The Edge of Exploration: An Edge Storage and Computing Framework for Ambient Noise Seismic Interferometry Using Internet of Things Based Sensor Networks

Sepulveda, Frank; Thangraj, J. S.; Pulliam, J.

doi:10.3390/s22103615

Cited by 4 publications

(5 citation statements)

References 61 publications

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“…By utilising these low-power networks, the compact form factor of nodal seismic stations can be conserved while still providing real-time data. Indeed in recent years there have been some efforts to develop real-time seismic nodes for ambient noise tomography using IoT sensor networks [ 21 , 22 , 23 ]. Sepulveda et al [ 23 ] argue that a hub-and-spoke topology, where the network of sensors transmits pre-processed data to a base station, which in turn uploads the data to the cloud using wireless broadband communication (e.g., LTE), is suitable for real-time ambient noise tomography.…”

Section: Methodsmentioning

confidence: 99%

“…Indeed in recent years there have been some efforts to develop real-time seismic nodes for ambient noise tomography using IoT sensor networks [ 21 , 22 , 23 ]. Sepulveda et al [ 23 ] argue that a hub-and-spoke topology, where the network of sensors transmits pre-processed data to a base station, which in turn uploads the data to the cloud using wireless broadband communication (e.g., LTE), is suitable for real-time ambient noise tomography. Satellite communications, on the other hand, provide a more cost-effective alternative compared to other terrestrial solutions and can enable global connectivity [ 24 ].…”

Section: Methodsmentioning

confidence: 99%

“…The components contained in the Geode (from top to bottom) are the satellite modem, battery pack, enclosure housing the electronics and sensor digitiser, single-component geophone element and spike. To enable the use of DtS-IoT, we reduce the data volumes by performing data pre-processing (or edge computing) directly on the Geode microcontroller (similar to [ 21 , 22 , 23 ]). It is important to note that the utilisation of edge-processing is main differentiating factor between IoT-enabled seismic devices and conventional real-time seismic stations that utilise cellular or satellite telemetry.…”

Section: Methodsmentioning

confidence: 99%

“…The low data rates for ambient noise tomography lends itself well to long-range low-power Internet of Things (IoT) networks. In recent years there have been some efforts to develop real-time seismic nodes for ambient noise tomography using IoT sensor networks [ 21 , 22 , 23 ], but these either require a base station with line-of-sight to each node (similar to current real-time nodes) or narrowband IoT (NB-IoT) coverage for each node to transmit its data to the cloud. These limitations severely impact the utility of these devices in remote regions with limited or no cellular coverage and moderate topography variations that make line-of-sight difficult to establish.…”

Section: Introductionmentioning

confidence: 99%

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Fleet’s Geode: A Breakthrough Sensor for Real-Time Ambient Seismic Noise Tomography over DtS-IoT

Olivier

Borg

Trevor

et al. 2022

Sensors

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As most of the outcropping and shallow mineral deposits have been found, new technology is imperative to finding the hidden critical mineral deposits required to transition to renewable energy. One such new technique, called ambient seismic noise tomography, has shown promise in recent years as a low-cost, low environmental impact method that can image under cover and at depth. Wireless and compact nodal seismic technology has been instrumental to enable industry applications of ambient noise tomography, but these devices are designed for the active seismic reflection method and do not have the required sensitivity at low frequencies for ambient noise tomography, and real-time data transmission in remote locations requires significant infrastructure to be installed. In this paper, we show the development and testing of the Geode—a real-time seismic node purpose-built by Fleet Space Technologies for ambient seismic noise tomography on exploration scales. We discuss the key differences between current nodal technology and the Geode and show results of a field trial where the performance of the Geode is compared with a commercially popular nodal geophone. The use of a 2 Hz high sensitivity geophone and low noise digitiser results in an instrument noise floor that is more than 30 dB lower below 5 Hz than nodes that are commonly used in the industry. The increased sensitivity results in signal-to-noise ratios in the cross-correlation functions in the field trial that are more than double that of commercially available nodal geophone at low frequencies. When considering the full bandwidth of retrievable correlations in our study, using the Geode would reduce the required recording time from 75 h to 32 h to achieve an average signal-to-noise ratio in the cross-correlation functions of 10. We also discuss the integration of a real-time direct-to-satellite Internet of Things (DtS-IoT) modem in the Geode, which, together with edge processing of seismic data directly on the Geode, enables us to image the subsurface in real-time. During the field trial, the Geodes successfully transmitted more than 90% of the available preprocessed data packets. The Geode is compact enough so that several devices can be carried and installed by one field technician, whilst the array of stations do not require a base station to transmit data to the cloud for further processing. We believe this is the future of passive seismic surveys and will result in faster and more dynamic seismic imaging capabilities analogous to the medical imaging community, increasing the pace at which new mineral deposits are discovered.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Fleet’s Geode: A Breakthrough Sensor for Real-Time Ambient Seismic Noise Tomography over DtS-IoT

Olivier

Borg

Trevor

et al. 2022

Sensors

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“…In contrast, cloud infrastructure can provide storage and processing capabilities for large-scale data analysis and modeling [157]. Coordination between these various technical aspects is essential for the successful deployment and operation of EEWS systems, and careful consideration of their capabilities, limitations, and interdependencies is necessary to ensure their effectiveness in mitigating the impact of earthquakes [158].…”

mentioning

confidence: 99%

Early Detection of Earthquakes Using IoT and Cloud Infrastructure: A Survey

Abdalzaher,

Krichen,

Yiltas-Kaplan

et al. 2023

Sustainability

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Earthquake early warning systems (EEWS) are crucial for saving lives in earthquake-prone areas. In this study, we explore the potential of IoT and cloud infrastructure in realizing a sustainable EEWS that is capable of providing early warning to people and coordinating disaster response efforts. To achieve this goal, we provide an overview of the fundamental concepts of seismic waves and associated signal processing. We then present a detailed discussion of the IoT-enabled EEWS, including the use of IoT networks to track the actions taken by various EEWS organizations and the cloud infrastructure to gather data, analyze it, and send alarms when necessary. Furthermore, we present a taxonomy of emerging EEWS approaches using IoT and cloud facilities, which includes the integration of advanced technologies such as machine learning (ML) algorithms, distributed computing, and edge computing. We also elaborate on a generic EEWS architecture that is sustainable and efficient and highlight the importance of considering sustainability in the design of such systems. Additionally, we discuss the role of drones in disaster management and their potential to enhance the effectiveness of EEWS. Furthermore, we provide a summary of the primary verification and validation methods required for the systems under consideration. In addition to the contributions mentioned above, this study also highlights the implications of using IoT and cloud infrastructure in early earthquake detection and disaster management. Our research design involved a comprehensive survey of the existing literature on early earthquake warning systems and the use of IoT and cloud infrastructure. We also conducted a thorough analysis of the taxonomy of emerging EEWS approaches using IoT and cloud facilities and the verification and validation methods required for such systems. Our findings suggest that the use of IoT and cloud infrastructure in early earthquake detection can significantly improve the speed and effectiveness of disaster response efforts, thereby saving lives and reducing the economic impact of earthquakes. Finally, we identify research gaps in this domain and suggest future directions toward achieving a sustainable EEWS. Overall, this study provides valuable insights into the use of IoT and cloud infrastructure in earthquake disaster early detection and emphasizes the importance of sustainability in designing such systems.

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