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IoT-Based Wireless Networkingfor Seismic Applications

Hadi Jamali-Rad, Xander Campman

TL;DR

This work integrates low-power wide-area networks (LPWANs) into seismic sensing by designing two IoT-centric network architectures that pair long-range, low-power sensing with cloud computing for storage and analytics. It analyzes four seismic scenarios (GMM, ANSI, MFM, QCLS) to quantify data-generation rates and demonstrates a cross-layer PHY–MAC approach to accommodate both continuous and trigger-based data streams under duty-cycle constraints. A Groningen field design study and a LoRa-based proof-of-concept field test validate data-rate feasibility, scalability to thousands of sensors, and practical cost considerations, showing that delay-tolerant seismic monitoring can be effectively realized with LPWANs and cloud infrastructure. The results suggest a scalable, minimum-maintenance, real-time-capable seismic networking paradigm with potential applicability across Oil & Gas operations, from subsurface monitoring to asset tracking.

Abstract

We propose to employ a recently developed IoT-based wireless technology, so called low-power wide-area networks (LPWANs), to exploit their long range, low power, and inherent compatibility to cloud storage and computing. We create a remotely-operated minimum-maintenance wireless solution for four major seismic applications of interest. By proposing appropriate network architecture and data coordination (aggregation and transmission) designs we show that neither the low data-rate nor the low duty-cycle of LPWANs impose fundamental issues in handling a considerable amount of data created by complex seismic scenarios as long as the application is delay-tolerant. In order to confirm this claim, we cast our ideas into a practical large-scale networking design for simultaneous seismic monitoring and interferometry and carry out an analysis on the data generation and transmission rates. Finally, we present some results from a small-scale field test in which we have employed our IoT-based wireless nodes for real-time seismic quality control (QC) over clouds.

IoT-Based Wireless Networkingfor Seismic Applications

TL;DR

This work integrates low-power wide-area networks (LPWANs) into seismic sensing by designing two IoT-centric network architectures that pair long-range, low-power sensing with cloud computing for storage and analytics. It analyzes four seismic scenarios (GMM, ANSI, MFM, QCLS) to quantify data-generation rates and demonstrates a cross-layer PHY–MAC approach to accommodate both continuous and trigger-based data streams under duty-cycle constraints. A Groningen field design study and a LoRa-based proof-of-concept field test validate data-rate feasibility, scalability to thousands of sensors, and practical cost considerations, showing that delay-tolerant seismic monitoring can be effectively realized with LPWANs and cloud infrastructure. The results suggest a scalable, minimum-maintenance, real-time-capable seismic networking paradigm with potential applicability across Oil & Gas operations, from subsurface monitoring to asset tracking.

Abstract

We propose to employ a recently developed IoT-based wireless technology, so called low-power wide-area networks (LPWANs), to exploit their long range, low power, and inherent compatibility to cloud storage and computing. We create a remotely-operated minimum-maintenance wireless solution for four major seismic applications of interest. By proposing appropriate network architecture and data coordination (aggregation and transmission) designs we show that neither the low data-rate nor the low duty-cycle of LPWANs impose fundamental issues in handling a considerable amount of data created by complex seismic scenarios as long as the application is delay-tolerant. In order to confirm this claim, we cast our ideas into a practical large-scale networking design for simultaneous seismic monitoring and interferometry and carry out an analysis on the data generation and transmission rates. Finally, we present some results from a small-scale field test in which we have employed our IoT-based wireless nodes for real-time seismic quality control (QC) over clouds.
Paper Structure (19 sections, 16 equations, 12 figures, 4 tables, 1 algorithm)

This paper contains 19 sections, 16 equations, 12 figures, 4 tables, 1 algorithm.

Table of Contents

  1. Introduction and Related Work
  2. Scenarios of Interest: Seismic Setup and Data Generation
  3. Ground Motion Monitoring (GMM)
  4. Ambient Noise Seismic Interferometry (ANSI)
  5. Microseismic Fracture Monitoring (MFM)
  6. Quality Control for Active Land Seismic Surveys (QCLS)
  7. Suitable Wireless Technologies in the Market
  8. Low-Power Wide-Area Wireless (LPWA) Technologies
  9. Physical Layer (PHY) Design
  10. Cellular Network-Based Data Transmission
  11. Hybrid Private-Cellular Network-Based Data Transmission
  12. Cross-Layer (PHY-MAC) Design
  13. Design Study
  14. Data Generation Rates and Volumes
  15. Proposed Network Architecture
  16. ...and 4 more sections

Figures (12)

  • Figure 1: Ground motion monitoring network in the southern Apennines Weber07. The squares represent the network stations and the circles the local control centers (LCCs). The gray lines are the radio links between the stations and the LCCs, and the dashed lines are planned synchronous digital hierarchy (SDH) carrier-class radio upgrades for early-warning applications. The triangles represent radio repeater points.
  • Figure 2: Roving seismic interferometry network operated by NAM in Groningen, the Netherlands. The dots represent $3$-component sensors, and the green area on the map is the outline of the Groningen field.
  • Figure 3: A typical hydraulic fracturing setup where a high-pressure fluid is injected into a wellbore to create cracks in deep-rock formations through which oil and/or gas will flow more freely HydroFracImage.
  • Figure 4: Traditional cable-based seismic acquisition with tens of thousands of nodes connected through cables Kendall15.
  • Figure 5: LPWANs among the other legacy wireless technologies LinkLabsLPWAN. LPWANs stand out as they offer a long rage but a relatively low data-rate as compared to the existing wireless technologies.
  • ...and 7 more figures