Deblending of simultaneous-source seismic data via periodicity-coded nonnegative matrix factorization

Article for IEEE MLSP 2017 DATA CHALLENGE

Deblending of simultaneous-source seismic data via periodicity-coded nonnegative matrix factorization

Tzu-Hao Lin, Yu Tsao
Research Center for Information Technology Innovation, Academia Sinica

Abstract

To increase the efficiency of seismic acquisition, one needs to break down the simultaneous-source seismic data into single source responses by a procedure called deblending. In this study, we employed the periodicity-coded non-negative matrix factorization (PC-NMF) to separate the primary and the secondary sources recorded in Petroleum Geo-Services dataset. Due to the application of random delay times between consecutive shots, the two sources displayed different patterns among various shots. By combining the response matrices of successive receivers, the PC-NMF can learn the basic components and group the basic components into two clusters according to the periodicity among shot index. Therefore, the deblending of simultaneous-source seismic data can be effectively achieved in an unsupervised manner.

The full paper can be accessed in IEEE DATA PORT.

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PNC 2017 Annual Conference and Joint Meetings

2017/11/7-9 @ Tainan, Taiwan

Computing biodiversity change via a soundscape monitoring network

Tzu-Hao Lin, Yu Tsao
Research Center for Information Technology Innovation, Academia Sinica

Yu-Huang Wang
Taiwan Academy of Ecology

Han-Wei Yen
Academia Sinica Grid Computing Centre

Sheng-Shan Lu
Taiwan Forestry Research Institute

A monitoring network for biodiversity change is essential for wildlife conservation. In recent years, many soundscape monitoring projects have been carried out to investigate the diversity of vocalizing animals. However, the acoustic-based biodiversity assessment remains challenging due to the lack of sufficient recognition database and the inability to disentangle mixed sound sources. Since 2014, an Asian Soundscape monitoring project has been initiated in Taiwan. So far, there are 15 recording sites in Taiwan and three sites in Southeast Asia, with more than 20,000 hours of recordings archived in the Asian Soundscape. In this study, we employed the visualization of long-duration recordings, blind source separation, and clustering techniques, to investigate the spatio-temporal variations of forest biodiversity in the Triangle Mountain, Lienhuachih, and Taipingshan. On the basis of blind source separation, biological sounds, with prominent diurnal occurrence pattern, can be separated from the environmental sounds without any recognition database. Thus, clusters of biological sounds can be effectively identified and employed to measure the daily change in bioacoustic diversity. Our results show that the bioacoustic diversity was higher in the evergreen broad-leaved forest. However, the seasonal variation in bioacoustic diversity was most evident in the high elevation coniferous forest. This study demonstrates that a suitable integration of machine learning and ecoacoustics can facilitate the evaluation of biodiversity changes. In addition to biological activities, we can also measure the environmental variability from soundscape information. In the future, the Asian Soundscape will not only serve as an open database for soundscape recordings, but also will provide tools for analyzing the interactions between biodiversity, environment, and human activities.

If you are interested in this research, please check the full paper published in PNC 2017.