Search Results (93)

Accurate picking of microseismic P-wave arrival times is essential for the localization and monitoring of mining-induced seismic events. Conventional Short-Term Average/Long-Term Average (STA/LTA) detectors, while computationally efficient, are highly susceptible to noise interference. Conversely, deep learning approaches exhibit superior noise robustness but often involve substantial computational redundancy and compromised real-time performance. To address these limitations, we propose a novel two-stage picking framework that integrates STA/LTA with a lightweight U-Net, enabling rapid preliminary detection followed by fine-grained refinement. In the first stage, STA/LTA rapidly scans continuous waveforms to identify candidate windows potentially containing P-wave arrivals. In the second stage, a lightweight U-Net performs sample-level regression within each candidate window to refine arrival-time estimates with high precision. This coarse-to-fine paradigm effectively balances computational efficiency and picking accuracy. Experimental validation on 500 Hz microseismic data acquired from a coal mine in Gansu Province demonstrates that the proposed method achieves a hit rate of 63.21% within a tolerance window of ±0.01 s. This represents performance improvements of 25.42% and 40.47% over convolutional neural network (CNN) and STA/LTA methods, respectively, while reducing the mean absolute error to 0.0130 s. Furthermore, the model exhibits consistent performance on independent test sets, confirming its generalization capability and noise robustness. By combining the computational efficiency of STA/LTA with the representational power of deep learning, the proposed approach demonstrates significant potential for real-time industrial deployment. Full article

Seismic signal denoising stands as a vital process that enables precise seismic data analysis because noise interference blocks the detection of weak but valuable seismic signals. The current traditional denoising methods together with deep learning-based data-driven approaches encounter difficulties when they need to remove noise from seismic signals while keeping their fundamental structural elements, especially under conditions of low signal-to-noise ratios. In this study, we propose a novel denoising framework that integrates a physics-guided neural network with adaptive time–frequency decomposition, referred to as TF-PhysNet. The system breaks down broadband seismic data into separate frequency bands. Scientists can use these to study specific noise patterns that appear at various frequency points. The system uses a shared convolutional neural network-long short-term memory architecture to remove noise from each sub-band, which helps it learn both short-term waveform patterns and extended temporal relationships. The system uses physics-guided restrictions to eliminate false signal variations, which appear during the signal recovery process. The experimental findings from synthetic and real seismic data sets show that TF-PhysNet delivers better results than standard denoising techniques and deep learning-based methods for signal-to-noise ratio improvement and correlation coefficient enhancement. Full article

During seismic exploration, seismic data is collected to determine underground structural features and hydrocarbon-bearing stratum interfaces. The seismic data inversion process is highly complex and susceptible to interference from noise, which may lead to significant errors in inversion and affect comprehensive stratigraphic interpretation. The application of machine learning to seismic data interpretation and denoising remains technically challenging and yields suboptimal results. Micromotion exploration technology employs conventional “noise” as its signal source, utilizing widely occurring regular noise. On the basis of the theory of stationary random processes, it extracts frequency curves of surface waves from micromotion signals and performs inversion to obtain underground shear wave velocity profiles. Owing to its simplicity, cost-effectiveness, and environmental friendliness, micromotion exploration has notable advantages in structural exploration and hydrocarbon discovery. The micromotion detection results of an experimental area can quickly reflect the location of fault zones. When combined with electrical logging, this method is effective for shallow gas reservoir structure detection. Full article

Urban environments face heightened seismic risks due to dense infrastructure and population concentration. Traditional seismic methods often face significant practical limitations in cities due to space constraints, traffic disruption, and acoustic noise, necessitating reliable alternative geophysical approaches for fault screening. This study evaluates the efficacy and practical utility of the opposing-coils transient electromagnetic method (OCTEM) as an effective alternative to conventional seismic techniques for detecting shallow-fault-like resistivity signatures under complex urban electromagnetic noise. By employing dual coaxial coils with opposing currents, the OCTEM suppresses primary-field interference, enabling high-resolution imaging of subsurface structures at depths of 0–200 m. A case study in Tiancheng Chengyuan, Cangzhou City, China, demonstrates the OCTEM’s capability to reliably delineate stratigraphic interfaces and resistivity anomalies under challenging electromagnetic background conditions. Field data exhibited a mean square relative error of 4.01%, validating its data quality and measurement stability. The survey successfully identified stratigraphic continuity and localized heterogeneity features within the investigation zone. These results establish the OCTEM as a robust and efficient tool for urban fault screening, particularly in environments where traditional high-resolution seismic methods are impractical or economically unfeasible. Full article

