed acoustic sensing (LF-DAS) in neighbor wells provides dense strain-rate observations, but gauge-length averaging limits spatial resolution and merges closely spaced fracture features. This study formulates gauge-length averaging as an explicit convolution operator and develops a regularized inversion method combining Tikhonov smoothing, a recursive prior, and L-curve parameter selection, supported by a semi-analytical multi-fracture forward model. On a synthetic benchmark, the method advances the effective resolution from the 10 m gauge-length scale to the 1 m sample-spacing scale, recovering fracture count in all hit-window time slices (versus 32% for raw data), achieving Pearson correlation of 0.80 versus 0.29, with peak-position error reduced by 47%. Noise-sensitivity analysis indicates a practical SNR floor near 20 dB, and Wiener-filter comparison confirms 1.5–2.7× correlation and 1.5–2.3× peak-count advantages across tested noise levels. Field application to HFTS-2 B1H stages 22 and 23 reveals previously hidden tensile features consistent with higher local fracture density. With per-stage processing in seconds and no extra sensing hardware, the method is well suited for near-real-time deployment. Full article

Satellite-derived bathymetry (SDB) offers a practical alternative for mapping shallow reefs in remote oceanic settings where acoustic surveys are costly and logistically constrained. Here we benchmark PlanetScope 8-band (3 m) surface reflectance—an underused commercial constellation for reef SDB—using ICESat-2 Advanced Topographic Laser Altimeter System (ATLAS) ATL03 photon data (Release 006) as independent vertical control. Seventeen ATL03 ground tracks (2019–2025) were processed using geometric filtering, photon classification, and explicit air–water refraction correction. This yielded 5171 candidate seafloor observations, of which 5021 were co-located with valid PlanetScope water pixels after Usable Data Mask screening (UDM2/UDM2.1), sun-glint correction, and reflectance quality screening. Four SDB formulations (Lyzenga, Bierwirth, and Stumpf) were calibrated and independently validated using depth-stratified train/validation partitions (70/30, 80/20, and 90/10). Across partitions, the multiband polynomial model of Lyzenga 2006 generalized best (R² = 0.843–0.859; RMSE = 1.734–1.813 m; bias = −0.070 to −0.081 m), followed by Bierwirth (R² = 0.826–0.845; RMSE = 1.818–1.904 m). Lyzenga 1985 reported lower skill (RMSE ≈ 3.1 m), while the Stumpf log-ratio failed in independent validation. ICESat-2 photon bathymetry provides repeatable point-based control in clear waters but remains less precise than echo sounding due to photon classification and spatial-support effects; therefore, uncertainties and applicability limits must be reported. Overall, PlanetScope 3 m, 8-band surface reflectance supports reproducible reef-scale SDB in Seaflower under the evaluated conditions, with Lyzenga 2006 as a robust baseline. Full article

Vertical docking of the autonomous underwater shuttle (AUS) for deep-sea data relay relies heavily on ultra-short baseline (USBL) acoustic positioning, whose measurements can be intermittently unavailable and contaminated by outliers in complex underwater environments. This paper proposes a neural network-enhanced robust navigation framework to improve AUS navigation reliability during acoustically guided vertical docking under USBL outages. First, a model-aided batch maximum a posteriori trajectory estimation method (MA-BMAP) is developed to generate learning quality supervision under sensor-limited conditions. Based on the estimated trajectories, a long short-term memory (LSTM)-based horizontal velocity predictor is integrated into a robust fusion filter with online ocean current estimation, enabling stable state estimation during USBL outages and robust rejection of abnormal USBL measurements. The proposed framework is validated through simulations and field trials in lake and sea environments. In sea trials, during two representative 200 s USBL outage intervals, the end-of-window horizontal position errors are 7.86 m and 4.14 m, respectively, corresponding to AUS-to-docking station distances of 244 m and 51 m. In addition, the introduced USBL outliers are successfully detected and rejected. The results indicate that the proposed method enables accurate and stable navigation during USBL unavailability and rapid recovery once USBL measurements resume, demonstrating its practicality for vertical docking missions. Full article

