Real-time In-situ Seismic Tomography in Sensor Network

Seismic tomography is a technique for illuminating the physical dynamics of the Earth by seismic waves generated by earthquakes or explosions. In both industry and academia, the seismic exploration does not yet have the capability of imaging seismic tomography in real-time and with high resolution. There are two reasons. First, at present raw seismic data are typically recorded on sensor nodes locally then are manually collected to central observatories for post processing, and this process may take months to complete. Second, high resolution tomography requires a large and dense sensor network, the real-time data retrieval from a network of large-amount wireless seismic nodes to a central server is virtually impossible due to the sheer data amount and resource limitations. This limits our ability to understand earthquake zone or volcano dynamics. To obtain the seismic tomography in real-time and high resolution, a new design of sensor network system for raw seismic data processing and distributed tomography computation is demanded. Based on these requirements, three research aspects are addressed in this work. First, a distributed multi-resolution evolving tomography computation algorithm is proposed to compute tomography in the network, while avoiding costly data collections and centralized computations. Second, InsightTomo, an end-to-end sensor network emulation platform, is designed to emulate the entire process from data recording to tomography image result delivery. Third, a sensor network testbed is presented to verify the related methods and design in real world. The design of the platform consists of hardware, sensing and data processing components

Passive Seismic Tomography Using Induced Seismicity at a Petroleum Field in Oman

A borehole network consisting of 5 monitoring wells was used to monitor the induced seismicity at a producing petroleum field for a period of about 11 months. Nearly 5400 microseismic events were analyzed and utilized in imaging the reservoir based on a new doubledifference (DD) seismic tomography. The DD tomography method simultaneously solves for event locations and Vp, Vs, and Vp/Vs models using absolute and differential P, S and S-P arrival times. Microseismicity in the field was primarily caused by compaction of the reservoir in and above the gas bearing formation and was distributed along the two major northeastsouthwest (NE-SW) faults in the field. The model resolution analysis based on the checkerboard test and the resolution matrix showed that the central part of the model was relatively well resolved for the depth range of 0.7 to 1.1 km. Clear velocity contrasts were imaged across most parts of the two NE-SW faults. Vp/Vs ratio estimations from the tomographic inversion were low (<1.75) in the shallow depth range, likely due to lithology and gas content, whereas they were large (>1.75) in the deeper part of the model, likely due to fluid saturated formation. In this study seismic tomography showed a great potential for reservoir imaging and property estimation using induced seismicity.Petroleum Development Oma

In-situ Data Analytics In Cyber-Physical Systems

Cyber-Physical System (CPS) is an engineered system in which sensing, networking, and computing are tightly coupled with the control of the physical entities. To enable security, scalability and resiliency, new data analytics methodologies are required for computing, monitoring and optimization in CPS. This work investigates the data analytics related challenges in CPS through two study cases: Smart Grid and Seismic Imaging System. For smart grid, this work provides a complete solution for system management based on novel in-situ data analytics designs. We first propose methodologies for two important tasks of power system monitoring: grid topology change and power-line outage detection. To address the issue of low measurement redundancy in topology identification, particularly in the low-level distribution network, we develop a maximum a posterior based mechanism, which is capable of embedding prior information on the breakers status to enhance the identification accuracy. In power-line outage detection, existing approaches suer from high computational complexity and security issues raised from centralized implementation. Instead, this work presents a distributed data analytics framework, which carries out in-network processing and invokes low computational complexity, requiring only simple matrix-vector multiplications. To complete the system functionality, we also propose a new power grid restoration strategy involving data analytics for topology reconfiguration and resource planning after faults or changes. In seismic imaging system, we develop several innovative in-situ seismic imaging schemes in which each sensor node computes the tomography based on its partial information and through gossip with local neighbors. The seismic data are generated in a distributed fashion originally. Dierent from the conventional approach involving data collection and then processing in order, our proposed in-situ data computing methodology is much more ecient. The underlying mechanisms avoid the bottleneck problem on bandwidth since all the data are processed distributed in nature and only limited decisional information is communicated. Furthermore, the proposed algorithms can deliver quicker insights than the state-of-arts in seismic imaging. Hence they are more promising solutions for real-time in-situ data analytics, which is highly demanded in disaster monitoring related applications. Through extensive experiments, we demonstrate that the proposed data computing methods are able to achieve near-optimal high quality seismic tomography, retain low communication cost, and provide real-time seismic data analytics

Decentralized Convex Optimization for Wireless Sensor Networks

Many real-world applications arising in domains such as large-scale machine learning, wired and wireless networks can be formulated as distributed linear least-squares over a large network. These problems often have their data naturally distributed. For instance applications such as seismic imaging, smart grid have the sensors geographically distributed and the current algorithms to analyze these data rely on centralized approach. The data is either gathered manually, or relayed by expensive broadband stations, and then processed at a base station. This approach is time-consuming (weeks to months) and hazardous as the task involves manual data gathering in extreme conditions. To obtain the solution in real-time, we require decentralized algorithms that do not rely on a fusion center, cluster heads, or multi-hop communication. In this thesis, we propose several decentralized least squares optimization algorithm that are suitable for performing real-time seismic imaging in a sensor network. The algorithms are evaluated and tested using both synthetic and real-data traces. The results validate that our distributed algorithm is able to obtain a satisfactory image similar to centralized computation under constraints of network resources, while distributing the computational burden to sensor nodes

TRA of DigiMon components

The DigiMon project aims to develop an affordable, flexible, societally embedded and smart monitoring system for industrial scale subsurface CO2 storage. For this purpose, the DigiMon system is to combine various types of measurements in integrated workflows. In this report, we describe the process of conducting the Technology Readiness Assessment (TRA) of various measurement techniques. We report on the identification, description and assessment of these measurement techniques as Critical Technology Elements (CTEs) being part of the DigiMon system

TOMO-ETNA experiment at Etna volcano: Activities on land

In the present paper we describe the on-land field operations integrated in the TOMO-ETNA experiment carried out in June-November 2014 at Mt. Etna volcano and surrounding areas. This terrestrial campaign consists in the deployment of 90 short-period portable three-component seismic stations, 17 broadband seismometers and the coordination with 133 permanent seismic station belonging to Italy’s Istituto Nazionale di Geofisica e Vulcanologia (INGV). This temporary seismic network recorded active and passive seismic sources. Active seismic sources were generated by an array of air-guns mounted in the Spanish oceanographic vessel “Sarmiento de Gamboa” with a power capacity of up to 5200 cubic inches. In total more than 26,000 shots were fired and more than 450 local and regional earthquakes were recorded. We describe the whole technical procedure followed to guarantee the success of this complex seismic experiment. We started with the description of the location of the potential safety places to deploy the portable network and the products derived from this search (a large document including full characterization of the sites, owners and indication of how to arrive to them). A full technical description of the seismometers and seismic sources is presented. We show how the portable seismic network was deployed, maintained and recovered in different stages. The large international collaboration of this experiment is reflected in the participation of more than 75 researchers, technicians and students from different institutions and countries in the on-land activities. The main objectives of the experiment were achieved with great success.PublishedS04272SR. VULCANI - Servizi e ricerca per la SocietàJCR Journalope