Acoustic Data Research Articles

Enhancing Emergency Vehicle Detection: A Deep Learning Approach with Multimodal Fusion

Emergency vehicle detection plays a critical role in ensuring timely responses and reducing accidents in modern urban environments. However, traditional methods that rely solely on visual cues face challenges, particularly in adverse conditions. The objective of this research is to enhance emergency vehicle detection by leveraging the synergies between acoustic and visual information. By incorporating advanced deep learning techniques for both acoustic and visual data, our aim is to significantly improve the accuracy and response times. To achieve this goal, we developed an attention-based temporal spectrum network (ATSN) with an attention mechanism specifically designed for ambulance siren sound detection. In parallel, we enhanced visual detection tasks by implementing a Multi-Level Spatial Fusion YOLO (MLSF-YOLO) architecture. To combine the acoustic and visual information effectively, we employed a stacking ensemble learning technique, creating a robust framework for emergency vehicle detection. This approach capitalizes on the strengths of both modalities, allowing for a comprehensive analysis that surpasses existing methods. Through our research, we achieved remarkable results, including a misdetection rate of only 3.81% and an accuracy of 96.19% when applied to visual data containing emergency vehicles. These findings represent significant progress in real-world applications, demonstrating the effectiveness of our approach in improving emergency vehicle detection systems.

Population health implications of exposure to pervasive military aircraft noise pollution.

While the adverse health effects of civil aircraft noise are relatively well studied, impacts associated with more intense and intermittent noise from military aviation have been rarely assessed. In recent years, increased training at Naval Air Station Whidbey Island, USA has raised concerns regarding the public health and well-being implications of noise from military aviation. This study assessed the public health risks of military aircraft noise by developing a systematic workflow that uses acoustic and aircraft operations data to map noise exposure and predict health outcomes at the population scale. Acoustic data encompassing seven years of monitoring efforts were integrated with flight operations data for 2020-2021 and a Department of Defense noise simulation model to characterize the noise regime. The model produced contours for day-night, nighttime, and 24-h average levels, which were validated by field monitoring and mapped to yield the estimated noise burden. Established thresholds and exposure-response relationships were used to predict the population subject to potential noise-related health effects, including annoyance, sleep disturbance, hearing impairment, and delays in childhood learning. Over 74,000 people within the area of aircraft noise exposure were at risk of adverse health effects. Of those exposed, substantial numbers were estimated to be highly annoyed and highly sleep disturbed, and several schools were exposed to levels that place them at risk of delay in childhood learning. Noise in some areas exceeded thresholds established by federal regulations for public health, residential land use and noise mitigation action, as well as the ranges of established exposure-response relationships. This study quantified the extensive spatial scale and population health burden of noise from military aviation.We employed a novel GIS-based workflow for relating mapped distributions of aircraft noise exposure to a suite of public health outcomes by integrating acoustic monitoring and simulation data with a dasymetric population density map. This approach enables the evaluation of population health impacts due to past, current, and future proposed military operations. Moreover, itcanbemodified for application to other environmental noise sources and offers an improved open-sourcetool to assess the population health implications of environmental noise exposure, inform at-risk communities, and guide efforts in noise mitigation and policy governing noise legislation, urban planning, and land use.

Marine predators segregate interspecifically by space and time in a sheltered coastal bay.

Marine predators are vital to the healthy functioning of coastal ecosystems, but to understand their roles, it is necessary to elucidate their movement ecology, particularly in relation to one another. A decade's worth of acoustic telemetry data (2011-2020) from Algoa Bay, South Africa, was investigated to determine how two mesopredatory species (teleosts: dusky kob Argyrosomus japonicus, n = 11, and leervis Lichia amia, n = 16) and two top predatory species (sharks: ragged-tooth sharks Carcharias taurus, n = 45, and white sharks Carcharodon carcharias, n = 31) used and shared this bay ecosystem. Multi-annual seasonal fidelity to the bay was exhibited by all species, but differences in residency were observed among species. Similarly, species used space in the bay differently-the teleosts moved less and had movements restricted to the central and western inshore regions of the bay. Conversely, the sharks roamed more, but detections were concentrated in the western part of the bay for C. taurus and in the eastern part of the bay for C. carcharias. Social network analysis showed that species segregated in space and time on a fine scale. However, there was some interaction observed between C. taurus, L. amia, and A. japonicus, but to varying degrees. This is likely because of strong habitat preferences exhibited by each species and predator-prey relationships between these predatory guilds. Results highlight that the sheltered marine Algoa Bay is a resource-rich environment, supporting multiple predators with different hunting strategies albeit similar prey preferences. Finally, these species are likely afforded some protection by the current Greater Addo Elephant National Park Marine Protected Area in the bay but are vulnerable to fishing pressure when they leave this ecosystem.

