A near-surface nodal seismic exploration system and its collaborative data acquisition method

Abstract

This invention belongs to the field of seismic exploration technology, specifically a near-surface node seismic exploration system and its data collaborative acquisition method; it includes miniaturized intelligent node units, adaptive mesh network base stations, and a cloud-based collaborative processing platform. Each node integrates a three-component sensor, a hybrid timing module, an edge computing unit, and a multimodal communication interface, employing an adaptive coupling mechanism to adapt to complex terrain. The acquisition method includes intelligent deployment planning, adaptive network construction, multimodal data acquisition, edge-cloud collaborative processing, and dynamic quality control. The nodes are buried shallowly below the surface, receiving reflected seismic wave signals from deep underground targets without being affected by the complex shallow surface strata above. The node units can also receive direct signals from ground-based seismic sources, used to calibrate and detect near-surface velocities and tectonic models. This invention offers advantages such as high density and low cost, high-precision synchronization across all scenarios, efficient data transmission, and multi-physics field fusion.

Description

Technical Field

[0001] This invention belongs to the field of seismic exploration technology, specifically relating to a near-surface node seismic exploration (NSN) system and its data collaborative acquisition method, applicable to shallow geological structure detection, engineering geological exploration, urban underground space exploration, and deep oil and gas exploration. Background Technology

[0002] Traditional seismic exploration techniques primarily target underground oil and gas resource exploration in areas with simple surface structures, employing wired acquisition systems or simple wireless node acquisition methods. In recent years, with the development of nodal seismograph technology, wireless nodal seismic exploration systems have been widely used in deep exploration. However, due to the complexity of the near-surface environment in western and southern China, the signal-to-noise ratio of acquired seismic data is extremely low, severely impacting the quality of seismic imaging. Steep mountains, deep ravines, and significant elevation differences lead to travel time distortion in the acquired raw seismic data, resulting in severe static correction problems. Strong deformation of older strata, with thrusting and overburden exposure, causes dramatic near-surface velocity variations and steep structures. Severe weathering and erosion of exposed rock strata, along with a loose surface layer, result in severe dispersion effects and high-frequency absorption. These complex near-surface seismic geological conditions pose significant challenges to seismic data acquisition and processing, leading to extremely poor consistency between excitation and reception conditions and seismic wavelets (waveform, energy, and frequency). In summary, for surface seismic exploration, the most significant impact of complex near-surface structures is the strong scattering of all deep reflected waves. Combined with severe near-surface absorption and dispersion, this creates a semi-random, semi-coherent near-surface strong scattering noise background that permeates the entire shot gather, drowning out deep reflected signals. Therefore, the complexity of the near-surface is the main reason for the extremely low signal-to-noise ratio (SNR) of seismic data, severely restricting the seismic imaging effect of deep structures. This is a bottleneck problem for oil exploration in complex areas and a long-standing challenge for oil exploration both domestically and internationally. Extremely low SNR data typically exhibit waveform fragmentation, severe dispersion, untrackable phase axes, and noise intensity exceeding the effective signal strength. Complex near-surface scattering attenuates all surface-observed wavefields, mainly manifesting as focusing, defocusing, and wave mode conversion, which is closely related to the roughness of the undulating surface and the correlation length of the random near-surface medium.

[0003] Therefore, when nodal seismic instruments are deployed on the ground in work areas with complex surface conditions to collect reflected seismic signals from deep or ultra-deep underground targets, the strong scattering effect of the complex near-surface on all deep reflected waves, coupled with severe absorption and dispersion near the surface, forms a semi-random and semi-coherent near-surface strong scattering noise background that permeates the entire shot gather, drowning out deep reflected signals and making it impossible to achieve effective exploration of deep or ultra-deep underground targets.

