machine learning method and double-differencing technique  The machine learning method and waveform cross-correlation enabled systematic phase picking and reduced inconsistencies in catalog picking. Hypocentre patterns demonstrate clear spatial-temporal clustering with depth constrained within five km. High-resolution earthquake relocations and waveform similarity analysis revealed an unmapped fault that potentially led to the 2019 September Mw 5.0 event.

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 integrated, coordinated, open, networked (ICON) principles (Goldman et al., 2021, https://doi.org/10.1029/2021eo153180) 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.

Seismology is an applied discipline and integrates techniques and data from physics, mathematics, informatics, mineralogy, and geology. Seismological studies involve various natural and anthropogenic activities across a wide range of spatial and temporal scales, including tectonic plate motions, volcano eruptions, hydrocarbon exploration, carbon sequestration, mining, landslides, and laboratory stimulation experiments (Shearer, 2009;Stein & Wysession, 2003). Seismological observations can provide critical insights into the physical processes of the Earth's interior and the associated near-surface consequences (Cloetingh & Negendank, 2010; NASEM, 2020). The multidisciplinary nature and multiscale observations of seismological studies embody integration, corresponding to the "I" in ICON science. There are many seismological data centers, such as the International Federation of Digital Seismograph Networks (https://www.fdsn.org/about/) and the Incorporated Research Institutions for Seismology Data Management Center (IRISDMC, https://ds.iris.edu/ds/), providing findable, accessible, interoperable, and reusable (FAIR) seismic data collected at local, regional to global scales, which generally comply