Disributed principal component analysis for data compression of sequential seismic sensor arrays

This work considers the data compression of sequential seismic sensor arrays. First, the statistics of the seismic traces collected by all the sensors are modeled by using the mixture model. Hence, a distributed Principle Component Analysis (PCA) compression scheme for sequential sensor arrays is designed. The proposed scheme does not require transmitting the traces, leading to a more efficient computation and compression compared with the conventional local PCA compression. Furthermore, an efficient communication scheme is developed for the sequential sensor array for delivering the local statistics to the fusion center. In this communication scheme, the sensors update and pass a data package consisting of cumulative variables. The size of the data package does not increase throughout the process, which is more efficient than the direct communication scheme. Finally, the performance of the proposed scheme is evaluated by using both real and synthetic seismic data.