A document by Regina Maass. Click on the document to view its contents.

A document by Lei Li. Click on the document to view its contents.

Seismology focuses on the study of earthquakes and associated phenomena to characterize seismic sources and Earth structure, which both are of immediate relevance to society. This article is composed of two independent views on the state of the ICON principles (Goldman et al., 2021) in seismology and reflects on the opportunities and challenges of adopting them from a different angle. Each perspective focuses on a different topic. Section 1 deals with the integration of multiscale and multidisciplinary observations, focusing on integrated and open approaches, whereas Section 2 discusses computing and open-source algorithms, reflecting coordinated, networked, and open principles. In the past century, seismology has benefited from two co-existing technological advancements - the emergence of new, more capable sensory systems and affordable and distributed computing infrastructure. Integrating multiple observations is a crucial strategy to improve the understanding of earthquake hazards. However, current efforts in making big datasets available and manageable lack coherence, which makes it challenging to implement initiatives that span different communities. Building on ongoing advancements in computing, machine learning algorithms have been revolutionizing the way of seismic data processing and interpretation. A community-driven approach to code management offers open and networked opportunities for young scholars to learn and contribute to a more sustainable approach to seismology. Investing in new sensors, more capable computing infrastructure, and open-source algorithms following the ICON principles will enable new discoveries across the Earth sciences.

A vast majority of marine geological research is based on academic seismic data collected with single-channel systems or short-offset multi-channel seismic cables, which often lack reflection moveout for conventional velocity analysis. Consequently, our understanding of earth processes often relies on seismic time sections, which hampers quantitative analysis in terms of depth, formation thicknesses, or dip angles of faults. In order to overcome these limitations, we present a robust diffraction extraction scheme that models and adaptively subtracts the reflected wavefield from the data. We use diffractions to estimate insightful wavefront attributes and perform wavefront tomography to obtain laterally resolved seismic velocity information in depth. Using diffraction focusing as a quality control tool, we perform an interpretation-driven refinement to derive a geologically plausible depth-velocity-model. In a final step, we perform depth migration to arrive at a spatial reconstruction of the shallow crust. Further, we focus the diffracted wavefield to demonstrate how these diffraction images can be used as physics-guided attribute maps to support the identification of faults and unconformities. We demonstrate the potential of this processing scheme by its application to a seismic line from the Santorini-Amorgos Tectonic Zone, located on the Hellenic Volcanic Arc, which is notorious for its catastrophic volcanic eruptions, earthquakes, and tsunamis. The resulting depth image allows a refined fault pattern delineation and, for the first time, a quantitative analysis of the basin stratigraphy. We conclude that diffraction-based data analysis is a decisive factor, especially when the acquisition geometry of seismic data does not allow conventional velocity analysis.