Dataset Information

Hydrologically-driven crustal stresses and seismicity in the New Madrid Seismic Zone.

ABSTRACT: The degree to which short-term non-tectonic processes, either natural and anthropogenic, influence the occurrence of earthquakes in active tectonic settings or 'stable' plate interiors, remains a subject of debate. Recent work in plate-boundary regions demonstrates the capacity for long-wavelength changes in continental water storage to produce observable surface deformation, induce crustal stresses and modulate seismicity rates. Here we show that a significant variation in the rate of microearthquakes in the intraplate New Madrid Seismic Zone at annual and multi-annual timescales coincides with hydrological loading in the upper Mississippi embayment. We demonstrate that this loading, which results in geodetically observed surface deformation, induces stresses within the lithosphere that, although of small amplitude, modulate the ongoing seismicity of the New Madrid region. Correspondence between surface deformation, hydrological loading and seismicity rates at both annual and multi-annual timescales indicates that seismicity variations are the direct result of elastic stresses induced by the water load.

SUBMITTER: Craig TJ

PROVIDER: S-EPMC5732252 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature

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Publications

Hydrologically-driven crustal stresses and seismicity in the New Madrid Seismic Zone.

Craig Timothy J TJ Chanard Kristel K Calais Eric E

Nature communications 20171215 1

The degree to which short-term non-tectonic processes, either natural and anthropogenic, influence the occurrence of earthquakes in active tectonic settings or 'stable' plate interiors, remains a subject of debate. Recent work in plate-boundary regions demonstrates the capacity for long-wavelength changes in continental water storage to produce observable surface deformation, induce crustal stresses and modulate seismicity rates. Here we show that a significant variation in the rate of microeart ...[more]

PMID: 29247158

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Project description:Monitoring mining-induced seismicity (MIS) can help engineers understand the rock mass response to resource extraction. With a thorough understanding of ongoing geomechanical processes, engineers can operate mines, especially those mines with the propensity for rock-bursting, more safely and efficiently. Unfortunately, processing MIS data usually requires significant effort from human analysts, which can result in substantial costs and time commitments. The problem is exacerbated for operations that produce copious amounts of MIS, such as mines with high-stress and/or extraction ratios. Recently, deep learning methods have shown the ability to significantly improve the quality of automated arrival-time picking on earthquake data recorded by regional seismic networks. However, relatively little has been published on applying these techniques to MIS. In this study, we compare the performance of a convolutional neural network (CNN) originally trained to pick arrival times on the Southern California Seismic Network (SCSN) to that of human analysts on coal-mine-related MIS. We perform comparisons on several coal-related MIS data sets recorded at various network scales, sampling rates and mines. We find that the Southern-California-trained CNN does not perform well on any of our data sets without retraining. However, applying the concept of transfer learning, we retrain the SCSN model with relatively little MIS data after which the CNN performs nearly as well as a human analyst. When retrained with data from a single analyst, the analyst-CNN pick time residual variance is lower than the variance observed between human analysts. We also compare the retrained CNN to a simpler, optimized picking algorithm, which falls short of the CNN's performance. We conclude that CNNs can achieve a significant improvement in automated phase picking although some data set-specific training will usually be required. Moreover, initializing training with weights found from other, even very different, data sets can greatly reduce the amount of training data required to achieve a given performance threshold.

Dataset Information

Hydrologically-driven crustal stresses and seismicity in the New Madrid Seismic Zone.

Publications

Hydrologically-driven crustal stresses and seismicity in the New Madrid Seismic Zone.

Similar Datasets

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