[1] Earthquakes and the faults upon which they occur interact over a wide range of spatial and temporal scales. In addition, many aspects of regional seismicity appear to be stochastic both in space and time. However, within this complexity, there is considerable self-organization. We argue that the occurrence of earthquakes is a problem that can be attacked using the fundamentals of statistical physics. Concepts of statistical physics associated with phase changes and critical points have been successfully applied to a variety of cellular automata models. Examples include sandpile models, forest fire models, and, particularly, slider block models. These models exhibit avalanche behavior very similar to observed seismicity. A fundamental question is whether variations in seismicity can be used to successfully forecast the occurrence of earthquakes. Several attempts have been made to utilize precursory seismic activation and quiescence to make earthquake forecasts, some of which show promise.

https://www.eoas.ubc.ca/~mjelline/453website/eosc453/E_prints/2003RG000135.pdf

Statistical physics approach to understanding the multiscale dynamics of earthquake fault systems

Linear Differential Operators By Prof. Cornelius Lanczos. Pp. xvi + 564. (London: D. Van Nostrand Co., Ltd.; New York: D. Van Nostrand Company, Inc., 1961.) 80s.

Linear Differential Operators

The moment magnitude (Mw) 5.5 earthquake that struck South Korea in November 2017 was one of the largest and most damaging events in that country over the past century. Its proximity to an enhanced geothermal system site, where high-pressure hydraulic injection had been performed during the previous 2 years, raises the possibility that this earthquake was anthropogenic. We have combined seismological and geodetic analyses to characterize the mainshock and its largest aftershocks, constrain the geometry of this seismic sequence, and shed light on its causal factors. According to our analysis, it seems plausible that the occurrence of this earthquake was influenced by the aforementioned industrial activities. Finally, we found that the earthquake transferred static stress to larger nearby faults, potentially increasing the seismic hazard in the area.

/pdf/the-november-2017-mw-5-5-pohang-earthquake-a-possible-case-3oleaqfo4s.pdf

The November 2017 Mw 5.5 Pohang earthquake: A possible case of induced seismicity in South Korea

NASA’s InSight (Interior exploration using Seismic Investigations, Geodesy and Heat Transport) mission landed in Elysium Planitia on Mars on 26 November 2018. It aims to determine the interior structure, composition and thermal state of Mars, as well as constrain present-day seismicity and impact cratering rates. Such information is key to understanding the differentiation and subsequent thermal evolution of Mars, and thus the forces that shape the planet’s surface geology and volatile processes. Here we report an overview of the first ten months of geophysical observations by InSight. As of 30 September 2019, 174 seismic events have been recorded by the lander’s seismometer, including over 20 events of moment magnitude Mw = 3–4. The detections thus far are consistent with tectonic origins, with no impact-induced seismicity yet observed, and indicate a seismically active planet. An assessment of these detections suggests that the frequency of global seismic events below approximately Mw = 3 is similar to that of terrestrial intraplate seismic activity, but there are fewer larger quakes; no quakes exceeding Mw = 4 have been observed. The lander’s other instruments—two cameras, atmospheric pressure, temperature and wind sensors, a magnetometer and a radiometer—have yielded much more than the intended supporting data for seismometer noise characterization: magnetic field measurements indicate a local magnetic field that is ten-times stronger than orbital estimates and meteorological measurements reveal a more dynamic atmosphere than expected, hosting baroclinic and gravity waves and convective vortices. With the mission due to last for an entire Martian year or longer, these results will be built on by further measurements by the InSight lander. Geophysical and meteorological measurements by NASA’s InSight lander on Mars reveal a planet that is seismically active and provide information about the interior, surface and atmospheric workings of Mars.

/pdf/initial-results-from-the-insight-mission-on-mars-53b9ay0igc.pdf

Initial results from the InSight mission on Mars

This article provides an overview of current applications of machine learning (ML) in seismology. ML techniques are becoming increasingly widespread in seismology, with applications ranging from identifying unseen signals and patterns to extracting features that might improve our physical understanding. The survey of the applications in seismology presented here serves as a catalyst for further use of ML. Five research areas in seismology are surveyed in which ML classification, regression, clustering algorithms show promise: earthquake detection and phase picking, earthquake early warning (EEW), ground‐motion prediction, seismic tomography, and earthquake geodesy. We conclude by discussing the need for a hybrid approach combining data‐driven ML with traditional physical modeling.

