Source characterization is a fundamental task of passive seismic monitoring. Spatial-temporal evolution of both, point sources and finite-fault source, provides essential information for timely seismic hazard management and advanced analysis of the seismicity in the monitored areas. In the last few decades, the rise of dense seismic arrays, increase of high-performance computing resources, and development of advanced array-based techniques lead to studies using recorded wavefields in great detail. Full waveform inversion can invert passive seismic source parameters with an iterative framework, which connects the delay-and-sum imaging technique and kernel-based inversion strategy. Moreover, emerging technologies like distributed acoustic sensing and machine learning also have great potential in advancing passive seismic imaging and source characterization. Besides, non-earthquake sources and ambient noise, as unconventional and passive sources, are also undergoing rapid development in infrastructure monitoring and subsurface imaging, due to the emergence of sensitive sensors and modern techniques like seismic interferometry.
Advanced source characterization techniques have been successfully utilized for different purposes varying from laboratory acoustic emission events and mining-induced seismicity to reservoir-related microearthquakes and regional earthquakes. An improved characterization of passive seismic sources is beneficial to a better understanding of source physical properties, including both industry-related seismicity and tectonic-related earthquakes. However, there are still many new challenges and opportunities in this field. For example, the full potential of dense arrays requires further exploration, the influence of different factors on source characterization at different scales has not been thoroughly studied, and the applicability and performance of machine learning algorithms in seismic source inversion and imaging require more investigations.
The aim of this Research Topic is to collect theoretical and methodological progress related to passive seismic source characterization associated with different scenarios, and promote development and application of advanced seismic source imaging and inversion at different scales. We welcome submissions of research articles, case studies, and reviews on the related topics, but not limited to:
• Novel methods and frameworks for source imaging and characterization of both point sources and finite-fault sources
• New understandings of passive seismic source characterization associated with different scenarios and scales
• Comparison, evaluation, and benchmark studies for passive seismic source imaging and inversion
• Application of advanced frameworks involving passive seismic source characterization, including natural and anthropogenic earthquakes, non-earthquake sources, and ambient noise
• Application of Artificial Intelligence on passive seismic data
Source characterization is a fundamental task of passive seismic monitoring. Spatial-temporal evolution of both, point sources and finite-fault source, provides essential information for timely seismic hazard management and advanced analysis of the seismicity in the monitored areas. In the last few decades, the rise of dense seismic arrays, increase of high-performance computing resources, and development of advanced array-based techniques lead to studies using recorded wavefields in great detail. Full waveform inversion can invert passive seismic source parameters with an iterative framework, which connects the delay-and-sum imaging technique and kernel-based inversion strategy. Moreover, emerging technologies like distributed acoustic sensing and machine learning also have great potential in advancing passive seismic imaging and source characterization. Besides, non-earthquake sources and ambient noise, as unconventional and passive sources, are also undergoing rapid development in infrastructure monitoring and subsurface imaging, due to the emergence of sensitive sensors and modern techniques like seismic interferometry.
Advanced source characterization techniques have been successfully utilized for different purposes varying from laboratory acoustic emission events and mining-induced seismicity to reservoir-related microearthquakes and regional earthquakes. An improved characterization of passive seismic sources is beneficial to a better understanding of source physical properties, including both industry-related seismicity and tectonic-related earthquakes. However, there are still many new challenges and opportunities in this field. For example, the full potential of dense arrays requires further exploration, the influence of different factors on source characterization at different scales has not been thoroughly studied, and the applicability and performance of machine learning algorithms in seismic source inversion and imaging require more investigations.
The aim of this Research Topic is to collect theoretical and methodological progress related to passive seismic source characterization associated with different scenarios, and promote development and application of advanced seismic source imaging and inversion at different scales. We welcome submissions of research articles, case studies, and reviews on the related topics, but not limited to:
• Novel methods and frameworks for source imaging and characterization of both point sources and finite-fault sources
• New understandings of passive seismic source characterization associated with different scenarios and scales
• Comparison, evaluation, and benchmark studies for passive seismic source imaging and inversion
• Application of advanced frameworks involving passive seismic source characterization, including natural and anthropogenic earthquakes, non-earthquake sources, and ambient noise
• Application of Artificial Intelligence on passive seismic data
