Posted by Hafiz Issah

New technologies, such as low-cost nodes and distributed acoustic sensing (DAS), are making it easier to continuously collect broadband, high-density seismic monitoring data. To reduce the time to move data from the field to computing centers, reduce archival requirements, and speed up interactive data analysis and visualization, lossy compression techniques can be employed to reduce the data’s volume while preserving essential information. An area of focus of my research is analyzing the errors introduced by various compression types and the extent to which these errors propagate in various geophysical processing workflows.

Recent research has primarily concentrated on understanding the extent to which compression errors influence outcomes, specifically in terms of detectability and the precision of microseismic event arrival time picks. We compare three types of lossy compression: sparse thresholded wavelet compression, zfp compression, and low-rank singular value decomposition compression. We apply these techniques to compare compression schemes on two publicly available datasets: an urban dark-fiber DAS experiment, and a surface DAS array above a geothermal field. We find that depending on the level of compression needed, and the importance of preserving large versus small seismic events, different compression schemes are preferable. This work is in the review process for publication in Seismological Research Letters but the manuscript is available on mines repository server at https://hdl.handle.net/11124/177939 . It is our goal that the code from this research hosted at https://github.com/aissah/Issah-SRL-compression-2023.git serves as a good tool for other researchers who wish to analyze lossy compressed seismic data.

Detection significance of events at various compression rates shows the detectability of events even at high compression rates.