MBCFNet: A Multimodal Brain–Computer Fusion Network for human intention recognition

Accurate recognition of human intent is crucial for effective human–computer speech interaction. Numerous intent understanding studies were based on speech-to-text transcription, which often overlook the influence of paralinguistic cues (such as speaker’s emotion, attitude, etc.), leading to misunderstandings, especially when identical textual content conveys multiple intents by means of different paralinguistic information. Considering that interaction intent is produced from the human brain, we propose a novel Multimodal Brain–Computer Fusion Network (MBCFNet) to discriminate the different intents carried by the identical textual information, in which the acoustic-textual representation was adapted by brain functional information through a cross-modal transformer. In the output module, a joint multi-task learning method was used to optimize both the primary task of intent recognition and the auxiliary task of emotion recognition of the speaker. To evaluate the model performance, we constructed a multimodal dataset CMSLIU that consists of acoustic, textual, and electroencephalograph (EEG) data of the same Chinese texts with varying intents. Experimental results on the self-constructed dataset indicate that the proposed model achieves state-of-the-art (SOTA) performance compared with other competing models. Ablation experiment results reveal that the model performance declines without information from EEG or audio modality, and also when the auxiliary task of emotion recognition is removed. All these results suggest the effectiveness of the proposed information fusion strategy in spoken language intent recognition and the brain–computer information fusion idea can also be extended to other similar fields.

Geographic variation in the acoustic signals of lesser treefrogs Dendropsophus minutus (Anura, Hylidae)

Biological sound archives are a major source to investigate geographic variation in animal acoustic signals and their evolutionary drivers. The acoustic signals of anuran species with wide distribution ranges often vary geographically as a result of isolation by distance and climate amongst other factors. We examined whether the acoustic structure of call notes would vary geographically in lesser treefrogs Dendropsophus minutus using recordings from bioacoustics repositories. We also tested whether climate (mean annual temperature and annual precipitation) drive geographic variation in those signals. The acoustic distance was unrelated to geographic distance, suggesting that isolation by distance solely cannot explain geographic variation in call structure. Overall, lesser treefrogs uttered call notes with lower frequencies and bandwidths in the west of their range. In addition, frogs produced shorter call notes in hotter, wetter sites and narrow-bandwidth notes in hotter environments. We suggest that frogs produce more calls (not measured here) of shorter durations to maximize transmission and minimize the metabolic costs of calling at high air temperatures. We also suggest that hotter environments favor the propagation of lower-bandwidth calls. This study reinforces the feasibility and power of citizen science and acoustic data repositories for understanding the evolution of anuran acoustic signals.

A Methodology for Speaker Diazaration System Based on LSTM and MFCC Coefficients

Research on Speaker Identification is always difficult. A speaker may be automatically identified using by comparing their voice sample with their previously recorded voice, the machine learning strategy has grown in favor in recent years. Convolutional neural networks (CNN) , deep neural networks (DNN) are some of the machine learning techniques that has employed recently. The article will discuss a successful speaker verification system based on the d-vector to construct a new approach based on speaker diarization. In particular, in this article, we use the concept of LSTM to cluster the speech segments using MFCC coefficients and identify the speakers in the diarization system. The proposed system will be evaluated using benchmark performance metrics, and a comparative study will be made with other models. The need to consider the LSTM neural network using acoustic data and linguistic dialect is considered. LSTM networks could produce reliable speaker segmentation outputs.