Machine Learning in Seismology: Turning Data into Insights

We analyze the performance of a prototype Earthquake Early Warning system deployed at the National Seismological Network of Costa Rica in collaboration with the Swiss Seismological Service by presenting the real-time performance during six earthquakes (Mw 5.1-6.4) that took place during 2018 and 2019. We observe that, despite only limited efforts to optimize the existing network of 158 stations, for Earthquake Early Warning purposes, the network density allows fast determination of source parameters using both the Virtual Seismologist and the Finite Fault Rupture Detector algorithms. Shallow earthquakes on or near-shore are routinely identified within 11-20 s of their occurrence. The warning times for the capital city of San Jose are of 43 s for epicenters located at 220 km, like for the Mw 6.4 Armuelles earthquake. On the other hand, during the time analyzed, the Earthquake Early Warning system did not provide positive warning times for earthquakes at distances less than 40 km from San Jose. Even though large (Mw > 7) distant historical earthquakes have not caused heavy damage in San Jose, there is potential for developing an Earthquake Early Warning system for Costa Rica, especially for the purposes of rapid earthquake notifications, disaster response management, and seismic risk mitigation.

/pdf/preliminary-results-of-an-earthquake-early-warning-system-in-1c6h16j2em.pdf

Preliminary Results of an Earthquake Early Warning System in Costa Rica

Earthquake early warning systems utilize the capability of modern real-time systems to process and transmit information faster than seismic waves propagate (3–6 km/sec); they provide first information on forthcoming ground shaking prior to the arrival of seismic waves at potential user sites. The possible warning time ranges up to 70 seconds, depending on the distances between the seismic source, sensor and user sites. The main effort in the design and implementation of early warning systems in the last few years aims at increased warning times and thus at an expansion of areas that can be effectively warned. This requires new strategies and methods for the rapid characterization of earthquakes: on-site warning systems are based on observations at single stations for the quick estimate of source parameters. The on-site warning approach is significantly faster than network based regional strategies for early warning even though often at the expense of accuracy. We have developed an alternative method for earthquake early warning — called PreSEIS (pre-seismic shaking) — that is as fast as the on-site warning approach but combines information from several sensors within small seismic networks with apertures of about 100 kilometers to estimate source parameters from the first few seconds of seismic recordings. Therefore, it is not necessary for seismic waves to have arrived at all sensors before estimates can be issued because even a non-triggered station provides valuable information about the possible location of the seismic source and confines the space of possible solutions to the problem of a magnitude-distance trade-off. With on-going time, longer time series at more stations become available, allowing improved information to be inferred about the earthquake. PreSEIS is based on artificial neural networks. The algorithm will be demonstrated using the example of the mega-city Istanbul, Turkey.

A New Approach to Earthquake Early Warning

CISN ShakeAlert: The Decision Module for Earthquake Alerts

Evaluating and Improving Earthquake Early Warning in Central America

Earthquake early warning systems (EEWSs) aim to rapidly detect earthquakes and provide timely alerts, so that users can take protective actions prior to the onset of strong ground shaking. The promise and limitations of EEWSs have both been widely debated. On one hand, an operational EEWS could mitigate earthquake damage by triggering potentially cost- and life-saving actions. These range from automated system responses such as slowing down trains to the actions of individuals that receive the alerts and take protective measures. On the other hand, the effectiveness of an EEWS is conditional on the ability to issue warnings that are sufficiently accurate and timely to facilitate an appropriate action. The refinement of earthquake early warning (EEW) algorithms and the installation of denser and faster seismic networks have improved performance; however, the benefit in risk reduction that an EEWS could achieve remains unquantified. In this study, we leverage upon regional event-based probabilistic seismic risk assessment to devise a quantitative and fully customizable framework for evaluating the effectiveness of EEW in mitigating seismic risk. We demonstrate this framework using Switzerland as a testbed, for which we compute and contrast human loss exceedance curves with and without EEW.

https://doi.org/10.1177/87552930231153424

Maren Böse

Papers

Preliminary Results of an Earthquake Early Warning System in Costa Rica

A New Approach to Earthquake Early Warning

CISN ShakeAlert: The Decision Module for Earthquake Alerts

Evaluating and Improving Earthquake Early Warning in Central America

A framework to quantify the effectiveness of earthquake early warning in mitigating seismic risk