s r!This paper identifies and explores the technical requirements and issues associated with remotely monitoring continuous wave (CW) sources with seismic arrays. Potential approaches to this monitoring problem will be suggested and partially evaluated to expose the monitoring challenges which arise when realistic local geologies and cultural noise sources are considered. The selective directionality and the adaptive noise cancellation properties of arrays are required to observe weak signals while suppressing a colored background punctuated with an unknown distribution of point and sometimes distributive sources. The array is also required to characterize the emitters and propagation environment so as to properly focus on the CW sources of interest while suppressing the remaining emitters. The proper application of arrays requires an appreciation of the complexity of propagation in a non-homogeneous earth. The heterogeneity often limits the available spatial coherence and therefore the size of the army. This adversely impacts the array gain and the array's ability to carefully resolve various emitters. Arrays must also contend with multipath induced by the source and the heterogeneous earth. If the array is to focus on an emitter and realize an enhancement in the signal to noise ratio, methods must be sought to coherently add the desired signal components while suppressing interference which may be correlated with the desired signal. The ihpact of these and other issues on army design and processing are described and discussed.
Q . § T 0This paper describes a novel digital signal processing algorithm for adaptively detecting and identifying signals burie in noise. The algorithm continually computes and updates the long-term statistics and spectral characteristics of the background noise. Using this noise model, a set of adaptive thresholds and matched digital filters are implemented to enhance and detect signals that are buried in the noise. The algorithm furthermore automatically suppresses coherent noise sources and adapts to time-varying signal conditions. Signal detection is performed in both the time-domain and the frequency-domain, thereby permitting the detection of both broad-band transients and narrow-band signals. The detection algorithm also provides for the computation of important signal features such as amplitude, timing, and phase information. Signal identification is achieved through a combination of frequency-domain template matching and spectral peak picking. The algorithm described herein is well suited for real-time implementation on digital signal processing hardware. This paper presents the theory of the adaptive algorithm, provides an algorithmic block diagram, and demonstrate its implementation and performance with real-world data. The computational efficiency of the algorithm is demonstrated through benchmarks on specific DSP hardware. The applications for this algorithm, which range from vibration analysis to real-time image processing, are also discussed.
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