Assessing learners’ English public speaking anxiety with multimodal deep learning technologies

Public speaking anxiety (PSA) is a common phenomenon for language learners involving both psychological and physiological symptoms. Timely and effective PSA assessment can help diagnose learners’ speaking anxiety, offer learners feedback to alleviate their anxiety, and improve their public speaking competence. However, it is still a challenging issue to achieve accurate automated assessment of learners’ PSA due to the lack of large-scale and open-source multimodal datasets based on real classroom settings. This study collected the public speaking videos of English as a foreign language (EFL) learners in public speaking courses, and constructed a large-scale multimodal dataset named Speaking Anxiety in Real Classrooms (SARC) with three modalities of acoustic, visual, and textual data (including 1,158 manually-annotated speech videos and corresponding speech drafts of 382 participants). A multimodal deep learning model for automated assessment of learners’ speaking anxiety was proposed, and an online formative assessment platform was then developed to realize the automated assessment of PSA for classroom teaching. A pilot survey study involving 78 participants was then conducted to investigate learners’ acceptance of the platform. Experimental results verified the validity of the deep learning model and the consistency between automated assessment and teacher assessment. Learners’ acceptance data further indicated that collaboration between automated and human assessment provided them with the most satisfactory experience of using the platform to improve their English public speaking.

Cross-modal zero-sample diagnosis framework utilizing non-contact sensing data fusion

Gearboxes, fundamental components in the domains of manufacturing, transportation, and aerospace apparatus, are highly susceptible to impairments. The emerging technique of non-contact sensing measurement holds promise as a valuable tool for real-time monitoring and diagnosis of gearboxes, especially in challenging, dynamic environments. However, historical non-contact diagnostic techniques often face difficulties due to load-variation-induced interference, which leads to inconsistent diagnostic results. Moreover, acquiring sufficient non-contact sensing data for specific rare or severe gearbox faults can be exceedingly challenging and, at times, unattainable. This data scarcity limits the application of existing supervised and semi-supervised non-contact diagnostic methods. To address these limitations, this paper develops a novel diagnostic model, called Non-Contact Sensing Data Fusion Driven Cross-Modal Zero-sample Diagnostic Network. Specifically, we first develop a novel central fusion module to facilitate feature-level fusion at both global and local scales utilizing infrared thermal and acoustic data, thereby establishing a rich and comprehensive fault representation. Following that, we construct an end-to-end cross-modal zero-sample diagnostic architecture to mine and fuse complementary fault information. This approach enhances zero-sample diagnostic performance through efficient information utilization and cross-modal feature fusion. Afterward, we adopt a cross-modal self-enhancing fusion strategy during the training phase, which improves the information fusion performance of intra-class features from various modalities. This strategy effectively reduces misclassification risks and augments the robustness of the proposed zero-sample diagnostic network against interference from load variations. Comprehensive experimental results confirm that our proposed NCFZD sets the new state-of-the-art in multiple non-contact, zero-sample diagnostic scenarios, reaching HM scores of 86.06 %, 81.53 %, and 62.56 % respectively. By incorporating the information fusion theory, this model advances gearbox diagnostics in non-contact sensing and contributes to the broader field of information fusion theory by demonstrating its practical application in real-world problem-solving scenarios.

Merging experiment data and simulation data for parameter identification of shaft seal

A parameter identification method which merges experimentally monitored signals and physics-based simulation data is proposed, targeting the identification tasks in shaft seals which are challenging due to the high-dimensional parameter space. Features were extracted from the monitored acoustic emission signals following a proposed cross-timescale analysis routine, and the simulation data were augmented using Kriging surrogate model to obtain a dataset with stratified fidelity. Then, a transferable architecture of convolutional neural network modified for periodical data was proposed, with which part of the parameters trained by simulation data were reserved when training model using acoustic emission data acquired in experiments. Cross validation shows that transfer learning can effectively improve the performance, provided data augmentation and proper transfer mode. In conclusion, the study provides an effective parameter identification method which merges the simulation data which carry the physical knowledge and the experiment data which carry directly monitored results.

