Lossless compression of seismic signals using differentiation

Lossless compression of seismic data

“…This algorithm consists of five simple eigenpredictors. Eigenpredictors introduced in this work have an advantage in that they include the linear predictors of [7] as a special case. Furthermore, each eigenpredictor has integer coefficients and only one parameter to optimize l. Experimental results show that by optimizing l, variance of residue sequence can be reduced and CR can be improved.…”

“…An advantage of the development presented here is that it includes the work of [7] as a special case. Specifically, by setting o ¼ 0 in Equations (8)- (10), we obtain…”

“…Figs. 7 and 8 show the CR versus the frame number for data files 4 and 8 using l ¼ 1 (corresponding to the results of [7]) and l opt (corresponding to the proposed method in this work). In our computation we have set N ¼ 1000.…”

“…Although, lossless compression of seismic data has not received a great deal of attention so far, a number of algorithms have been developed [1][2][3][4][5][6][7][8][9]. These algorithms have two stages in common.…”

Efficient seismic response data storage and transmission using ARX model-based sensor data compression algorithm

“…This correlation leads to the information redundancy in the seismic sensor data. It is worth noting that although data compression algorithms for seismic waveform data has been examined by Peterson and Hutt [8], Stearns [5], Stearns et al [9], Kiely and Pollara [10], Nijim et al [11], Ives et al [12], all these data compression algorithms were based on single sensor data which did not utilize structural system information to enhance the compression performance. Using the structural system information made available from sensor network can maximize the performance of data compression, as evidenced by the results of a comparative study shown in Table I.…”