[0004] However, existing technologies are mainly designed for large-area (usually greater than 20 km²) and deep-seated exploration, and have the following problems:

[0005] (1) The contradiction between deployment density and cost: In order to pursue deep exploration capabilities, traditional node systems adopt a sparse deployment method, which cannot meet the requirements of high-resolution near-surface exploration for dense sampling; while simply increasing the node density will lead to a sharp increase in cost;

[0006] (2) Insufficient clock synchronization accuracy: Existing wireless node systems mostly use GPS timing or simple wireless synchronization methods. In complex near-surface environments (urban canyons, dense forests, tunnels, etc.), GPS signals are easily blocked, resulting in large time synchronization errors between nodes, which affects the imaging accuracy of shallow reflected waves.

[0007] (3) Low data transmission efficiency: Traditional node systems use TDMA or simple OFDMA transmission methods. In near-surface exploration with high node density and large data volume, the data return delay is long and the real-time processing capability is weak.

[0008] (4) Poor environmental adaptability: Most existing node instruments are designed for deep exploration, are large in size, and are inconvenient to deploy, making it difficult to adapt to the flexible deployment requirements of complex near-surface environments such as cities and mountains.

[0009] (5) Lack of multi-physics field coordination: Traditional seismic exploration only focuses on seismic wave fields and does not make full use of auxiliary information such as electromagnetic and temperature to comprehensively characterize complex near-surface media.

[0010] Therefore, there is an urgent need for a high-density, high-precision, and highly adaptable nodal seismic exploration system that is specifically designed for near-surface detection needs and can also meet the requirements for deep and ultra-deep underground target detection. Summary of the Invention

[0011] This invention provides a near-surface node seismic exploration (NSN) system and its data collaborative acquisition method, which solves the problem that due to the numerous complex influencing factors near the surface, the interference of P-waves and converted waves is mainly near the surface, and the coupling effect of shear wave sources is closely related to near-surface conditions, the existing seismic exploration data has a low signal-to-noise ratio and resolution, and the quality is difficult to meet the requirements of high-resolution detection of shallow surfaces and exploration of deep and ultra-deep underground targets.

[0012] The specific technical solution is as follows:

[0013] A near-surface node seismic exploration system includes: two-dimensional or three-dimensional artificial seismic sources deployed on the ground according to a design scheme, and miniaturized intelligent node units (NSN-Node) buried underground. The miniaturized intelligent node unit integrates a three-component seismic sensor array, a hybrid time synchronization module, an edge computing processing unit, a multimodal communication interface, and an environment adaptive coupling mechanism.

[0014] It also includes adaptive Mesh network base stations for dynamically managing node communication, performing distributed data fusion, and accessing multi-source heterogeneous data; and a cloud-based collaborative processing platform for performing near-surface-specific algorithm processing, digital twin visualization, and dynamic quality control.

[0015] The miniaturized intelligent node unit (NSN-Node) adopts a modular design and includes: a three-component seismic sensor array: integrating a high-sensitivity MEMS accelerometer (three-axis) and a moving-coil detector, with a frequency band of 0.1 Hz ~ 10 kHz and a dynamic range of ≥120 dB, suitable for near-surface broadband signal acquisition;

[0016] The hybrid time synchronization module integrates GPS / BeiDou, IEEE 1588 Precise Time Protocol (PTP), and a crystal oscillator-based adaptive holding algorithm. When satellite signals are lost, time synchronization is achieved through an inter-node mesh network with a synchronization accuracy better than ±1 μs.

[0017] The edge computing processing unit has a built-in ARM Cortex-M7 processor and supports real-time digital filtering, adaptive noise suppression, and automatic arrival pickup to reduce the amount of raw data transmission.

[0018] The multimodal communication interface supports adaptive switching between three modes: LoRa (long-range low power), Wi-Fi 6 (high-speed short-range), and 5G NR (real-time backhaul), dynamically selecting the optimal transmission method based on the on-site network environment.

[0019] The environmental adaptive coupling mechanism employs a biomimetic design with a variable stiffness tail cone that automatically adjusts the insertion depth and coupling pressure based on the hardness of the shallow subsurface medium, ensuring good acoustic coupling under various surface conditions such as soil, gravel, asphalt, and concrete.