Rock physics-based anisotropy parameters estimation of Marcellus Shale using log data acquired in a vertical well

The elastic properties of shale rock exhibit anisotropy due to the presence of highly anisotropic clay minerals and their alignment. This anisotropy has a significant impact on geophysical and geomechanical responses. However, the elastic anisotropy of shale is often overlooked due to the difficulty in measuring sufficient parameters in the field, resulting in a gap between theoretical understanding and practical implementation. To address this issue, a rock physics-based approach was tested on log data obtained from a vertical well in the Marcellus Shale to estimate anisotropy parameters. The approach utilized a rock physics model based on the Sayers-den Boer method with model parameters optimized using measured stiffness parameters C33, C55, and C66. Anisotropy parameters δ and ε were then calculated using these optimized model parameters. The optimized model parameters and estimated anisotropy parameters are consistent with existing studies, indicating the correctness of the model. Additionally, the rock physics model was used to investigate the impact of gas saturation on the vertical VP/VS ratio and ε/γ ratio. It was found that gas saturation has a significant impact on these ratios. This carries important implications for acoustic log data interpretation and seismic characterization of shales.

Acoustic emission data based modelling of fracture of glassy polymer

Acoustic emission (AE) activity data resulting from the fracture processes of brittle materials is valuable real time information regarding the evolving state of damage in the material. Here, through a combined experimental and computational study we explore the possibility of utilising the statistical signatures of AE activity data for characterisation of disorder parameter in simulation of tensile fracture of epoxy based polymer. For simulations we use a square random spring network model with quasi-brittle spring behaviour and a normally distributed failure strain threshold. We show that the disorder characteristics while have marginal effect on the power law exponent of the avalanche size distribution, are strongly correlated with the waiting time interval between consecutive record breaking avalanches as well as the total number of records. This sensitivity to disorder is exploited in estimating the disorder parameter suitable for the experiments on tensile failure of epoxy based polymer. The disorder parameter is estimated assuming equivalence between the amplitude distribution of AE data and avalanche size distribution of the simulations. The chosen disorder parameter is shown to well reproduce the failure characteristics in terms of the peak load of the macroscopic response, the power-law behaviour with avalanche dominated fracture type as well as realistic fracture paths.

Audio-visual speech recognition based on regulated transformer and spatio-temporal fusion strategy for driver assistive systems

This article presents a research methodology for audio-visual speech recognition (AVSR) in driver assistive systems. These systems necessitate ongoing interaction with drivers while driving through voice control for safety reasons. The article introduces a novel audio-visual speech command recognition transformer (AVCRFormer) specifically designed for robust AVSR. We propose (i) a multimodal fusion strategy based on spatio-temporal fusion of audio and video feature matrices, (ii) a regulated transformer based on iterative model refinement module with multiple encoders, (iii) a classifier ensemble strategy based on multiple decoders. The spatio-temporal fusion strategy preserves contextual information of both modalities and achieves their synchronization. An iterative model refinement module can bridge the gap between acoustic and visual data by leveraging their impact on speech recognition accuracy. The proposed multi-prediction strategy demonstrates superior performance compared to traditional single-prediction strategy, showcasing the model’s adaptability across diverse audio-visual contexts. The transformer proposed has achieved the highest values of speech command recognition accuracy, reaching 98.87% and 98.81% on the RUSAVIC and LRW corpora, respectively. This research has significant implications for advancing human–computer interaction. The capabilities of AVCRFormer extend beyond AVSR, making it a valuable contribution to the intersection of audio-visual processing and artificial intelligence.

