This paper develops a new framework for data compression in seismic sensor networks by
using the distributed principal component analysis (DPCA). The proposed DPCA scheme …

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 …

Principal Component Analysis (PCA) offers an optimal dimensionality reduction while
maintaining the variances. A set of seismic traces data recorded by a sensor can be …

This paper proposes a distributed principal component analysis seismic data compression
algorithm based on fidelity restriction for non-cable seismic exploration systems. The …

Seismic data files in SEGY format can be of substantial size as these contain generally
hundreds of traces collected from multiple shots. The data is usually transmitted through …

The next generation of oil and gas exploration technology is moving toward large-scale
seismic acquisition, automation, and flexibility. This phenomenon has accelerated the …

Seismic data processing to interpret subsurface features is both computationally and data
intensive. It is necessary to keep the dimensionality of data as small as possible, for good …

For some classes of signals, particularly those dominated by low frequency components,
such as seismic data first and higher order differences between adjacent signal samples are …

Seismic data provides integral information in geophysical exploration, for locating
hydrocarbon rich areas as well as for fracture monitoring during well stimulation. Because of …

In this paper, we rigorously compare compressive sampling (CS) to four state of the art, on-
mote, lossy compression algorithms [K-run-length encoding (KRLE), lightweight temporal …