5:15 PM - 6:45 PM
[STT36-P07] Love Wave Detection and FK Analysis of Distributed Acoustic Sensing Data (DAS) from the Sakurajima Network
Keywords:Distributed Acoustic Sensing (DAS), FK (frequency-wavenumber) analysis, Love waves detection, Sakurajima, Cross-correlation analysis
Seismic data analysis using FK (frequency-wavenumber) analysis is crucial in geophysics, offering profound insights into subsurface structures, source mechanisms and seismic wavefields across various domains such as oil and gas exploration, earthquake studies, and environmental monitoring. The emergence of Distributed Acoustic Sensing (DAS) has revolutionized seismic monitoring, providing continuous, high-resolution data capture. However, adapting FK analysis to DAS data presents unique challenges, particularly with directional sensitivity effects leading to features like multiple peaks in the FK spectrum.
This research introduces a novel method for extracting love waves from DAS data recordings of ambient noise and analyzing them using FK analysis to determine slowness and azimuth. Our DAS cable has a unique characteristic: at one part of the cable, it bends at a 90-degree angle. This bend causes the polarity of the love wave to be recorded as flipped in the second portion of the cable, after the bend. Leveraging this inherent property, our analysis automatically considers the polarity flip when detecting love waves, facilitating automated detection, and eliminating the need for manual inspection of the data.
Our methodology initiates the detection of love waves, by conducting cross-correlation analysis between each channel and the average of all channels before the bend, as well as between each channel and the average of all channels after the bend, within a 10-second window. The average cross-correlation coefficient (CCC) serves as a vital indicator of data quality. If the quality is insufficient, we will proceed to the next time window for analysis. Subsequently, we examine the cross-correlation coefficient (CCC) values between channel segments within the same time window, both before and after the bend, to identify polarity flips. These flips are indicative of the presence of love waves. Upon detecting love waves, FK analysis is performed to accurately determine slowness and azimuth. To address directional sensitivity issues in FK analysis, we employ the polarity flip method as utilized by Zhao et al. (2023). While Zhao et al. (2023) employs the polarity flip method to improve the accuracy of MUSIC (Multiple Signal Classification) beamforming for determining the source direction of ambient noise in DAS measurements, we opt to use it for FK analysis. Iteratively analyzing consecutive 10-second windows allows for the precise localization of love waves within the Sakurajima DAS data.
We have identified distinctive Love wave patterns within our dataset, particularly within the frequency band of 0.4 to 0.5 Hz. Initial analysis using the FK method on 46 samples of Love waves (approximately 6 samples per day) indicates that the waves are originating from the south. However, comprehensive trend interpretation is limited at this stage due to the preliminary nature of our analysis. Further investigation and data gathering are required to accurately determine the propagation patterns and any underlying trends.
In conclusion, this research significantly advances seismic monitoring techniques by introducing a novel approach for detecting and analyzing love waves in DAS data, providing valuable insights into seismic phenomena relevant to real-world applications.
This research introduces a novel method for extracting love waves from DAS data recordings of ambient noise and analyzing them using FK analysis to determine slowness and azimuth. Our DAS cable has a unique characteristic: at one part of the cable, it bends at a 90-degree angle. This bend causes the polarity of the love wave to be recorded as flipped in the second portion of the cable, after the bend. Leveraging this inherent property, our analysis automatically considers the polarity flip when detecting love waves, facilitating automated detection, and eliminating the need for manual inspection of the data.
Our methodology initiates the detection of love waves, by conducting cross-correlation analysis between each channel and the average of all channels before the bend, as well as between each channel and the average of all channels after the bend, within a 10-second window. The average cross-correlation coefficient (CCC) serves as a vital indicator of data quality. If the quality is insufficient, we will proceed to the next time window for analysis. Subsequently, we examine the cross-correlation coefficient (CCC) values between channel segments within the same time window, both before and after the bend, to identify polarity flips. These flips are indicative of the presence of love waves. Upon detecting love waves, FK analysis is performed to accurately determine slowness and azimuth. To address directional sensitivity issues in FK analysis, we employ the polarity flip method as utilized by Zhao et al. (2023). While Zhao et al. (2023) employs the polarity flip method to improve the accuracy of MUSIC (Multiple Signal Classification) beamforming for determining the source direction of ambient noise in DAS measurements, we opt to use it for FK analysis. Iteratively analyzing consecutive 10-second windows allows for the precise localization of love waves within the Sakurajima DAS data.
We have identified distinctive Love wave patterns within our dataset, particularly within the frequency band of 0.4 to 0.5 Hz. Initial analysis using the FK method on 46 samples of Love waves (approximately 6 samples per day) indicates that the waves are originating from the south. However, comprehensive trend interpretation is limited at this stage due to the preliminary nature of our analysis. Further investigation and data gathering are required to accurately determine the propagation patterns and any underlying trends.
In conclusion, this research significantly advances seismic monitoring techniques by introducing a novel approach for detecting and analyzing love waves in DAS data, providing valuable insights into seismic phenomena relevant to real-world applications.