[0020] The adaptive mesh network base station (NSN-Gateway), serving as a regional data aggregation center, features: dynamic time division multiple access (DTDMA) scheduling, dynamically optimizing time slot allocation based on the number of nodes, data volume, and channel quality, supporting single base station management of ≥1000 nodes; distributed data fusion: achieving time alignment, spatial interpolation, and preliminary offset imaging of multi-node data at the base station, reducing the central processing pressure; and multi-source heterogeneous data access: synchronously receiving auxiliary sensor data such as electromagnetic, temperature, and tilt data from nodes in addition to seismic data.

[0021] The cloud-based collaborative processing platform (NSN-Cloud) includes: a near-surface dedicated processing algorithm library, containing static correction algorithms, surface wave intelligent separation algorithms, and high-resolution reflected wave imaging algorithms for shallow loose media; a digital twin visualization module, which constructs a near-surface three-dimensional geological model in real time based on the collected data and supports virtual reality (VR) interactive analysis; and a quality monitoring and autonomous decision-making system, which evaluates data quality in real time through machine learning, automatically identifies abnormal nodes, and triggers supplementary measurement instructions.

[0022] The artificial seismic source is a controllable seismic source, an explosive seismic source, a directional gas explosion seismic source, a hammer seismic source, an electric spark seismic source, an underwater air gun seismic source, or an underwater plasma seismic source.

[0023] The data collaborative acquisition method of the near-surface node seismic exploration system includes the following steps:

[0024] S1: Intelligent Deployment Planning: Based on the depth and resolution requirements of the detection target, the optimal node spacing is calculated based on Fresnel zone theory (usually 3.125m~12.5m or 25m); terrain data is acquired using UAVs or Mobile Measurement System (MMS) to generate two-dimensional or three-dimensional miniaturized intelligent node unit deployment path planning, avoiding obstacles and optimizing node density distribution;

[0025] S2: Adaptive Network Construction: After node deployment, a Mesh network is automatically formed, and the optimal multi-hop route is selected through link quality assessment to achieve sub-microsecond time synchronization; the base station periodically broadcasts the time reference, and the nodes achieve sub-microsecond synchronization through the PTP protocol; in the GPS denied environment, the relative synchronization mode between nodes is started, and clock consistency is maintained by using bidirectional time transfer (TWTT);

[0026] S3: Multimodal data acquisition, simultaneously acquiring seismic data and auxiliary data; Seismic acquisition: automatically adjusts the sampling rate (1 kHz ~ 50 kHz) and recording length according to the source type (hammer strike, drop impact, controlled source); Auxiliary acquisition: simultaneously records environmental electromagnetic field, temperature field and nodal attitude data for subsequent data quality evaluation and noise suppression;

[0027] S4: Edge computing processing unit performs edge-cloud collaborative processing. The node performs real-time filtering, compression (compression ratio ≥10:1) and event detection. The base station completes gather sorting, first arrival correction, near-surface model inversion and preliminary processing. The cloud performs full waveform inversion (FWI) and multi-wave joint imaging and geological interpretation.

[0028] S5: Dynamic quality control establishes a multi-dimensional data quality evaluation system based on the ellipsoidal volume method, and monitors the signal-to-noise ratio, synchronization accuracy, and coupling status in real time; it automatically marks abnormal miniaturized intelligent node units and pushes them to the field terminal to guide rapid maintenance.

[0029] The miniaturized intelligent node unit is buried below the shallow surface and can directly receive down-going direct P-wave and direct S-wave signals from ground seismic sources. Based on the burial depth of the miniaturized intelligent node unit and the travel time of the direct P-wave and S-wave, it can directly calculate the accurate shallow surface P-wave and S-wave velocities and the attenuation coefficients of the P-wave and S-wave, which can be used to calibrate and detect near-surface velocities and structural models. It can also be used to calibrate other indirect inversion results.

