Hamood ur Rehman KhanAdvisor: Dr. Salam A. ZummoEE702: Directed Research IIApplication of Compressed Sensing to Distributed Seismic WSNs for Signal Compression1IntroductionWSNs for Seismic AcquisitionSignal Compression in Wireless Sensor NetworksCompressible Signal ModelSystem Model and AssumptionsPerformance Bounds: Optimum Distortion in Centralized SystemPerformance Bounds: Distortion in a Distributed WSNPerformance Bounds: Compressive Wireless SensingResultsOutline2How presentation will benefit audience: Adult learners are more interested in a subject if they know how or why it is important to them.Presenters level of expertise in the subject: Briefly state your credentials in this area, or explain why participants should listen to you.23Seismic measurements are well-known from their use in hydrocarbon explorationMethod is comparable to marine echo-sounder.Seismic waves are created by a hit on the surface and they travel undergroundLike sound waves, they are reflected and refracted when they reach a boundaryTime for the wave to come back to the surface and velocity allows determination of depth of geological boundariesIntroduction: Seismic Methods4Since the 1920s seismic reflection techniques have been used for the search of petroleum.Mainly two types of waves generatedP-waves: Primary compressional wavesS-waves: Secondary Shear wavesP-waves are faster and arrive before S-waves

Introduction: Seismic Methods (2)

5The intensity of reflected wave depends on the velocity and density difference between boundaries.Geometric ray propagation can be appliedFermats principle of least-time and Snells law is valid in describing the wave propagation Introduction: Seismic methods (3)

Head wavesDirect WavesReflected Waves6(1) Refracted Signal (2) Air-coupled Waves (R) Reflected Waves (3) Ground RollIntroduction: Typical field record (Shot Gather)

Seismic Acquisition involves recording the amplitudes of reflected wave fronts from subsurface structures.An active energy source (e.g., vibreosis) is used.Passive Acquisition: Blind signal recording of reflected energyActive Seismic Acquisition:Signal is seen in real-time as it is being shotSeismic and the case for WSNs7

8WSNs afford the followingFlexibility in survey deployment.Ability to handle unfavorable terrain.Unconventional layout of nodes for special applications like microseismic surveys.Reduced costs: Cables account for 50 percent of the weight and cost in seismic surveys.Fast deployment. Fewer crew members are required.

Advantage for using Wireless Sensor Network for Seismic Acquisition9Wide coverage area: Swathes of 30 sq. km are surveyed simultaneously.Highly dense network: 50-300K total nodes with a target of 1 million in the coming future for high density surveys.Very high aggregate network throughput: The min. sampling time is .5 ms with at least 24 bit quantization.This leads to an approx. bit rate of 50 kbps per node. Yields a network throughput of 5 Gpbs.This is the network load of a medium size city!Low Delay Application. For real-time data visibility, there is a delay constraint on the network for data delivery.

WSNs: Key requirements for Seismic10Signal Compression is Mandatory. It is needed to relax the tight throughput bottleneck on the network.Many different approaches can be adopted to achieve this end.The simplest method is a multi-hop compress and forward scheme for each node.More sophisticated schemes are those that have been proposed by othersWe will consider performance for lossy transform compression and compressed wireless sensing in terms of scaling laws vs. number of sensors.

WSN requirements: Signal Compression11Lossy compression for seismic data presents special challenges.The Dynamic range of the data is very high (~100 dB)High dynamic range requires very fine quantization in the ADC (24-32 bit).Seismic signals have an oscillatory nature.There is coherent noise presentCoherent noise is characterized by a coherent-noise function, which:Always produces the same output for a given input valueA small change in input value produces a small change in output valueA large change in the input value will a produce a random change in the output value

Lossy Compression in Wireless Seismic Acquisition Systems: Challenges12In ground seismic signals, ground roll is also present.Ground roll occurs because of waves travelling parallel to the surfaceGround roll is characterized by low frequency high amplitude events in the middle of the shot gather.In seismic processing and interpretation information from the ground roll is also extracted.The above fact means that ground roll has to be preserved in compression, i.e., it is part of the compressed information.Lossy Compression (Challenges: II)13Three basic dimensions to signal compression algorithms in WSNsSignal Model: The signal model identifies the key characteristics of the signals can be exploited during compression (spatial correlation, sparsity)Interplay between compression and routing: Design of compression algorithm may have to respect communication architecture of the WSNCoding Tools: Specific coding and quantization algorithm may have to be adopted to exploit signal characteristics (e.g., linear predictive coding, distributed source coding, distributed transform coding).

Signal Compression in WSNs14System Model and Assumptions15System Model and Assumptions (Continued)16Model of signal compressibility171819Communication Setup20Communication Setup21Communication Setup22Performance Metrics23Performance Metrics24Main Results: Optimal Distortion Scaling in a Centralized System25Main Results: Optimal Distortion Scaling in a Centralized System26Optimal Distortion Scaling in a Centralized System27Here is a plotMain Results: Optimal Distortion Scaling

28Distortion Scaling in Distributed WSNs29Distortion Scaling in Distributed WSNs30Scaling laws and Power-Distortion-Latency Tradeoffs31Bias and Variance limited Regimes32Bias and Variance Limited Regimes33

Power-Distortion-Latency Exponents Relationship34

Scaling Laws

35Compressed Wireless Sensing

36Compressed Wireless Sensing37

Rate Distortion38

Rate Distortion Communication Noise39Conclusions