New high-resolution 3D imagery of the malfunction area shows how fluid movement causes an earthquake swarm.
Quote: Pierce Onus, J. 2020. Filming from 22,000 small earthquakes reveal the complexities of the fault area, Templore, http://doi.org/10.32858/temblor.099
Cahwela Tewanit Vista Point, Santa Rosa Monument and the San Jacinto National Mountains. Credit: KenLundCCBSA20
Scientists are constantly monitoring the activity of the fault area across the country. Sometimes, they hear from audience members who notice seismic activity and want explanations. Always, when scientists receive these inquiries, they have an explanation or at least actually monitor the activity. However, in early 2018, a member of the public contacted scientists in Southern California before scientists noticed an increasing pattern of low-volume earthquakes known as swarms. The inquiry eventually reached the office of Zakari Ross, the geophysicist in Caltec. When he and his colleagues start looking at the query, they certainly notice an ongoing earthquake swarm that started in 2016 near Kahwela, California, about halfway between the Elsinore Rift Zone and the San Jacinto Rift Zone.
The squadron was occurring near Kahuela, California, between the San Jacinto Fault Zone and the Elsinore Fault Zone. In the figure on the right, you can see the largest earthquakes as well as their general pattern, which together revealed a fault area of 50 meters wide. Credit: Ross et al., Sciences, 2020, used with permission of the author
Ross began tracking swarm and applying a new machine learning algorithm he was developing to examine the structure of errors and how earthquake swarms spread. The resulting study, published last week in Science, provides the most detailed error engineering maps to date.
Most earthquake swarms last for short periods – from hours to months. When Ross began to look at him, this swarm had been continuously active for more than a year.
Ross says “swarms” are driven by processes such as fluid moving across the Earth or seismic slip … and not controlled by a major or prominent event in the sequence. He adds that if there are large earthquakes, they do not often occur at the beginning of the swarm. The same is true for the Kahuela chain, which witnessed the largest earthquake to date, with a strength of 4.4, in August 2018.
What started it?
Although seismologists know that fluids are a possible cause of earthquake swarms and that invisible barriers and structures have an impact on fluid flow and earthquakes, previous two-dimensional models of general fault engineering lack details about the constraints on the processes that start, grow, and stop swarms. .
According to journalist and seismologist Catherine Miles, who was not involved in this study, part of the reason why seismologists are not able to predict earthquakes is that there is not a lot that we do not know about the structures of the fault region, how it works or even where all the errors occur, where it cannot Always noticed in the human eye.
With the new machine learning algorithm, scientists can, in much more detail, examine the properties and structures of error regions. This information provides a clearer view of the effects that the physical properties of fault regions have on earthquakes and seismic processes, and the changes that occur after seismic activity.
This bird’s eye view of the aftershocks in the underground, encoded in color according to the time it occurred, shows how earthquakes migrated during the swarm period. Credit: Ross et al., Sciences, 2020, used with permission of the author
Accurately determine earthquake activity
The algorithm takes advantage of more than 100 sensors that are placed near Elsinore and San Jacinto malfunction areas that provide information every second separately. Then the earthquake algorithm detects, identifies, collects and processes data automatically. Ross says this new process is more sensitive, and therefore can detect smaller earthquakes than previously observed. It can also accurately locate the earthquake and the fault zone. Initially, Ross used his new methodology to assess the earthquake swarm starting in 2019. When it proved successful in locating swarms accurately and providing more detailed imagery than previously available, he then used the algorithm to return and evaluate all data from 2016 to the present. The entire data set included approximately 22,000 individual earthquakes with a magnitude of 0.7 to 4.4.
The result was the most detailed three-dimensional images of the structure of the fault area to date. Ross and his colleagues reported that the squadron was built on a narrow rift zone, 50 meters wide, with steep curves. These detailed images facilitate the identification of whether seismic activity is caused by seismic slip or fluid injection.
Fluid movement causes earthquakes
The photos revealed that the cause of the Kahwela swarm is the injection of natural fluids from the underground reservoir and the subsequent migration of the liquid along the curves of the fault zone once the reservoir begins to leak. The data also indicates that all earthquake events were migrating away from one point an average of 5 meters per day during the four-year period. Ross and his colleagues suggest that this is the only source of fluid injection.
The photos revealed that the Kahwela swarm was run by injecting natural fluids from an invisible underground reservoir so far it had begun to leak in one way or another. Subsequent earthquakes were caused by fluid migration along the curves of the fault zone. Credit: Ross et al., Sciences, 2020, used with permission of the author
The images allowed the team to see that the physical structure of the error affected the earthquake swarm. In the past 18 months, the migration rate has slowed dramatically but has not stopped yet. “Until this point, in four years, it is the longest squad we have recorded in Southern California dating back 40 years or more,” says Ross.
Seismologists have speculated that earthquakes move through a fault zone that does not always move in the same direction and at the same speed, says John Vidali, a University of Southern California seismologist who has not been involved in the new study. Since Ross and his colleagues can paint these earthquakes well, they can notice differences in movement and speculation when this is due to the physical structure of the fault zone.
Thanks to the improved resolution of images produced by the new Ross algorithm, Vidal says, scientists can better observe the complex sequences resulting from the dynamic physical properties of the defect regions. Additionally, he says, information about stress drops – the difference between stress by error before and after the earthquake – will help researchers see more clearly when they attribute changes in stress drops to the physical structure of the fault area, changes in fluid movement or other causes. This data adds more information about earthquake causes to the current list of earthquake information that can be used later to forecast hazards.
Vidale says, further research could include discovering how fluids flow as there is no seismic to track its progress. “We would like to know where fluid pressure changes because that can tell us where there is an increased probability of serious earthquakes.”
Want to know your earthquake risk? Check it out at Temblor.
Ross, ZE, Cochran, ES, Trojman, DT, and Smith, JD (2020). 3D architecture controls the dynamics of earthquake swarms. Science, 368 (6497), 1357-1361. https://doi.org/10.1126/science.abb0779.
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