[0030] The data collaborative acquisition method of the near-surface node seismic exploration system is applied to seismic exploration scenarios with complex shallow surface structures, extremely uneven lateral shallow surface velocities, dramatic topographic elevation variations, and targets located in deep and ultra-deep layers. By burying miniaturized intelligent node units below the shallow surface, these underground miniaturized intelligent node units can receive reflected seismic wave signals from deep and ultra-deep targets without being affected by the complex shallow surface strata above them.

[0031] The beneficial effects of this invention are:

[0032] High density and low cost: Miniaturized design reduces the cost per node by more than 60%, supporting the deployment of ≥10,000 nodes per square kilometer;

[0033] High-precision synchronization across all scenarios: The hybrid timing solution ensures sub-microsecond synchronization in complex environments, improving the accuracy of traditional GPS timing by an order of magnitude.

[0034] High-efficiency data transmission: The combination of multimodal communication and edge computing enables data return latency of less than 5 seconds, meeting real-time processing requirements;

[0035] Multiphysics field fusion: Simultaneous acquisition of seismic-electromagnetic-temperature data enhances the ability to identify complex near-surface media (such as caves, mining voids, and pollution plumes). Attached Figure Description

[0036] To more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be considered as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort. In the drawings:

[0037] Figure 1 : A schematic diagram of the field deployment of a near-surface node seismic exploration system according to the present invention;

[0038] Figure 2 : Schematic diagram of the miniaturized intelligent node unit structure of the present invention.

[0039] The attached diagram shows the markings and corresponding component names:

[0040] 1-Artificial seismic source; 2-Miniaturized intelligent node unit; 21-Three-component seismic sensor array; 22-Hybrid time synchronization module; 23-Edge computing processing unit; 24-Multimodal communication interface; 25-Environmentally adaptive coupling mechanism; 3-Adaptive Mesh network base station; 4-Cloud collaborative processing platform. Detailed Implementation

[0041] To facilitate understanding of the objectives, technical solutions, and advantages of this invention, the invention will be described in more detail below with reference to the accompanying drawings and specific embodiments. The accompanying drawings illustrate preferred embodiments of the invention. However, the invention can be implemented in many different forms and is not limited to the embodiments described in this specification. Rather, these embodiments are provided to provide a more thorough and complete understanding of the disclosure of this invention. The illustrative embodiments and descriptions of this invention are for illustrative purposes only and do not constitute a limitation of the invention; they are merely examples, and the advantages of the invention will become clearer and easier to understand by illustrating them.

[0042] Example 1

[0043] like Figure 1 A schematic diagram of the field deployment of a near-surface node seismic exploration system is shown, including: two-dimensional or three-dimensional artificial seismic sources 1 deployed on the ground according to the design scheme, and miniaturized intelligent node units 2 buried underground.

[0044] like Figure 2 As shown in the schematic diagram of the miniaturized intelligent node unit structure, this system also includes an adaptive Mesh network base station 3, which is used to dynamically manage node communication, perform distributed data fusion, and access multi-source heterogeneous data; and a cloud-based collaborative processing platform 4, which is used to perform near-surface dedicated algorithm processing, digital twin visualization, and dynamic quality control.

[0045] The miniaturized intelligent node unit 2 integrates a three-component seismic sensor array 21, a hybrid time synchronization module 22, an edge computing processing unit 23, a multimodal communication interface 24, and an environment adaptive coupling mechanism 25;

[0046] The hybrid time synchronization module 22 integrates GPS / BeiDou, IEEE 1588 precise time protocol and crystal oscillator-based adaptive holding algorithm. When the satellite signal is lost, it achieves time synchronization through the adaptive Mesh network base station 3, with a synchronization accuracy better than ±1μs.

[0047] The multimodal communication interface 24 supports adaptive switching between three modes: LoRa, Wi-Fi 6, and 5G NR, and dynamically selects the optimal transmission method according to the on-site network environment.