The development of AI-driven computational methods has enabled rapid and non-invasive analysis of respiratory sounds using acoustic data, particularly cough recordings. Although the COVID-19 pandemic accelerated research on cough-based acoustic analysis, many early studies were limited by insufficient data quality, lack of standardized protocols, and limited reproducibility due to data scarcity. In this study, we propose an audio analysis framework for cough-based respiratory disease screening research using COVID-19 as a clinically validated case dataset. All analyses were conducted on a single clinically acquired multicentric dataset collected under standardized conditions in certified laboratories in Mexico and Spain, comprising cough recordings from 1105 individuals. Model training and testing were performed exclusively within this dataset. The framework incorporates signal preprocessing and a comparative evaluation of segmentation strategies, showing that segmented cough analysis significantly outperforms full-signal analysis. Class imbalance was addressed using the Synthetic Minority Over-sampling Technique (SMOTE) for CNN2D models and the supervised Resample filter implemented in WEKA for classical machine learning models, both applied exclusively to the training subset to generate balanced training sets and prevent data leakage. Feature extraction and classification were carried out using Random Forest, Support Vector Machine (SVM), XGBoost, and a 2D Convolutional Neural Network (CNN2D), with hyperparameter optimization via AutoML. The proposed framework achieved a best balanced screening performance of 85.58% sensitivity and 86.65% specificity (Random Forest with GeMAPSvB01), while the highest-specificity configuration reached 93.90% specificity with 18.14% sensitivity (CNN2D with SMOTE and AutoML). These results demonstrate the methodological feasibility of the proposed framework under the evaluated conditions. Full article

Underwater Acoustic Sensor Networks (UASNs) are pivotal for environmental monitoring, surveillance, and marine data collection. However, their open and largely unattended operational settings, constrained communication capabilities, limited energy resources, and susceptibility to insider attacks make it difficult to achieve safe, secure, and efficient collaborative learning. Federated learning (FL) offers a privacy-preserving method for decentralized model training but is inherently vulnerable to Byzantine threats and malicious participants. This paper proposes trust-aware FL for underwater sensor networks (TAFL-UWSN), a trust-aware FL framework designed to improve security, reliability, and energy efficiency in UASNs by incorporating trust evaluation directly into the FL process. The goal is to mitigate the impact of adversarial nodes while maintaining model performance in low-resource underwater environments. TAFL-UWSN integrates continuous trust scoring based on packet forwarding reliability, sensing consistency, and model deviation. Trust scores are used to weight or filter model updates both at the node level and the edge layer, where Autonomous Underwater Vehicles (AUVs) act as mobile aggregators. A trust-aware federated averaging algorithm is implemented, and extensive simulations are conducted in a custom Python-based environment, comparing TAFL-UWSN to standard FedAvg and Byzantine-resilient FL approaches under various attack conditions. TAFL-UWSN achieved a model accuracy exceeding 92% with up to 30% malicious nodes while maintaining a false positive rate below 5.5%. Communication overhead was reduced by 28%, and energy usage per node dropped by 33% compared to baseline methods. The TAFL-UWSN framework demonstrates that integrating trust into FL enables secure, efficient, and resilient underwater intelligence, validating its potential for broader application in distributed, resource-constrained environments. Full article

Sand production in an offshore hydrocarbon wells poses significant operational and integrity challenges, particularly in deviated wells, where complex flow geometries intensify particle transport and erosion risks. The traditional sand-monitoring method utilizes stationary acoustic sensors attached to the production flowline at the surface. However, these sensors provide limited spatial coverage and intermittent measurements, restricting their ability to detect early sanding onset or precisely localize sanding intervals. By combining with vertical seismic profiling (VSP), Distributed Acoustic Sensing (DAS) delivers continuous, high-density data along the entire length of the wellbore and is increasingly recognized as a powerful diagnostic tool for real-time downhole monitoring. This study presents a field application of DAS-VSP for detecting and characterizing sand transport in a deviated offshore production well equipped with 350 distributed fiber-optic channels spanning 0–1983 m true vertical depth (TVD) at 8 m spacing. A multistage workflow was developed, including SEGY ingestion and shot merging, channel and time window selection, trace normalization, and low-pass filtering below 20 Hz. Multi-domain signal analysis, such as RMS energy, spike-based time-domain attributes, FFT, PSD spectral characterization, and time–frequency decomposition, were used to isolate the characteristic im-pulsive low-frequency (<20 Hz) signatures associated with sand impact. An adaptive thresholding and event-clustering scheme was then applied to discriminate sanding bursts from background noise and integrate their acoustic energy over depth. The processed DAS section revealed distinct, depth-localized sand ingress zones within the production interval (1136–1909 m TVD). The derived sand log provided a quantitative measure of sand intensity variations along the deviated wellbore, with normalized RMS amplitudes ranging from 0.039 to 1.000 a.u., a mean value of 0.235 a.u., and 137 analyzed channels within the production interval. These results indicate that sand production is highly clustered within discrete depth intervals, offering new insights into sand–fluid interactions during steady-state flow. Overall, the findings confirm that DAS-VSP enables continuous real-time monitoring of the sanding behavior with a far greater depth resolution than conventional tools. This approach supports proactive sand management strategies, enhances well-integrity decision-making, and underscores the potential of DAS to evolve into a standard surveillance technology for hydrocarbon production wells. Full article