Land seismic data are always contaminated by surface waves, which demonstrate strong energy, low velocity, and long vibrations. Such noises often mask deep effective reflections, seriously reducing the data’s signal-to-noise ratio while limiting the imaging accuracy of complex deep structures and the efficiency of hydrocarbon reservoir identification. To address this critical technical bottleneck, this paper proposes a surface wave joint reconstruction method based on stationary phase analysis, combining the cross-correlation seismic interferometry method with the convolutional seismic interferometry method. This approach integrates cross-correlation and convolutional seismic interferometry techniques to achieve coordinated reconstruction of surface waves in both shot and receiver domains while introducing adaptive weight factors to optimize the reconstruction process and reduce interference from erroneous data. As a purely data-driven framework, this method does not rely on underground medium velocity models, achieving efficient noise reduction by adaptively removing reconstructed surface waves through multi-channel matched filtering. Application validation with field seismic data from the piedmont regions of western China demonstrates that this method effectively suppresses high-energy surface waves, significantly restores effective signals, improves the signal-to-noise ratio of seismic data, and greatly enhances the clarity of coherent events in stacked profiles. This study provides a reliable technical approach for noise reduction in seismic data under complex near-surface conditions, particularly suitable for hydrocarbon exploration in regions with developed scattering sources such as mountainous areas in western China. It holds significant practical application value and broad dissemination potential for advancing deep hydrocarbon resource exploration and improving the quality of complex structural imaging. Full article

Pre-seismic anomalies in solid tidal factors can reveal crustal stress accumulation and predict seismic risk; such disturbance signals associated with earthquake incubation are extremely subtle and easily obscured by environmental noise, instrument errors, and other interference factors, placing heightened demands on the precision of gravity data acquisition and the capability to detect and isolate solid tidal signals effectively. In this paper, we propose a novel method for determining time-varying solid tidal factors based on the normal time–frequency transform (NTFT) theory, an approach allowing us to unbiasedly determine the instantaneous amplitude, frequency, and phase of time-varying signals, while mitigating the influence of edge effects to a certain extent. In the study outlined in this paper, we first design simulation experiments to validate the effectiveness of the new method. Subsequently, utilising high-precision superconducting gravimeter observation data, the proposed method is applied to the detection of pre-seismic disturbances preceding the 2004 Sumatra megathrust earthquake. Our results demonstrate that, compared to traditional harmonic analysis methods, this novel approach more accurately filters out interference signals, effectively captures the faint pre-seismic perturbations of solid tides, and significantly enhances the timeliness of pre-seismic disturbance detection, thus providing more reliable technical support for earthquake precursor monitoring. Full article

This paper describes the results of monitoring wave processes in the geospheres using laser interference instruments, a weather station, a seismometer, and other measuring devices. Processing in situ data revealed general patterns in seismic events and variations in the hydrosphere and atmospheric pressure. Laser strainmeters and a seismometer were used to identify natural and anthropogenic seismic activity. A laser nanobarograph and strainmeters allowed us to detect baro-deformation interactions. Processing data from supersensitive detectors of hydrosphere pressure variations, a tide gauge, and temperature sensors revealed regional features of marine wave processes. Full article

