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

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

The bottom of Earth’s mantle hosts strong seismic wave speed heterogeneities, such as ultra-low velocity zones (ULVZs). ULVZ heterogeneities are commonly detected via forward modeling of seismic waveforms, which can include time-consuming waveform synthesis and visual inspection. Furthermore, ULVZ imaging has been most commonly carried out with waves that have limited global coverage. In this work, we investigate the efficacy of the diffracted S (Sdiff) wavefield, which has global coverage to map CMB heterogeneity. We implement a Kirchhoff migration algorithm to objectively investigate the presence or absence of postcursors to Sdiff due to ULVZ heterogeneity. The Kirchhoff approach is efficient, taking less than one CPU-minute per earthquake (for ~1000 receivers) for our implementation. Our approach makes use of the expected moveout of ULVZ-born Sdiff post cursors as a function of distance from great-circle path at the base of the mantle. We investigate epicentral distances 95°, where Sdiff includes asymptotic S and ScS up to diffraction. We test the algorithm using synthetic waveforms calculated for models that include lowermost mantle wave speed heterogeneity. These results demonstrate that the migration approach can well resolve the location of heterogeneity structures in the azimuthal direction, but is less accurate at constraining the along-great circle path location in the absence of crossing ray paths. Lastly, our real-data examples detect CMB heterogeneity that agrees with past ULVZ work. Our algorithm provides a quantitative assessment of the magnitude of the waveform anomalies and, therefore, how anomalous the structures are that are producing them.

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.