Microseismic identification and localization of signals from single-component distributed optical fiber acoustic sensors (DAS) in vertical wells are limited by low signal-to-noise ratio and lack of directional information, making effective signal identification and accurate localization difficult. Improving the detection rate and accuracy of such data events is helpful for analyzing the effect of fracturing. To address this, this paper proposes a method for automatically picking and locating microseismic events based on dual fitting modeling and waveform inversion. First, empirical mode decomposition (EMD) is used to adaptively decompose and reconstruct the original DAS signal to filter out approximately 80% of high-frequency noise (noise above 200 Hz). Second, the classic short-time average/long-time average energy ratio algorithm is used to pick all “event points.” Finally, DBSCAN density clustering and RANSAC robust fitting are combined to perform secondary screening and fitting modeling of the “event points” to obtain the continuous event arrival time distribution along the well section direction, and the spatial location of the seismic source is inverted based on the fitting results. Tested with experimental data from Well XX, the automatic detection rate reached 96%, and the accuracy of machine detection compared with manual judgment reached 95%. Full article

With the rapid development and widespread application of multibeam echo-sounding systems, large-scale and high-resolution seafloor topography can be efficiently acquired, enabling precise mapping of seabed terrain. However, due to complex oceanographic conditions, instrumental noise, and acoustic interferences, the acquired multibeam data often contain outliers that deviate from the true seafloor surface. These outliers can distort the representation of seafloor topography, adversely affecting subsequent geological analysis and engineering applications. To address this issue, a hybrid outlier detection method combining CUBE filtering with the Isolation Forest (IForest) algorithm, termed CUBE-IForest, is proposed. The method first employs CUBE filtering to remove gross outliers based on local uncertainty estimation, followed by the application of IForest to identify subtle anomalies in the refined data, achieving hierarchical detection of outliers. Experimental results based on in situ multibeam bathymetric data from the northeastern Pacific demonstrate that compared with traditional filtering methods the CUBE-IForest approach significantly improves detection accuracy and reduces both false positive and false negative rates by approximately 30%, confirming its efficiency and reliability in seafloor mapping and analysis. Full article

Acoustic emission (AE) technology, a kind of non-destructive testing method, was used in this study to monitor the fracture process of Q245R steel in the single edge notched tension (SENT) test. The obtained AE signals were first processed by the sensor gauge method to distinguish the noise and signals related to a fracture. Based on the filtered data, it was found that the load-displacement curve and load–Crack Mouth Opening Distance (CMOD) curve of the fracture development were correlated with the characteristics of signals. In addition, an AE crack development index (CDI) was proposed to characterize different stages in the crack propagation process, and the results were verified by unloading compliance experiments. The results showed that the condition of structure can be well characterized by trends of cumulative counts and peak amplitudes of AE signals. In addition, stable cracks were found to occur when the load reached 92% of the ultimate load which produced AE signals with high counts, duration, and more high-amplitude signals. The proposed AE CDI of 40%max(CDI), 50%max(CDI), and 60%max(CDI) reflects the elastic, plastic, and stable crack propagation stages under monotonic tension, respectively, and remains stable even when the tensile loading method changes. Full article

The integration of artificial intelligence (AI) with ultrasonic biosensing presents a transformative opportunity for enhancing diagnostic accuracy in agricultural and biomedical applications. This study develops a data-driven deep learning model to address the challenge of acoustic artifacts in B-mode ultrasound imaging, specifically for sow pregnancy diagnosis. We designed a biosensing system centered on a mechanical sector-scanning ultrasound probe (5.0 MHz) as the core biosensor for data acquisition. To overcome the limitations of traditional filtering methods, we introduced a lightweight Deep Neural Network (DNN) based on the YOLOv8 architecture, which was data-driven and trained on a purpose-built dataset of sow pregnancy ultrasound images featuring typical artifacts like reverberation and acoustic shadowing. The AI model functions as an intelligent detection layer that identifies and masks artifact regions while simultaneously detecting and annotating key anatomical features. This combined detection–masking approach enables artifact-aware visualization enhancement, where artifact regions are suppressed and diagnostic structures are highlighted for improved clinical interpretation. Experimental results demonstrate the superiority of our AI-enhanced approach, achieving a mean Intersection over Union (IOU) of 0.89, a Peak Signal-to-Noise Ratio (PSNR) of 34.2 dB, a Structural Similarity Index (SSIM) of 0.92, and clinically tested early gestation accuracy of 98.1%, significantly outperforming traditional methods (IoU: 0.65, PSNR: 28.5 dB, SSIM: 0.72, accuracy: 76.4). Crucially, the system maintains a single-image processing time of 22 ms, fulfilling the requirement for real-time clinical diagnosis. This research not only validates a robust AI-powered ultrasonic biosensing system for improving reproductive management in livestock but also establishes a reproducible, scalable framework for intelligent signal enhancement in broader biosensor applications. Full article

