Earthquakes and very deep groundwater perturbation mutually induced.
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ABSTRACT:
SUBMITTER: Kaown D
PROVIDER: S-EPMC8249596 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Earthquakes are among the most destructive natural events. The 6 April 2009, 6.3-Mw earthquake in L'Aquila (Italy) markedly altered the karstic Gran Sasso Aquifer (GSA) hydrogeology and geochemistry. The GSA groundwater invertebrate community is mainly comprised of small-bodied, colourless, blind microcrustaceans. We compared abiotic and biotic data from two pre-earthquake and one post-earthquake complete but non-contiguous hydrological years to investigate the effects of the 2009 earthquake on the dominant copepod component of the obligate groundwater fauna. Our results suggest that the massive earthquake-induced aquifer strain biotriggered a flushing of groundwater fauna, with a dramatic decrease in subterranean species abundance. Population turnover rates appeared to have crashed, no longer replenishing the long-standing communities from aquifer fractures, and the aquifer became almost totally deprived of animal life. Groundwater communities are notorious for their low resilience. Therefore, any major disturbance that negatively impacts survival or reproduction may lead to local extinction of species, most of them being the only survivors of phylogenetic lineages extinct at the Earth surface. Given the ecological key role played by the subterranean fauna as decomposers of organic matter and "ecosystem engineers", we urge more detailed, long-term studies on the effect of major disturbances to groundwater ecosystems.
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Project description:Slow earthquakes may trigger failure on neighboring locked faults that are stressed sufficiently to break, and slow slip patterns may evolve before a nearby great earthquake. However, even in the clearest cases such as Cascadia, slow earthquakes and associated tremor have only been observed in intermittent and discrete bursts. By training a convolutional neural network to detect known tremor on a single seismic station in Cascadia, we isolate and identify tremor and slip preceding and following known larger slow events. The deep neural network can be used for the detection of quasi-continuous tremor, providing a proxy that quantifies the slow slip rate. Furthermore, the model trained in Cascadia recognizes tremor in other subduction zones and also along the San Andreas Fault at Parkfield, suggesting a universality of waveform characteristics and source processes, as posited from experiments and theory.
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Project description:The accurate and automated determination of small earthquake (ML?<?3.0) locations is still a challenging endeavor due to low signal-to-noise ratio in data. However, such information is critical for monitoring seismic activity and assessing potential hazards. In particular, earthquakes caused by industrial injection have become a public concern, and regulators need a solid capability for estimating small earthquakes that may trigger the action requirements for operators to follow in real time. In this study, we develop a fully convolutional network and locate earthquakes induced during oil and gas operations in Oklahoma with data from 30 network stations. The network is trained by 1,013 cataloged events (ML???3.0) as base data along with augmented data accounting for smaller events (3.0?>?ML???0.5), and the output is a 3D volume of the event location probability in the Earth. The prediction results suggest that the mean epicenter errors of the testing events (ML???1.5) vary from 3.7 to 6.4?km, meeting the need of the traffic light system in Oklahoma, but smaller events (ML?=?1.0, 0.5) show errors larger than 11?km. Synthetic tests suggest that the accuracy of ground truth from catalog affects the prediction results. Correct ground truth leads to a mean epicenter error of 2.0?km in predictions, but adding a mean location error of 6.3?km to ground truth causes a mean epicenter error of 4.9?km. The automated system is able to distinguish certain interfered events or events out of the monitoring zone based on the output probability estimate. It requires approximately one hundredth of a second to locate an event without the need for any velocity model or human interference.
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Project description:Recent studies of slow earthquakes along plate boundaries have shown that tectonic tremor, low-frequency earthquakes, very-low-frequency events (VLFEs), and slow-slip events (SSEs) often accompany each other and appear to share common source faults. However, the source processes of slow events occurring in the shallow part of plate boundaries are not well known because seismic observations have been limited to land-based stations, which offer poor resolution beneath offshore plate boundaries. Here we use data obtained from seafloor observation networks in the Nankai trough, southwest of Japan, to investigate shallow VLFEs in detail. Coincident with the VLFE activity, signals indicative of shallow SSEs were detected by geodetic observations at seafloor borehole observatories in the same region. We find that the shallow VLFEs and SSEs share common source regions and almost identical time histories of moment release. We conclude that these slow events arise from the same fault slip and that VLFEs represent relatively high-frequency fluctuations of slip during SSEs.
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Project description:Understanding the physical mechanisms that underpin the link between fluid injection and seismicity is essential in efforts to mitigate the seismic risk associated with subsurface technologies. To that end, here we develop a poroelastic model of earthquake nucleation based on rate-and-state friction in the manner of spring-sliders, and analyze conditions for the emergence of stick-slip frictional instability-the mechanism for earthquakes-by carrying out a linear stability analysis and nonlinear simulations. We find that the likelihood of triggering earthquakes depends largely on the rate of increase in pore pressure rather than its magnitude. Consequently, fluid injection at constant rate acts in the direction of triggering seismic rupture at early times followed by aseismic creep at late times. Our model implies that, for the same cumulative volume of injected fluid, an abrupt high-rate injection protocol is likely to increase the seismic risk whereas a gradual step-up protocol is likely to decrease it.
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Project description:This study aimed to determine if biotic contaminants originating from pig production farms are disseminated into soil and groundwater microbial communities. A spatial and temporal sampling of soil and groundwater in proximity to pig production farms was conducted, and quantitative PCR (Q-PCR) was utilized to determine the abundances of tetracycline resistance genes (i.e., tetQ and tetZ) and integrase genes (i.e., intI1 and intI2). We observed that the abundances of tetZ, tetQ, intI1, and intI2 in the soils increased at least 6-fold after manure application, and their abundances remained elevated above the background for up to 16 months. Q-PCR further determined total abundances of up to 5.88 × 10(9) copies/ng DNA for tetZ, tetQ, intI1, and intI2 in some of the groundwater wells that were situated next to the manure lagoon and in the facility well used to supply water for one of the farms. We further utilized 16S rRNA-based pyrosequencing to assess the microbial communities, and our comparative analyses suggest that most of the soil samples collected before and after manure application did not change significantly, sharing a high Bray-Curtis similarity of 78.5%. In contrast, an increase in Bacteroidetes and sulfur-oxidizing bacterial populations was observed in the groundwaters collected from lagoon-associated groundwater wells. Genera associated with opportunistic human and animal pathogens, such as Acinetobacter, Arcobacter, Yersinia, and Coxiella, were detected in some of the manure-treated soils and affected groundwater wells. Feces-associated bacteria such as Streptococcus, Erysipelothrix, and Bacteroides were detected in the manure, soil, and groundwater ecosystems, suggesting a perturbation of the soil and groundwater environments by invader species from pig production activities.
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