The deployment of wireless seismic nodal systems necessitates the efficient identification of optimal locations for sensor installation, considering factors such as ground stability and the absence of interference. Semantic segmentation of satellite imagery has advanced significantly, and its application to this specific task remains unexplored. This work presents a baseline empirical evaluation of the U-Net architecture for the semantic segmentation of surfaces applicable for seismic sensor installation. We utilize a novel dataset of Sentinel-2 multispectral images, specifically labeled for this purpose. The study investigates the impact of pretrained encoders (EfficientNetB2, Cross-Stage Partial Darknet53—CSPDarknet53, and Multi-Axis Vision Transformer—MAxViT), different combinations of Sentinel-2 spectral bands (Red, Green, Blue (RGB), RGB+Near Infrared (NIR), 10-bands with 10 and 20 m/pix spatial resolution, full 13-band), and a technique for improving small object segmentation by modifying the input convolutional layer stride. Experimental results demonstrate that the CSPDarknet53 encoder generally outperforms the others (IoU = 0.534, Precision = 0.716, Recall = 0.635). The combination of RGB and Near-Infrared bands (10 m/pixel resolution) yielded the most robust performance across most configurations. Reducing the input stride from 2 to 1 proved beneficial for segmenting small linear objects like roads. The findings establish a baseline for this novel task and provide practical insights for optimizing deep learning models in the context of automated seismic nodal network installation planning. Full article

Many fluid-injection sequences display burst-like seismicity with approximate power-law event-size distributions whose exponents drift between catalogs. Classical percolation models instead predict fixed, dimension-dependent exponents and do not specify which geometric mechanisms could underlie such b-value variability. We address this gap using two loopless invasion percolation variants—the constrained Leath invasion percolation (CLIP) and avalanche invasion percolation (AIP) models—to generate synthetic burst catalogs and quantify how burst geometry modifies size–frequency statistics. For each model we measure burst-size distributions and an interference fraction, defined as the proportion of attempted growth steps that terminate on previously activated bonds. Single-burst clusters recover the Fisher exponent of classical percolation, whereas multi-burst sequences show systematic, dimension-dependent drift of the effective exponent with a burst number that is strongly correlated with the interference fraction. CLIP and AIP are indistinguishable under these diagnostics, indicating that interference-driven exponent drift is a generic feature of burst growth rather than a model-specific artifact. Mapping the size-distribution exponent to an equivalent Gutenberg–Richter b-value shows that increasing interference suppresses large bursts and produces b value ranges comparable to those reported for injection-induced seismicity, supporting the interpretation of interference as a geometric proxy for mechanical inhibition that limits the growth of large events in real fracture networks. Full article

With the increasing demand for safety monitoring of offshore engineering structures, traditional single-modality sensing and centralized data processing models face challenges such as insufficient real-time performance and weak anti-interference abilities in complex marine environments. This research proposes an intelligent monitoring system based on multimodal sensor fusion and edge computing, aiming to achieve high-precision real-time diagnosis of offshore structure damage. The research plans to construct multimodal sensors through sensors such as stress change sensors, vibration sensors, ultrasonic sensors, and fiber Bragg grating sensors. A distributed wireless sensor network will be adopted to realize the transmission of sensor data, reduce the complexity of wiring, and meet the requirements of high humidity and strong corrosion in the marine environment. At the edge computing layer, lightweight deep learning models (such as multi-branch Transformer) and D-S evidence theory fusion algorithms will be deployed to achieve real-time feature extraction of multi-source data and damage feature fusion, supporting the intelligent identification of typical damages such as cracks, corrosion, and deformation. Experiments will simulate the coupled working conditions of wave impact, seismic load, and corrosion to verify the real-time performance and accuracy of the system. The expected results can provide a low-latency and highly robust edge-intelligent solution for the health monitoring of offshore engineering structures and promote the deep integration of sensor networks and artificial intelligence in Industry 4.0 scenarios. Full article

The prediction of seismic intensity in an accurate and timely manner is needed to provide scientific guidance for disaster relief. Traditional seismic intensity prediction methods rely on seismograph equipment, which is limited by slow response times and high equipment costs. In this study, we introduce a low-computational-cost transformer-based (LCCTV) visual tracking method to predict seismic intensity in surveillance videos. To this end, an earthquake video dataset is proposed. It is captured in the laboratory environment, where the seismic level is obtained through seismic station simulation. With the proposed dataset, a low-computational-cost transformer-based visual tracking method is first proposed to estimate the movement trajectory of the calibration board target in videos in real time. In order to further improve the recognition accuracy, we then utilize a Butterworth filter to smooth the generated movement trajectory so as to remove low-frequency interference signals. Finally, the seismic intensity is predicted based on the velocity and acceleration derived from the smoothed movement trajectory. Experimental results demonstrated that the LCCTV outperformed other state-of-the-art approaches. The findings confirm that the proposed LCCTV can serve as a low-cost, scalable solution for seismic intensity analysis. Full article