Total organic carbon (TOC) is a critical parameter for evaluating shale source rock quality and hydrocarbon generation potential. However, accurate TOC estimation from conventional well logs remains challenging, especially in data-limited geological settings. This study proposes an optimized XGBoost model for TOC prediction using conventional logging data from the Shahejie Formation in the Dongying Depression, Bohai Bay Basin, China. We systematically transform four standard logs—resistivity, acoustic transit time, density, and neutron porosity—into 165 candidate features through multi-scale smoothing, statistical derivation, interaction term creation, and spectral transformation. A two-stage feature selection process, combining univariate filtering and recursive feature elimination and further refined by principal component analysis, identifies ten optimal predictors. The model hyperparameters are optimized via Bayesian search within the Optuna framework to minimize cross-validation error. The optimized model achieves an R² of 0.9395, with a Mean Absolute Error (MAE) of 0.3392, a Root Mean Squared Error (RMSE) of 0.4259, and a Normalized Root Mean Squared Error (NRMSE) of 0.0604 on the test set, demonstrating excellent predictive accuracy and generalization capability. This study provides a reliable and interpretable methodology for TOC characterization, offering a valuable reference for source rock evaluation in analogous shale formations and sedimentary basins. Full article

Background/Objectives: The vestibular labyrinth is classically viewed as a sensor of low-frequency head motion—linear acceleration for the otoliths and angular velocity/acceleration for the semicircular canals. However, there is now substantial evidence that air-conducted sound (ACS) can also activate vestibular receptors and afferents in mammals and other vertebrates. This sound sensitivity underlies sound-evoked vestibular-evoked myogenic potentials (VEMPs), sound-induced eye movements, and several clinical phenomena in third-window pathologies. The cellular and biophysical mechanisms by which a pressure wave in the cochlear fluids is transformed into a vestibular neural signal remain incompletely integrated into a single framework. This study aimed to provide a narrative synthesis of how ACS activates the vestibular labyrinth, with emphasis on (1) the anatomical and biophysical specializations of the maculae and cristae, (2) the dual-channel organization of vestibular hair cells and afferents, and (3) the encoding of fast, jerk-rich acoustic transients by irregular, striolar/central afferents. Methods: We integrate experimental evidence from single-unit recordings in animals, in vitro hair cell and calyx physiology, anatomical studies of macular structure, and human clinical data on sound-evoked VEMPs and sound-induced eye movements. Key concepts from vestibular cellular neurophysiology and from the physics of sinusoidal motion (displacement, velocity, acceleration, jerk) are combined into a unified interpretative scheme. Results: ACS transmitted through the middle ear generates pressure waves in the perilymph and endolymph not only in the cochlea but also in vestibular compartments. These waves produce local fluid particle motions and pressure gradients that can deflect hair bundles in selected regions of the otolith maculae and canal cristae. Irregular afferents innervating type I hair cells in the striola (maculae) and central zones (cristae) exhibit phase locking to ACS up to at least 1–2 kHz, with much lower thresholds than regular afferents. Cellular and synaptic specializations—transducer adaptation, low-voltage-activated K⁺ conductances (K_LV), fast quantal and non-quantal transmission, and afferent spike-generator properties—implement effective high-pass filtering and phase lead, making these pathways particularly sensitive to rapid changes in acceleration, i.e., mechanical jerk, rather than to slowly varying displacement or acceleration. Clinically, short-rise-time ACS stimuli (clicks and brief tone bursts) elicit robust cervical and ocular VEMPs with clear thresholds and input–output relationships, reflecting the recruitment of these jerk-sensitive utricular and saccular pathways. Sound-induced eye movements and nystagmus in third-window syndromes similarly reflect abnormally enhanced access of ACS-generated pressure waves to canal and otolith receptors. Conclusions: The vestibular labyrinth does not merely “tolerate” air-conducted sound as a spill-over from cochlear mechanics; it contains a dedicated high-frequency, transient-sensitive channel—dominated by type I hair cells and irregular afferents—that is well suited to encoding jerk-rich acoustic events. We propose that ACS-evoked vestibular responses, including VEMPs, are best interpreted within a dual-channel framework in which (1) regular, extrastriolar/peripheral pathways encode sustained head motion and low-frequency acceleration, while (2) irregular, striolar/central pathways encode fast, sound-driven transients distinguished by high jerk, steep onset, and precise spike timing. Full article