[0048] The environmental adaptive coupling mechanism 25 adopts a biomimetic variable stiffness tail cone, which automatically adjusts the insertion depth and coupling pressure according to the hardness of the shallow subsurface medium.

[0049] The adaptive Mesh network base station 3 adopts dynamic time division multiple access (DTDMA) scheduling, which dynamically optimizes time slot allocation based on the number of nodes, data volume and channel quality, and supports a single base station to manage ≥1000 nodes;

[0050] The artificial seismic source 1 is a controllable seismic source, an explosive seismic source, a directional gas explosion seismic source, a hammer seismic source, an electric spark seismic source, an underwater air gun seismic source, or an underwater plasma seismic source.

[0051] The data collaborative acquisition method for the near-surface node seismic exploration system includes the following steps:

[0052] S1: Intelligent deployment planning, based on Fresnel zone theory to calculate the optimal spacing of miniaturized intelligent node units 2, and use UAVs or mobile measurement systems to acquire terrain data to generate two-dimensional or three-dimensional miniaturized intelligent node unit 2 deployment path planning;

[0053] S2: Adaptive network construction, nodes automatically form a mesh network, select the optimal multi-hop route through link quality assessment, and achieve sub-microsecond time synchronization; in GPS denied environments, relative synchronization mode between nodes is initiated, and clock consistency is maintained by bidirectional time transfer (TWTT);

[0054] S3: Multimodal data acquisition, simultaneously acquiring seismic data and auxiliary data; automatically adjusting the sampling rate (1 kHz ~ 50 kHz) and recording length according to the source type; auxiliary data includes environmental electromagnetic field, temperature field and nodal attitude data.

[0055] S4: Edge computing processing unit 23 performs edge-cloud collaborative processing. The node performs real-time filtering, compression and event detection, the base station completes gather sorting and preliminary processing, and the cloud performs full waveform inversion and multi-wave joint imaging.

[0056] S5: Dynamic quality control, establishing a multi-dimensional data quality evaluation system, automatically marking abnormal miniaturized intelligent node units 2 and pushing maintenance instructions; the multi-dimensional data quality evaluation system is based on the ellipsoidal volume method, and monitors the signal-to-noise ratio, synchronization accuracy and coupling status in real time.

[0057] The aforementioned underground miniaturized intelligent node unit 2 is buried below the shallow surface and can directly receive the down-going direct P-wave and direct S-wave signals generated by the ground source 1. Based on the burial depth of the miniaturized intelligent node unit 2 and the travel time of the direct P-wave and S-wave, it can directly calculate the accurate shallow surface P-wave and S-wave velocities and the attenuation coefficients of the P-wave and S-wave, which can be used to calibrate and detect near-surface velocities and structural models. It can also be used to calibrate other indirect inversion results.

[0058] The data collaborative acquisition method of the near-surface node seismic exploration system is applied to seismic exploration scenarios with complex shallow surface structures, extremely uneven lateral shallow surface velocities, dramatic topographic elevation variations, and targets located in deep and ultra-deep layers. By burying miniaturized intelligent node units 2 below the shallow surface, the underground miniaturized intelligent node units 2 can receive reflected seismic wave signals from deep and ultra-deep targets without being affected by the complex shallow surface strata above them.

[0059] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A near-surface nodal seismic exploration system, characterized in that, include: Two-dimensional or three-dimensional artificial seismic sources (1) are deployed on the ground according to the design scheme, and miniaturized intelligent node units (2) are buried underground. The miniaturized intelligent node unit (2) integrates a three-component seismic sensor array (21), a hybrid time synchronization module (22), an edge computing processing unit (23), a multimodal communication interface (24), and an environmental adaptive coupling mechanism (25). It also includes an adaptive Mesh network base station (3), which is used to dynamically manage node communication, perform distributed data fusion, and access multi-source heterogeneous data; The cloud-based collaborative processing platform (4) is used to perform near-surface-specific algorithm processing, digital twin visualization, and dynamic quality control.