The traditional magnetoelectric geophone is widely used in the microseismic monitoring of coal mines. However, its measurement capability in the low-frequency range is insufficient and cannot fully meet the monitoring requirements of underground coal mines, which extend as low as 0.1 Hz. This paper proposes a signal conditioning (SC) circuit based on the extended filtering method to improve the low-frequency response capability of the geophone. Through simulation and experimental tests, it is verified that the designed SC circuit can reduce the cut-off frequency of the EST-4.5C geophone from 4.5 Hz to 0.16 Hz. Meanwhile, the noise introduced by this SC circuit is relatively low thanks to its simple and easy-to-implement structural model. The test results also indicate that it provides a strong ability to resist noise interference for the geophone, which is valuable under complex working conditions. Overall, this circuit offers a feasible option for enhancing the capability of the seismic geophones used in coal mines to detect low-frequency vibration signals. Full article

In marine seismic surveys, the indistinguishability of subsurface boundaries caused by the superimposition of the acoustic signals reflected from it, particularly at specific frequency ranges characterized by strong spectral interference, reduces the resolution of the seismic record. We processed sub-bottom profiler data, acquired using a Bubble Pulser (nominal central frequency: ~400 Hz; effective bandwidth extending to ~1 kHz), (i) by extracting continuous wavelet transform (CWT) coefficients at the dominant energy scale to form the envelope and (ii) by applying Hilbert-based instantaneous frequency analysis to characterize medium-dependent spectral shifts. Envelope accuracy was benchmarked against four conventional filters using the sum of squared error (SSE) relative to a cubic-spline reference. CWT yielded the lowest SSE, outperforming low-pass 1 kHz and band-pass 400–1000 Hz; band-pass 400–650 Hz and low-pass 650 Hz were the least effective. Instantaneous-frequency trends differentiated rock, sand, and mud layers. Thus, compared to fixed-band filters, the scale-adaptive CWT envelope replicates raw energy more faithfully, while frequency attributes improve sediment classification. Low-pass filtering at 1000 Hz provides a more accurate representation of energy distribution than does bandpass filtering, particularly in the 400–650 Hz range. The integrated workflow—a robust, parameter-light alternative for high-resolution stratigraphic interpretation—enhances offshore engineering safety. Full article

The structure and geomorphology of channel systems play a critical role in interpreting sedimentary processes and characterizing subsurface reservoir capacity. This study presents an innovative 3D DS-TransUnet model for seismic channel interpretation. The model incorporates a multi-scale Swin Transformer architecture capable of processing 3D data in both the encoder and decoder, and integrates a feature fusion module into the skip connections to effectively combine shallow detail features with deep semantic features, thereby enhancing the detectability of weak reflection signals. This design not only enables the network to capture global dependencies but also preserves fine-grained local details, allowing for more robust feature learning under complex geological conditions. In addition, a complete synthetic data generation workflow is proposed, through which 300 pairs of high-quality synthetic data were constructed for model training. During training, the proposed model achieved a significantly faster convergence speed compared with other selected models. Experimental results on both synthetic and field seismic datasets demonstrate that the proposed method yields substantial improvements in channel boundary delineation accuracy and interference suppression, providing an efficient and reliable approach for intelligent channel recognition. Full article

This review systematically examines intelligent event perception in distributed acoustic sensing (DAS) systems. Beginning with the elucidation of the DAS principles, system architectures, and core performance metrics, it establishes a comprehensive theoretical framework for evaluation. This study subsequently delineates methodological innovations in both traditional machine learning and deep learning approaches for event perception, accompanied by performance optimization strategies. Particular emphasis was placed on advances in hybrid architectures and intelligent sensing strategies that achieve an optimal balance between computational efficiency and detection accuracy. Representative applications spanning traffic monitoring, perimeter security, infrastructure inspection, and seismic early warning systems demonstrate the cross-domain adaptability of the technology. Finally, this review addresses critical challenges, including data scarcity and environmental noise interference, while outlining future research directions. This work provides a systematic reference for advancing both the theoretical and applied aspects of DAS technology, while highlighting its transformative potential in the development of smart cities. Full article