This study tackles real-time state estimation for autonomous underwater vehicle (AUV)–mobile docking station (MDS) cooperation over low-bandwidth, high-latency, jitter-dominated acoustic links, with the goal of turning delayed/out-of-sequence measurements (OOSM) into consistent and informative constraints without sacrificing online operation. We propose an integrated scheme centered on a delay-compensated extended Kalman filter (DC-EKF): a ring buffer enables backward updates and forward replay so that OOSM are absorbed strictly at their physical timestamps; a data-driven delay threshold is learned from “effective information gain” combined with normalized estimation error squared (NEES) filtering; and dynamic confidence, derived from innovation statistics, delay, and signal-to-noise ratio (SNR) proxies, scales the measurement noise to adapt fusion weights. Simulations show the learned delay threshold converges to about 6.4 s (final 6.35 s), error spikes are suppressed, and the overall position root-mean-square error (RMSE) is 5.751 m; across the full data stream, 1067 station measurements were accepted and 30 rejected, and the fusion weights shifted smoothly from inertial measurement unit (IMU)-dominant to station-dominant (≈0.16/0.84) over time. On this basis, a cooperative augmented EKF (Co-Aug-EKF) is added as a lightweight upper layer for unified-frame cooperative estimation, further improving relative consistency. The results indicate that the framework reliably maps delayed acoustic measurements into closed-loop useful information, significantly enhancing estimator stability and docking readiness, while remaining practical to deploy and readily extensible. Full article

Petrochemical port cities in China face the challenge of promoting industrial development and improving environmental quality. In this situation, this paper constructs a semantic ontology-based data model from the perspective of the overall classification of environmental factors to review the environmental quality of the last three years in seven major petrochemical port cities in China. The process includes three stages. Firstly, the information sources were identified, and the research team collected and screened 1858 related papers from Web of Science and the China National Knowledge Infrastructure according to the theme of the review. Secondly, the information preprocessing was carried out, and the selected literature was sorted and filtered according to different cities and environmental elements. Finally, the research team established semantic ontology data models for the atmosphere, water, soil, biology, and acoustics environment based on the preprocessed information through visualization analysis. By using these models, the research team analyzed the hotspots of pollutants and pollution sources research in different cities in various environmental domains and summarized the main pollution mitigation measures highlighted in the research. In this way, the systematic bias and structural problem of the existing environmental study were revealed. Based on the above results, the targeted governance strategies were proposed to provide theoretical support for promoting coordinated industrial and environmental development in China’s petrochemical port cities. Full article

The timely identification of rock fractures is crucial in deep subterranean engineering. However, it remains necessary to identify reliable warning indicators and establish effective warning levels. This study introduces the Acoustic Emission Transformer for FRActure Prediction (AET-FRAP) multi-input time series forecasting framework, which employs acoustic emission feature parameters. First, Empirical Mode Decomposition (EMD) combined with Fast Fourier Transform (FFT) is employed to identify and filter periodicities among diverse indicators and select input channels with enhanced informative value, with the aim of predicting cumulative energy. Thereafter, the one-dimensional sequence is transformed into a two-dimensional tensor based on its predominant period via spectral analysis. This is coupled with InceptionNeXt—an efficient multiscale convolution and amplitude spectrum-weighted aggregate—to enhance pattern identification across various timeframes. A secondary criterion is created based on the prediction sequence, employing cosine similarity and kurtosis to collaboratively identify abrupt changes. This transforms single-point threshold detection into robust sequence behavior pattern identification, indicating clearly quantifiable trigger criteria. AET-FRAP exhibits improvements in accuracy relative to long short-term memory (LSTM) on uniaxial compression test data, with R² approaching 1 and reductions in Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). It accurately delineates energy accumulation spikes in the pre-fracture period and provides advanced warning. The collaborative thresholds effectively reduce noise-induced false alarms, demonstrating significant stability and engineering significance. Full article