2. The near-surface nodal seismic exploration system according to claim 1, characterized in that, The hybrid time synchronization module (22) integrates GPS or Beidou, IEEE 1588 precise time protocol and crystal oscillator-based adaptive holding algorithm. When the satellite signal is lost, it achieves time synchronization through adaptive Mesh network base station (3), with a synchronization accuracy better than ±1μs.

3. The near-surface nodal seismic exploration system according to claim 1, characterized in that, The multimodal communication interface (24) supports adaptive switching between three modes: LoRa, Wi-Fi 6 and 5G NR, and dynamically selects the optimal transmission method according to the on-site network environment.

4. The near-surface nodal seismic exploration system according to claim 1, characterized in that, The environmental adaptive coupling mechanism (25) adopts a biomimetic design of a variable stiffness tail cone, which automatically adjusts the insertion depth and coupling pressure according to the hardness of the shallow subsurface medium.

5. The near-surface nodal seismic exploration system according to claim 1, characterized in that, The adaptive Mesh network base station (3) adopts dynamic time division multiple access (DTDMA) scheduling, dynamically optimizes time slot allocation according to the number of nodes, data volume and channel quality, and supports a single base station to manage ≥1000 nodes.

6. The near-surface nodal seismic exploration system according to claim 1, characterized in that, The artificial seismic source (1) is a controllable seismic source, an explosive seismic source, a directional gas explosion seismic source, a hammer seismic source, an electric spark seismic source, an underwater air gun seismic source, or an underwater plasma seismic source.

7. The data collaborative acquisition method for the near-surface node seismic exploration system according to any one of claims 1-6, characterized in that, Includes the following steps: S1: Intelligent deployment planning, based on Fresnel zone theory to calculate the optimal spacing of miniaturized intelligent node units (2), and use UAVs or mobile measurement systems to obtain terrain data to generate two-dimensional or three-dimensional miniaturized intelligent node units (2) deployment path planning; S2: Adaptive network construction, nodes automatically form a mesh network, select the optimal multi-hop route through link quality assessment, and achieve sub-microsecond time synchronization; S3: Multimodal data acquisition, simultaneously acquiring seismic data and auxiliary data; S4: Edge computing processing unit (23) is used for edge-cloud collaborative processing. The node performs real-time filtering, compression and event detection, the base station completes gather sorting and preliminary processing, and the cloud performs full waveform inversion and multi-wave joint imaging. S5: Dynamic quality control, establish a multi-dimensional data quality evaluation system, automatically mark abnormal miniaturized intelligent node units (2) and push maintenance instructions.

8. The data collaborative acquisition method for a near-surface node seismic exploration system according to claim 7, characterized in that, In step S2, a relative synchronization mode between nodes is initiated in a GPS denied environment, and clock consistency is maintained by using bidirectional time transfer (TWTT).

9. The data collaborative acquisition method for a near-surface node seismic exploration system according to claim 7, characterized in that, In step S3, the sampling rate and recording length are automatically adjusted according to the source type. The auxiliary data include environmental electromagnetic field, temperature field and nodal attitude data.

10. The data collaborative acquisition method for a near-surface node seismic exploration system according to claim 7, characterized in that, In step S5, the multi-dimensional data quality evaluation system is based on the ellipsoidal volume method and monitors the signal-to-noise ratio, synchronization accuracy, and coupling status in real time. The miniaturized intelligent node unit (2) is buried below the shallow ground surface and can directly receive the down-going direct P-wave and direct S-wave signals from the ground source. Based on the burial depth of the miniaturized intelligent node unit (2) and the travel time of the direct P-wave and S-wave, it can directly calculate the accurate shallow surface P-wave and S-wave velocities and the attenuation coefficients of the P-wave and S-wave, which can be used to calibrate and detect near-surface velocities and structural models. It can also be used to calibrate other indirect inversion results.

Images