The Atacama Cosmology Telescope: Cosmology from Cross-correlations of unWISE Galaxies and ACT DR6 CMB Lensing

We present tomographic measurements of structure growth using cross-correlations of Atacama Cosmology Telescope (ACT) DR6 and Planck cosmic microwave background (CMB) lensing maps with the unWISE Blue and Green galaxy samples, which span the redshift ranges 0.2 ≲ z ≲ 1.1 and 0.3 ≲ z ≲ 1.8, respectively. We improve on prior unWISE cross-correlations not just by making use of the new, high-precision ACT DR6 lensing maps, but also by including additional spectroscopic data for redshift calibration and by analyzing our measurements with a more flexible theoretical model. We determine the amplitude of matter fluctuations at low redshifts (z ≃ 0.2–1.6), finding S8≡σ8(Ωm/0.3)0.5=0.813±0.021 using the ACT cross-correlation alone and S 8 = 0.810 ± 0.015 with a combination of Planck and ACT cross-correlations; these measurements are fully consistent with the predictions from primary CMB measurements assuming standard structure growth. The addition of baryon acoustic oscillation data breaks the degeneracy between σ 8 and Ω m , allowing us to measure σ 8 = 0.813 ± 0.020 from the cross-correlation of unWISE with ACT and σ 8 = 0.813 ± 0.015 from the combination of cross-correlations with ACT and Planck. These results also agree with the expectations from primary CMB extrapolations in ΛCDM cosmology; the consistency of σ 8 derived from our two redshift samples at z ∼ 0.6 and 1.1 provides a further check of our cosmological model. Our results suggest that structure formation on linear scales is well described by ΛCDM even down to low redshifts z ≲ 1.

Broadband backscattering by Atlantic herring (Clupea harengus L.) differs when measured from a research vessel vs. a silent uncrewed surface vehicle

Abstract Broadband frequency-modulated signals are believed to improve acoustic spectral-based target classification. Efficient use of uncrewed surface vehicles (USV) for fisheries science applications, with no possibility for biological sampling, is believed to be facilitated by use of broadband signals with methods for target classification. If the broadband frequency response used to train automated target classifiers are obtained from conventional research vessels (RVs), due to potential vessel avoidance, the swimming angle distribution may be different than for USVs. This may have consequences for target classification if the model is trained with RV data. The aim of this study was to assess whether the frequency response differs between platforms due to avoidance. Broadband acoustic data were collected with a conventional RV and a small USV. The broadband frequency response of Norwegian spring spawning herring obtained with the USV and RV was found to be significantly different for shallow herring layers in the 200 kHz band. This indicates that broadband frequency response has potential as a tool for real-time monitoring of behaviour reactions to vessels and to provide insight into fish behaviour in general. When using broadband frequency response for target classification, the potential platform-dependent broadband frequency response should be considered.

In situ process monitoring of multi-layer deposition in wire arc additive manufacturing (WAAM) process with acoustic data analysis and machine learning

In situ process monitoring of multi-layer deposition in wire arc additive manufacturing (WAAM) process with acoustic data analysis and machine learning

Inversion of shear wave slowness and attenuation from dipole acoustic logging data based on an equivalent tool theory

The presence of a sonic logging tool affects the propagation characteristics of dipole-flexural wave in a fluid-filled borehole. As a result, the tool effect should be considered during the processing and inversion of the dipole-flexural wave for acoustic properties of shear wave. We show that the presence of the tool not only changes the slowness dispersion of the flexural wave but also results in a shift of its frequency-dependent attenuation toward lower frequencies. Equivalent tool theory (ETT) can realistically model the effect of an acoustic tool on guided wave propagation. With the ETT framework, we develop a unified workflow for estimating both the shear wave slowness and attenuation from dipole array acoustic waveforms. The workflow is an extension of the model-based dispersive processing of dipole logging data. The proposed workflow involves two inversion procedures. By matching the actual dispersion data with the theoretical dispersion predicted by the ETT, we can invert the shear wave slowness and tool parameters. With the estimated shear wave slowness and other borehole parameters, we further calculate the partition coefficients of flexural waves using the ETT. Based on the concept of energy partitioning, we formulate a linear optimization problem and invert for the shear wave attenuation from the frequency-dependent dipole attenuation. We use synthetic and field data to demonstrate the effectiveness and applicability of the developed method. The results show that it is important to incorporate the tool effect when processing dipole acoustic logging data for shear wave slowness and attenuation, especially in a fast formation.

