... and a database with high‐level helper functions in order to be pluggable and extensible. Finally, it must produce exportable data, either in waveform format, tabular text files, or high‐quality figures. This is the purpose of Monitoring using Seismic Noise (i.e., MSNoise). We do not want to provide another...

... and autocorrelated in 1 hr segments, stacked into daily average functions, and saved as 4 min long waveforms. We use the MSNoise software ( Lecocq et al. , 2014 ) for computing the noise cross‐correlation functions and measuring the seismic velocity changes. Following Clarke et al. (2011) , we excluded cross...

...Figure 6. (a) NoisePy performance on computing cross‐correlation functions and (b) benchmark with MSNoise. (a) Strong scaling of NoisePy, such that the compute time is inversely proportional to the number of cores with a slope close to − 1 . In the benchmark, NoisePy and MSNoise process...

... package MSNoise was used to compute crosscorrelations and measure changes in velocity between each time period relative to the initial (prestimulation) time period. The majority of channel pairs showed a velocity reduction (average −3% relative velocity change) following both stimulations. We used a 3D...

...Figure 7. Time series of δ v / v obtained from optimal stretch factors for the 50 s window of coda and using the cross‐spectral moving window techniques (CSMWT) capabilities included in MSNoise. The Pearson correlation coefficient ( r ) is indicated in the upper right. The color version...

... ) . We use MSNoise ( Lecocq et al. , 2014 ) to compute CCFs that estimate the Green’s tensor G i j from seismic data from 200 days of data between 6 February and 15 September 2014 ( Ensing et al. , 2017 ), cross correlating 1800 s time windows with a maximum lag of 120 s, as in equation  (1...

.... (2007) , and were done using the MSNoise software ( Lecocq et al. 2014 ). The next 3 steps were done using MSNoise-TOMO. For all stations (ArrayA and ArrayB) the raw waveforms are divided into daily (24 hour) segments, and were converted from cube to Miniseed, ASCII and SeiscomP Data Structures (SDS...

..., highly expressive language, and in recent years its large ecosystem of high‐quality scientific third‐party packages have been established as one of the most widely used languages across all sciences ( Perkel, 2015 ). Available packages in seismology include the widely used ObsPy library, MSNoise ( Lecocq...

... using the high‐level programming language Python; for example, Whisper ( Briand et al. , 2013 ) and MSNoise ( Lecocq et al. , 2014 ). Although Python eases development and is therefore suitable for working on new seismological concepts, a compiled language is clearly more suitable to harness...

.... (2) Let users manage and analyze data in a single language: Python. Thanks to data acquisition tools like ObsPyLoad and wavesdownloader ( Bernardi and Michelini, 2013 ) and analysis tools like ObsPy ( Beyreuther et al. , 2010 ; Megies et al. , 2011 ), AIMBAT ( Lou et al. , 2013 ), MSNoise...

... stations with the MSNoise software ( Lecocq et al. , 2014 ). This noise cross‐correlation procedure is identical to previous works ( Taira et al. , 2015 , 2018 ), and is applied to all available data collected from the current experiment. We also included seismic data from BK.BRK and BK.BKS...

... applied. This is mainly because preprocessing is done per station, and correlation and denoising operations are done per station pair. ObsPy ( Krischer et al. , 2015 ) and MSNoise ( Lecocq et al. , 2014 ) are open‐source packages for Python that are excellent for proof‐of‐concept studies and often...

... −104.3867 −82.6823 2343.5 The ambient seismic cross correlations among all the station pairs were computed using MSNoise ( Lecocq et al. , 2014 ). The continuous data were split into 400 s long windows with an overlap of 75%. The linear trend and the mean were subtracted from each window...

... interferometry of SPE‐5 observed on the SPE large‐ N array (see Mellors et al. , 2018 , for information on the large‐ N deployment). For the ambient noise study, 51 station pairs are used, with 36 pairs from the UNR network and 15 pairs from the SPE backbone network (Fig.  2 ). We used the MSNoise...