Data aggregation is essential in near-ground long chain sensing networks, as it ensures data freshness and stability while extending communication range through path planning and traffic scheduling. However, the dynamic and fragile nature of long-chain networks under near-surface interference—particularly the presence of time-varying network links—poses significant challenges, for which no effective aggregation solution currently exists. This paper focuses on a representative ground-based distributed seismic node long-chain network and introduces a Weight Agnostic Structure-Free (WASF) data aggregation method based on artificial neural networks. WASF regulates packet forwarding paths and waiting times via activation functions and employs shared connection weights to identify the globally optimal aggregation node, thereby enabling efficient data aggregation. Simulation results demonstrate that, compared with state-of-the-art aggregation methods, WASF reduces end-to-end latency and packet loss rate by 11.8% and 9.4%, respectively. Field deployment on GEIWSR-III seismic nodes further confirmed its effectiveness, yielding 84.3% lower energy consumption, 41% reduced latency, and 20.2% fewer packet losses. By enabling structure-free aggregation, WASF provides a reliable reference framework for efficient, robust, and scalable communication in long-chain IoT networks, such as tunnels, pipe galleries, rivers, and railways.

The quality of seismic images is often affected by the limitation of acquisition conditions and the interference of noises, which causes the low resolution of seismic images and misleads the following geological interpretation. Although the super-resolution method for seismic images based on convolutional neural network (CNN) has behaved well, the quality of weak events especially deep events is still need to be improved, due to CNN is limited by the receptive fields, which results in weaker ability to perceive relationships among pixels far apart. In this letter, we solve this problem by designing a combination network of CNN and transformer (CNCT). CNCT consists of three parts, edge feature fusion block (EFB), deep feature mining block (DMB), and feature enhancement block (FEB). The EFB aims to fuse the input low-resolution (LR) image and the corresponding edges obtained by the Sobel algorithm and performs preliminary shallow feature extraction. DMB mines deeper features by stacking residual blocks, and each residual block makes full use of its excellent perception of global and local information by combining transformer and CNN. Finally, the FEB uses subpixel convolution for upsampling to expand the size of feature maps. The experimental results on synthetic data and field data show that CNCT not only behaves better on perception effect and texture details than that of other deep learning (DL) methods but also can suppress noise and improve the dominant frequency.

Seismic exploration is a crucial method for studying underground geological structures and oil/gas resources. However, the presence of various noise sources during seismic wave propagation hinders accurate interpretation and imaging. To address this challenge, effective denoising methods are essential. In recent years, deep learning, particularly Convolutional Neural Networks (CNNs), has shown promise in seismic data processing. Nevertheless, CNNs have limitations in capturing long-range dependencies and global coherence. As an alternative, we propose a Transformer-based model called Seismic Data Denoising Transformer (SDT) for seismic signal processing. By leveraging self-attention mechanisms, the SDT model overcomes the limitations of CNNs and effectively captures long-range features for seismic signal reconstruction. We also introduce a novel self-supervised pretraining strategy using a large-scale dataset to further enhance performance. Experimental results demonstrate the advantages of SDT in complex seismic noise attenuation and preserving weak signal amplitudes. The proposed method exhibits promising potential for real-world seismic data applications.

Many deep learning methods have been proposed to classify moving targets from seismic signals in recent years. However, the existing deep models are all designed based on the “end-cloud” framework, in which real-time data processing is difficult because of communication delays. To address this problem and achieve on-site target classification, we propose a novel edge intelligence-oriented method, named compressed sensing-edge convolutional neural network (CS-ECNN). In this method, the acquired seismic signals are first mapped onto a compressed domain using CS. This operation reduces data dimensions, while being able to retain the vast majority of valuable seismic features. Following that, a convolutional neural network is employed to extract implicit features directly from the compressed seismic measurements and then classify the feature vectors. To evaluate the proposed method, the seismic data recorded in DARPA’s SensIT project are used as a case study. The experimental results demonstrate that the proposed model is edge-matched, and it achieves comparable classification accuracy to the state-of-the-art cloud-based models with only 1/10 computation time.

Due to the strong nonstationary characteristics of seismic signals, energy criteria-based methods are not robust for detecting moving targets, especially in data with low SNRs. To address this problem, we propose a new method for detecting ground moving target based on fractal dimension (FD) theory named FD-based support vector machine (FD-SVM). In this method, seismic signals are first measured by fractals, which can effectively extract seismic nonlinear features. These fractal features are then fed into an SVM to distinguish moving targets from noise. Two data sets are used to evaluate the proposed method. One is a set of seismic signals induced by wheeled and tracked vehicles. The other is a set of seismic signals generated by human footsteps. Experimental results demonstrate that the proposed FD-SVM algorithm achieves promising results on both data sets. Compared with the benchmark methods, the FD-SVM algorithm achieves a better precision rate, recall rate, and F1 score.

Moving target recognition is a critical task for a variety of applications, ranging from environmental monitoring to regional security protection. Recently, many deep learning (DL) methods have been proposed to recognize the seismic features of moving targets. However, the established DL algorithms are mainly challenged by time-consuming feature extraction and lack of robustness. In this article, a novel moving target recognition method [Compression Observation-Seismic DL (CO-SDL)] is proposed to solve the above two problems simultaneously. CO-SDL first uses a measurement matrix to project the seismic signal onto a compressed domain and obtain compressed seismic measurements. This operation removes redundant data while retaining valuable seismic information and suppressing noise energy. Following that, CO-SDL efficiently and stably extracts deep nonlinear features from compressed seismic measurements and then accurately classifies the feature vectors. To evaluate the proposed method, a comprehensive seismic dataset is developed. This dataset covers six types of common moving targets, and the SNR ranges of all signal types are greater than 15 dB. The proposed method and the benchmark methods are tested on this dataset. Experimental results prove that the presented CO-SDL method is ten times faster than the state-of-the-art methods with comparable accuracy. Furthermore, the CO-SDL method shows the strongest robustness.

In modern seismic data acquisition, real-time data collection is a challenging task due to bandwidth limitations in wireless communications. In this letter, we propose a novel compressive data gathering scheme using generative adversarial networks, named GAN-CDG, to improve the efficiency of data gathering. Instead of collecting the originally acquired data, GAN-CDG gathers data projections in wireless geophone networks. Data compression and load-balanced relay transmission are utilized during the projection process. To speed up the formation of projections, the shortest path routing tree (SPRT) is constructed, which achieves the minimum end-to-end time delay. The sparse domain of seismic signals and its reconstruction mapping are learned by sparsity-constrained adversarial networks. The testing results demonstrate that projections with high compression ratios (e.g., 16) are gathered efficiently with the SPRT. Then, original seismic signals can be reconstructed accurately (over 30 dB) from the projections using the adversarial model, which outperforms the state-of-the-art method.