ALBERT

Description: 〈span〉〈div〉ABSTRACT〈/div〉Seismic waves that are recorded by near‐surface sensors are usually disturbed by strong noise. Hence, the recorded seismic data are sometimes of poor quality; this phenomenon can be characterized as a low signal‐to‐noise ratio (SNR). The low SNR of the seismic data may lower the quality of many subsequent seismological analyses, such as inversion and imaging. Thus, the removal of unwanted seismic noise has significant importance. In this article, we intend to improve the SNR of many seismological datasets by developing new denoising framework that is based on an unsupervised machine‐learning technique. We leverage the unsupervised learning philosophy of the autoencoding method to adaptively learn the seismic signals from the noisy observations. This could potentially enable us to better represent the true seismic‐wave components. To mitigate the influence of the seismic noise on the learned features and suppress the trivial components associated with low‐amplitude neurons in the hidden layer, we introduce a sparsity constraint to the autoencoder neural network. The sparse autoencoder method introduced in this article is effective in attenuating the seismic noise. More importantly, it is capable of preserving subtle features of the data, while removing the spatially incoherent random noise. We apply the proposed denoising framework to a reflection seismic image, depth‐domain receiver function gather, and an earthquake stack dataset. The purpose of this study is to demonstrate the framework’s potential in real‐world applications.〈/span〉

Description: 〈span〉〈div〉ABSTRACT〈/div〉Rapid association of seismic phases and event location are crucial for real‐time seismic monitoring. We propose a new method, named rapid earthquake association and location (REAL), for associating seismic phases and locating seismic events rapidly, simultaneously, and automatically. REAL combines the advantages of both pick‐based and waveform‐based detection and location methods. It associates arrivals of different seismic phases and locates seismic events primarily through counting the number of 〈span〉P〈/span〉 and 〈span〉S〈/span〉 picks and secondarily from travel‐time residuals. A group of picks are associated with a particular earthquake if there are enough picks within the theoretical travel‐time windows. The location is determined to be at the grid point with the most picks, and if multiple locations have the same maximum number of picks, the grid point among them with smallest travel‐time residuals. We refine seismic locations using a least‐squares location method (VELEST) and a high‐precision relative location method (hypoDD). REAL can be used for rapid seismic characterization due to its computational efficiency. As an example application, we apply REAL to earthquakes in the 2016 central Apennines, Italy, earthquake sequence occurring during a five‐day period in October 2016, midway in time between the two largest earthquakes. We associate and locate more than three times as many events (3341) as are in Italy's National Institute of Geophysics and Volcanology routine catalog (862). The spatial distribution of these relocated earthquakes shows a similar but more concentrated pattern relative to the cataloged events. Our study demonstrates that it is possible to characterize seismicity automatically and quickly using REAL and seismic picks.〈/span〉

Description: The tectonic processes that formed the Tibetan plateau have been a significant topic in earth science, but images of the subducting Indian continental lithosphere (ICL) are still not clear enough to reveal detailed continental collision processes. Seismological methods are the primary ways to obtain images of deep crust and upper-mantle structures. However, previous temporary seismic stations have been unevenly distributed over central Tibet. The Institute of Geology and Geophysics, Chinese Academy of Sciences, has initiated a 2D broadband seismic network in central Tibet across the Bangong–Nujiang suture to fill in gaps among earlier north–south linear profiles for the purpose of detecting the lateral variation of the northern end of the subducting ICL. The health status for each station has been checked at each scheduled service trip. The noise level analysis shows a quiet background in central Tibet, with low cultural noise. Preliminary earthquake locations indicate that they are crustal and broadly distributed rather than only occurring along major faults, suggesting a diffused deformation in the conjugated strike-slip fault zone. Preliminary receiver function analysis shows a complicated crust with significant east–west lateral variations.

Description: 〈span〉〈div〉ABSTRACT〈/div〉Seismic waves that are recorded by near‐surface sensors are usually disturbed by strong noise. Hence, the recorded seismic data are sometimes of poor quality; this phenomenon can be characterized as a low signal‐to‐noise ratio (SNR). The low SNR of the seismic data may lower the quality of many subsequent seismological analyses, such as inversion and imaging. Thus, the removal of unwanted seismic noise has significant importance. In this article, we intend to improve the SNR of many seismological datasets by developing new denoising framework that is based on an unsupervised machine‐learning technique. We leverage the unsupervised learning philosophy of the autoencoding method to adaptively learn the seismic signals from the noisy observations. This could potentially enable us to better represent the true seismic‐wave components. To mitigate the influence of the seismic noise on the learned features and suppress the trivial components associated with low‐amplitude neurons in the hidden layer, we introduce a sparsity constraint to the autoencoder neural network. The sparse autoencoder method introduced in this article is effective in attenuating the seismic noise. More importantly, it is capable of preserving subtle features of the data, while removing the spatially incoherent random noise. We apply the proposed denoising framework to a reflection seismic image, depth‐domain receiver function gather, and an earthquake stack dataset. The purpose of this study is to demonstrate the framework’s potential in real‐world applications.〈/span〉