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- Korpela, Kalevi M., et al.
(author)
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Environmental Strategies of Affect Regulation and Their Associations With Subjective Well-Being
- 2018
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In: Frontiers in Psychology. - : Frontiers Media SA. - 1664-1078. ; 9
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Journal article (peer-reviewed)abstract
- Environmental strategies of affect regulation refer to the use of natural and urban socio-physical settings in the service of regulation. We investigated the perceived use and efficacy of environmental strategies for regulation of general affect and sadness, considering them in relation to other affect regulation strategies and to subjective well-being. Participants from Australia, Finland, Germany, Great Britain, Italy, India, the Netherlands, Portugal, and Sweden (N = 507) evaluated the frequency of use and perceived efficacy of affect regulation strategies using a modified version of the Measure of Affect Regulation Styles (MARS). The internet survey also included the Satisfaction with Life Scale (SWLS), emotional well-being items from the RAND 36-Item Health Survey, and a single-item measure of perceived general health. Environmental regulation formed a separate factor of affect regulation in the exploratory structural equation models (ESEM). Although no relations of environmental strategies with emotional well-being were found, both the perceived frequency of use and efficacy of environmental strategies were positively related to perceived health. Moreover, the perceived efficacy of environmental strategies was positively related to life satisfaction in regulating sadness. The results encourage more explicit treatment of environmental strategies in research on affect regulation.
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- Lahivaara, Timo, et al.
(author)
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Estimation of groundwater storage from seismic data using deep learning
- 2019
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In: Geophysical Prospecting. - : WILEY. - 0016-8025 .- 1365-2478. ; 67:8, s. 2115-2126
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Journal article (peer-reviewed)abstract
- Convolutional neural networks can provide a potential framework to characterize groundwater storage from seismic data. Estimation of key components, such as the amount of groundwater stored in an aquifer and delineate water table level, from active-source seismic data are performed in this study. The data to train, validate and test the neural networks are obtained by solving wave propagation in a coupled poroviscoelastic-elastic media. A discontinuous Galerkin method is applied to model wave propagation, whereas a deep convolutional neural network is used for the parameter estimation problem. In the numerical experiment, the primary unknowns estimated are the amount of stored groundwater and water table level, while the remaining parameters, assumed to be of less of interest, are marginalized in the convolutional neural network-based solution. Results, obtained through synthetic data, illustrate the potential of deep learning methods to extract additional aquifer information from seismic data, which otherwise would be impossible based on a set of reflection seismic sections or velocity tomograms.
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