Antarctic sonobuoy surveys for blue whales from 2006-2021 reveal contemporary distribution, changes over time, and paths to further our understanding of their distribution and biology

Seven passive acoustic surveys for marine mammal sounds were conducted by deploying sonobuoys along ship tracks during Antarctic voyages spanning years 2006-2021. These surveys included nearly 330° of longitude throughout Antarctic (south of 60°S) and sub-Antarctic (between 50-60°S) latitudes. Here, we summarise the presence of calls from critically endangered Antarctic blue whales (Balaenoptera musculus intermedia) detected on all seven of these surveys. We describe and compare the spatial distribution of detections of three different types of Antarctic blue whale calls: unit-A, Z-calls, and D-calls. Three sets of voyages partially overlapped spatially but in different years, providing three regions (Indian Sector, Dumont d’Urville Sea, Ross Sea) to investigate differences over time for these three different call types. The proportion of sonobuoys with calls present was significantly higher in the more recent years for seven of the 15 combinations of years, regions, and call type. The proportion of sonobuoys with calls present was significantly lower only for one of the 15 combinations (unit A in the Ross Sea between 2015 vs 2017), and not significantly different for the remaining seven pairwise comparisons. We discuss possible explanations for these observations including: differences in probability of detection, whale behaviour, whale distribution, and abundance. These explanations are not mutually exclusive and cannot yet be resolved without application of complex analytical methods and collection of additional data. Lastly, we discuss future work that could help clarify the contributions of each of these potential drivers of acoustic detection. We propose continued acoustic data collection, application of new analytical methods, and collection of other synergistic data from Antarctic blue whales on their feeding grounds as a basis for future work on this species. This could provide a cost effective and holistic means of monitoring their status after the effects of 20th century industrial whaling, as well as their responses to natural and anthropogenic changes to their main prey, Antarctic krill, and a changing climate.

Cosmological prediction of the CSST Ultra Deep Field Type Ia supernova photometric survey

ABSTRACT Type Ia supernova (SN Ia) as a standard candle is an ideal tool to measure cosmic distance and expansion history of the Universe. Here, we investigate the SN Ia photometric measurement in the China Space Station Telescope Ultra Deep Field (CSST-UDF) survey, and study the constraint power on the cosmological parameters, such as the equation of state of dark energy. The CSST-UDF survey is expected to cover a 9 deg2 sky area in 2 yr with 250 s × 60 exposures for each band. The magnitude limit can reach i ≃ 26 AB mag for 5σ point source detection with a single exposure. We generate light-curve mock data for SNe Ia and different types of core-collapse SNe (CCSNe). sncosmo is chosen as the framework by utilizing the salt3 model to simulate SN Ia data. After selecting high-quality data and fitting the light curves, we derive the light-curve parameters and identify CCSNe as contamination, resulting in ∼2200 SNe with an $\sim\!\! 7{{\ \rm per\, cent}}$ CCSN contamination rate. We adopt a calibration method similar to Chauvenet’s criterion, and apply it to the distance modulus data to further reduce the contamination. We find that this method is effective and can suppress the contamination fraction to $\sim\!\! 3.5{{\ \rm per\, cent}}$ with 2012 SNe Ia and 73 CCSNe. In the cosmological fitting stage, we did not distinguish between SNe Ia and CCSNe. We find that the constraint accuracies on ΩM, ΩΛ, and w are about two times better than the current SN surveys, and they could be further improved by a factor of ∼1.4 if including the baryon acoustic oscillation data from the CSST spectroscopic wide-field galaxy survey.

