High-density seismic array method has attracted enormous interest in geophysical methods, and it is an advanced seismic observation technology which is of great significance in oil and gas resource detection and underground space exploration. However, designing a portable sensor node that prolongs the lifetime and equips wireless monitoring system to improve construction efficiency has been still a challenge for the high-density seismic array method. In this paper, a portable energy-efficient wireless sensor node and a hybrid communication system that content themselves with complex terrain, large-scale and reliable transmission are proposed to tackle the restrictions of high-density seismic survey. A Wi-Fi communication system based on the star network structure at the core network layer and a ZigBee communication system based on wireless multi-hop structure in the extended network layer form the hybrid communication system. In order to balance the energy of nodes in the extended network and prolong the network lifetime, this paper proposes a multi-hop variable weight routing method (MVWRM). The method selects the cluster-head node and the multi-hop route by changing the weight of the residual energy of the node and the weight of the distance at different periods. Then, a series of simulations and experiments are carried out for verification. The experiments show that the proposed sensor node suggest good performance in equivalent noise level (0.8μV@500Hz, PGA = 1), total harmonic distortion (124dB). Simulation results and performance analysis validate that the effectiveness of the proposed MVWRM can extend about 10% of the node lifetime.

The microtremor survey method (MSM) has the potential to be an important geophysical method for identifying the strata velocity structure and detecting the buried fault structures. However, the existing microtremor exploration equipment has been unable to satisfy the requirements of the MSM, which suffers from low data accuracy, long measurement time, and blind acquisition. In this study, we combined a 2 Hz moving coil geophone with advanced acquisition systems to develop a new integrated energy-efficient wireless sensor node for microtremor exploration. A high-precision AD chip and noise matching technology are used to develop a low-noise design for the sensor node. Dynamic frequency selection technology (DFS) and dynamic power management technology (DPM) are used to design an energy-efficient mode. The data quality monitoring system solves the closed technical flaws between the acquisition systems and the control center via 4G wireless monitoring technologies. According to the results of a series of in situ tests and field measurements, the noise level of the system was 0.7 μV@500 Hz with 0 dB attenuation and 220 mW power consumption of the system in the autonomous data acquisition mode. Therefore, it provides substantial support for the effective data acquisition over long measurement durations in microtremor exploration processes. The applicability of the system is evaluated using field data, according to which the integrated energy-efficient wireless sensor node is convenient and effective for MSM.

Large numbers of seismic channels and high-density energy-efficient acquisition systems are the development trend of seismic instruments and have attracted high R&D interests in recent years. The combination of remote sensing and wireless sensor network technology provides superior observation capabilities for high-density seismic exploration. However, large-scale and multi-node acquisition methods place higher requirements on time synchronization performance. Seismic data with poor time synchronization will cause considerable errors in the interpretation of seismic data and even have no practical significance. Thus, the strict time synchronization performance is the prerequisite and basis for the application of cable-less storage seismograph in high-density seismic array applications. The existing time synchronization methods have high power consumption and poor time synchronization accuracy, which is not suitable for the long-time task. In addition, these methods are affected by the number of nodes and the distance. This paper presents an energy-efficient time-sharing indexed interpolation intercept method for the seismic data synchronization. The time synchronization method uses the high-precision TCXO as the main clock and records GPS time in the SD card at intervals to achieve the high-precision time-stamp for the seismic data. Then the seismic data is intercepted intermittently based on precise time stamps, which achieves the strict seismic data synchronization. Performance analysis shows that the time synchronization accuracy of the proposed method is 0.6 μs and saves 73% energy of the time-sync periods compared to the common GPS timing method. The field measurement results indicate that the time synchronization accuracy is not associated with the working time and the distance between nodes so that the proposed synchronization method is suitable for the high-density seismic survey.

Hydrophone is a key component of marine seismic exploration systems, divided into a scalar hydrophone and vector hydrophone. The electrochemical vector hydrophone has attracted much attention due to its high sensitivity and low-frequency detection capability. With the development of noise reduction technology, high-frequency noise has been effectively suppressed, while low-frequency noise is still difficult to control, which has become a key issue in the monitoring of underwater target radiation noise. The traditional electrochemical vector hydrophone based on the molecular electron transfer (MET) principle is limited in the working bandwidth in the low-frequency band, which affects the detection capability of low-frequency radiation signals from underwater targets. In order to solve this problem, a frequency response extension method of a MET electrochemical vector hydrophone based on dynamic feedback network is proposed. By introducing a dynamic force balance negative feedback system based on a digital signal processor (DSP), the working bandwidth of the hydrophone is extended, and the detection capability of low-frequency signals is enhanced. At the same time, the system has field adjustability and can resist the long-term system frequency characteristic drift. Experimental results show that the proposed method effectively improves the frequency response performance of the electrochemical vector hydrophone, providing a new technical solution for its application in the monitoring of low-frequency radiation noise from underwater targets.

During tunnel excavation, precise detection of low-velocity anomalies ahead of the tunnel face is crucial for ensuring the smooth construction of the tunnel project. However, current tunnel seismic prediction (TSP) system can only provide reflection interface imaging, which lacks the required accuracy and reliability in seismic data interpretation. In response to these limitations, this study presents a tunnel geological anomaly detection approach by utilizing the developed GEI-TSP system in combination with the multiparameter elastic full waveform inversion (EFWI) technique. This approach enables the retrieval of multiparameter distributions, including P-wave velocity, S-wave velocity, and density, for the geological anomalies ahead of the tunnel face, thereby enhancing the detection reliability. First, we design the tunnel forward model to generate the hybrid seismic data encompassing both body waves and Rayleigh surface waves. Second, we derive gradients and pseudo-Hessian operators using a wave mode decomposition strategy to mitigate crosstalk effects among various model parameters and improve inversion accuracy. With an activity level measurement, we conduct a novel model updating strategy to balance contributions from different wavefield combinations and avoid crosstalk between velocities and density. Experiments with several theoretical tunnel models demonstrate the feasibility of the proposed detection method and highlight its substantial potential for imaging anomalies. Meanwhile, the inversion performance of the proposed detection method is evaluated under different noise levels and observation layouts. Finally, a comprehensive assessment of the computational and storage performance of this method is carried out.