Big Data Seismology

Abstract

The discipline of seismology is based on observations of ground motion that are inherently undersampled in space and time. Our basic understanding of earthquake processes and our ability to resolve 4D Earth structure are fundamentally limited by data volume. Today, Big Data Seismology is an emergent revolution involving the use of large, data‐dense inquiries that is providing new opportunities to make fundamental advances in these areas. This article reviews recent scientific advances enabled by Big Data Seismology through the context of three major drivers: the development of new data‐dense sensor systems, improvements in computing, and the development of new types of techniques and algorithms. Each driver is explored in the context of both global and exploration seismology, alongside collaborative opportunities that combine the features of long‐duration data collections (common to global seismology) with dense networks of sensors (common to exploration seismology). The review explores some of the unique challenges and opportunities that Big Data Seismology presents, drawing on parallels from other fields facing similar issues. Finally, recent scientific findings enabled by dense seismic data sets are discussed, and we assess the opportunities for significant advances made possible with Big Data Seismology. This review is designed to be a primer for seismologists who are interested in getting up‐to‐speed with how the Big Data revolution is advancing the field of seismology.

A comprehensive suite of earthquake catalogues for the 2016-2017 Central Italy seismic sequence

“… 21 ). With these catalogues, there are many more properties about earthquake occurrence that can be studied in more detail 22 , such as earthquake triggering, interaction, and spatiotemporal clustering.…”

On the Use of High‐Resolution and Deep‐Learning Seismic Catalogs for Short‐Term Earthquake Forecasts: Potential Benefits and Current Limitations

“…Furthermore, since their compilation in real‐time conditions is now within reach (Zhu et al., 2022), enhanced seismic catalogs might ultimately boost the predictive skill of earthquake forecast models (Beroza et al., 2021). While the spatial resolution of current data sets is certainly impressive and allows describing in detail the cascading nature of earthquake occurrence (Arrowsmith et al., 2022) as well as fault geometries (Waldhauser et al., 2021a), how modelers should use the information encoded in those data products is not straightforward. For example, it is unclear how the high‐resolution details about the seismicity evolution can be adequately captured to improve probabilistic models that inform operational earthquake forecasts (Jordan et al., 2011).…”

Sensitivity, Accuracy and Limits of the Lightweight Three-Component SmartSolo Geophone Sensor (5 Hz) for Seismological Applications

“…Recent developments in seismological research have seen tremendous increases in sheer size of data throughout the last decade (Quinteros et al, 2021;Arrowsmith et al, 2022). This evolution has been accompanied by increasing computational power enabling the processing of such large data-sets (Ahrens et al, 2011;Bozdag et al, 2014;MacCarthy et al, 2020) and the introduction of Machine Learning techniques for seismological data processing (Bergen et al, 2019;Kong et al, 2019;Arrowsmith et al, 2022).…”

“…Recent developments in seismological research have seen tremendous increases in sheer size of data throughout the last decade (Quinteros et al, 2021;Arrowsmith et al, 2022). This evolution has been accompanied by increasing computational power enabling the processing of such large data-sets (Ahrens et al, 2011;Bozdag et al, 2014;MacCarthy et al, 2020) and the introduction of Machine Learning techniques for seismological data processing (Bergen et al, 2019;Kong et al, 2019;Arrowsmith et al, 2022). On the hardware side, the introduction of low-cost geophone sensors (e.g., Raspberry Shake) often in combination with wholistic software/hardware solutions enabled data recording in unprecedented quantity of stations and for non-scientific audiences, for which the term "citizen science" has been introduced (Chen et al, 2020;Subedi et al, 2020;De Plaen et al, 2021;Calais et al, 2022).…”