Spatiotemporal patterns in habitat use of natal and non-natal adult Atlantic sturgeon in two spawning rivers

BackgroundMonitoring movement across an organism’s ontogeny is often challenging, particularly for long-lived or wide-ranging species. When empirical data are unavailable, general knowledge about species’ ecology may be used to make assumptions about habitat use across space or time. However, inferences about habitat use based on population-level ecology may overlook important eco-evolutionary contributions from individuals with heterogenous ethologies and could diminish the efficacy of conservation and management.MethodsWe analyzed over a decade of acoustic telemetry data to understand individual differences in habitat use of federally endangered adult Atlantic sturgeon (Acipenser o. oxyrinchus) in the Delaware and Hudson rivers during spawning season. In particular, we sought to understand whether sex or natal origin could predict patterns in habitat use, as there is a long-held assumption that adult Atlantic sturgeon seldom stray into non-natal rivers.ResultsIn both rivers, migration timing, spawning habitat occupancy, and maximum upstream migration distance were similar between natal and non-natal individuals. While non-natal individuals represented only 13% of fish detected in the Hudson River, nearly half of all tagged fish detected in the Delaware River were non-natal and generally occupied freshwater habitats longer than natal individuals. In both systems males had more heterogenous patterns of habitat use and longer duration of occupancy than did females.ConclusionsThis study demonstrates the importance of non-natal rivers for fulfilling ontogenetic habitat requirements in Atlantic sturgeon. Our results may also highlight an opportunity to improve conservation and management by extending habitat designations to account for more heterogenous patterns in individual habitat use in non-natal freshwater environments.

Formational stages of natural fractures revealed by U-Pb dating and C-O-Sr-Nd isotopes of dolomites in the Ediacaran Dengying Formation, Sichuan Basin, southwest China

Deep core (>4.9 km) from Ediacaran Deng IV Member algal dolomites in the Gaoshiti-Moxi block in the Sichuan Basin, southwest China, reveals multiple generations of dolomite-lined and dolomite-filled opening-mode fractures. Three progressive stages of fracture formation are marked by crosscutting relations visible in the core, by acoustic emission experiments revealing evidence of past stress directions, and by fluid inclusions, U-Pb ages, C-O-Sr-Nd isotope patterns, and rare earth element data for dolomite cements in fractures, which document ages and differing thermal conditions and fluid compositions during fracture. In calcite-filled fractures, U-Pb ages and carbon and oxygen isotope signatures vary greatly, indicating that fractures developed with intensified tectonic activity marked by regional structures and with enhanced diagenetic alteration. In stage I, WNW-striking opening-mode fractures formed that contain dolomite deposits precipitated from basinal fluids between ca. 549 Ma and ca. 532 Ma. At this time, the Sichuan Basin experienced Xingkai taphrogenesis (rifting) from the late Neoproterozoic to early Cambrian. The central Sichuan paleo-uplift was undergoing ENE extension, and preexisting ESE- and nearly E-W−striking faults were oblique to the ENE principal stress orientation. This led to a local stress field favoring dextral shear near fault zones accommodated by the fractures. In stage II, ENE-striking fractures that are younger based on crosscutting relations contain dolomite deposits from basinal fluids with ages from ca. 423 Ma to ca. 411 Ma. Contemporaneous with Xuefeng thrusting, the central Sichuan paleo-uplift was in a NNE-striking transpressional stress field, which likely further generated ENE-striking fractures. In stage III, nearly N-S−striking fractures formed in the Gaoshiti-Moxi block. High-temperature fluids related to the Permian Emeishan large igneous province invaded these fractures from ca. 260 Ma to ca. 256 Ma. At this time, the Sichuan Basin was uplifted under the influence of the Emei taphrogenesis in the late Permian, and the central Sichuan paleo-uplift was subjected to E-W−striking extension. In fractures in these carbonate rocks, micro-computed tomography imaging reveals that macropores (>10 μm, 12.1%−21.8%) and small pores (2−10 μm, 76.6%−86.1%) dominate the dolomite mineral deposits, and that there are few (1.6%−1.8%) micropores or nanopores (<2 μm). Medium-sized throats (1−3 μm) are the main connecting channels. We infer that fractures served as conduits for fluid migration, leading to the dissolution of matrix pores adjacent to the fractures. This secondary porosity not only enhances reservoir storage capacity but also augments reservoir connectivity. Our study shows that in situ U-Pb dating and full-diameter rock acoustic emission data can effectively constrain the timing of fractures. By integrating this information with regional tectonic sequences and fracture diagenetic sequences from combined relative timing, geochemistry, and rock mechanics evidence, we clarify the factors controlling fracture formation.