Microseismicity Associated with Actively Exploited Geothermal Systems: Earthquake Detection and Probabilistic Location at Rotokawa and Statistical Seismic Network Design at Kawerau

<p>Geothermal power has progressively been recognised as an important energy resource due to the depletion of old power sources, and as a more environmentally aware population pushes for an increase in renewable energy sources. Monitoring microseismicity occurring in active geothermal systems is one means of both characterising the system’s fault architecture and characterising fluid/rock interaction in response to production. This study focuses on better understanding seismicity in two active geothermal fields, through the development and implementation of two different algorithms: an automated microearthquake detection algorithm using a matched filter technique (improving earthquake detection), and an optimal seismic network design algorithm (improving earthquake location). Both algorithms have been implemented in codes that are easily adaptable to other data sets. The first of these algorithms has been applied to five months of continuous seismic waveform data spanning a fluid injection operation in the Rotokawa geothermal field. The cross-correlation of 14 high-quality master events with the continuous seismic data yields 2461 newly detected earthquakes spanning the magnitude range M=-0.4 to M=2.6 with a mean magnitude of M=0.47. The earthquakes detected with each master event exhibit high waveform similarity over approximately three orders of magnitude, and appear to follow a Gutenberg-Richter power law with a catalogue completeness down to M~ 0. Hypocentres for these detected events computed using the probabilistic earthquake location algorithm NonLinLoc reveal the dominant locus of seismicity to lie between 1.0–2.5 km depth, a location consistent with that of the Rotokawa Andesite which forms the Rotokawa reservoir. Focal mechanism solutions for the master events are predominantly normal, with half displaying a large strike-slip component, and the stress parameters obtained for this suite of focal mechanisms imply a northeast–southwest oriented maximum horizontal stress: both of these results are consistent with the extensional regime of the TVZ. Seismicity occurring within a 300 m horizontal radius of the injection well’s feed-zones, and extending to 5 km depth, initially exhibits a correlation with injection flow rates with a ~ 2 day lag, and seismicity rates decrease ~ 10 weeks after injection. We surmise that seismicity within the injection region and close to the injection well is likely to be injection-induced, with one portion of the injectate returning to the production region, while the other either migrates southeastward out of the field or remains within the injection region; the origin of seismicity within the production region in relationship to production and injection processes is unclear. The second of these algorithms involves the derivation of a design criterion, which we apply to inform the expansion of the existing seismic monitoring programme at Kawerau geothermal field; we also apply an early version to the short-term/rapid-response network design following the M7.1 September 2010 Darfield earthquake. Unlike previous seismic network design algorithms, the new algorithm incorporates methods for the realistic representation of 3D velocity structures and attenuation models for both P and S travel times, a surface noise model, and the ability to apply complex weighting functions to the earthquake set. The results demonstrate the utility of this algorithm in even simplistic cases, and show how each new parameter incorporated into the design model affects the optimal network design obtained, identifying the need for accurate input data to provide optimal results.</p>

Microseismicity Associated with Actively Exploited Geothermal Systems: Earthquake Detection and Probabilistic Location at Rotokawa and Statistical Seismic Network Design at Kawerau

<p>Geothermal power has progressively been recognised as an important energy resource due to the depletion of old power sources, and as a more environmentally aware population pushes for an increase in renewable energy sources. Monitoring microseismicity occurring in active geothermal systems is one means of both characterising the system’s fault architecture and characterising fluid/rock interaction in response to production. This study focuses on better understanding seismicity in two active geothermal fields, through the development and implementation of two different algorithms: an automated microearthquake detection algorithm using a matched filter technique (improving earthquake detection), and an optimal seismic network design algorithm (improving earthquake location). Both algorithms have been implemented in codes that are easily adaptable to other data sets. The first of these algorithms has been applied to five months of continuous seismic waveform data spanning a fluid injection operation in the Rotokawa geothermal field. The cross-correlation of 14 high-quality master events with the continuous seismic data yields 2461 newly detected earthquakes spanning the magnitude range M=-0.4 to M=2.6 with a mean magnitude of M=0.47. The earthquakes detected with each master event exhibit high waveform similarity over approximately three orders of magnitude, and appear to follow a Gutenberg-Richter power law with a catalogue completeness down to M~ 0. Hypocentres for these detected events computed using the probabilistic earthquake location algorithm NonLinLoc reveal the dominant locus of seismicity to lie between 1.0–2.5 km depth, a location consistent with that of the Rotokawa Andesite which forms the Rotokawa reservoir. Focal mechanism solutions for the master events are predominantly normal, with half displaying a large strike-slip component, and the stress parameters obtained for this suite of focal mechanisms imply a northeast–southwest oriented maximum horizontal stress: both of these results are consistent with the extensional regime of the TVZ. Seismicity occurring within a 300 m horizontal radius of the injection well’s feed-zones, and extending to 5 km depth, initially exhibits a correlation with injection flow rates with a ~ 2 day lag, and seismicity rates decrease ~ 10 weeks after injection. We surmise that seismicity within the injection region and close to the injection well is likely to be injection-induced, with one portion of the injectate returning to the production region, while the other either migrates southeastward out of the field or remains within the injection region; the origin of seismicity within the production region in relationship to production and injection processes is unclear. The second of these algorithms involves the derivation of a design criterion, which we apply to inform the expansion of the existing seismic monitoring programme at Kawerau geothermal field; we also apply an early version to the short-term/rapid-response network design following the M7.1 September 2010 Darfield earthquake. Unlike previous seismic network design algorithms, the new algorithm incorporates methods for the realistic representation of 3D velocity structures and attenuation models for both P and S travel times, a surface noise model, and the ability to apply complex weighting functions to the earthquake set. The results demonstrate the utility of this algorithm in even simplistic cases, and show how each new parameter incorporated into the design model affects the optimal network design obtained, identifying the need for accurate input data to provide optimal results.</p>

How to inflate a wind-blown bubble

ABSTRACT Stellar winds are one of several ways that massive stars can affect the star formation process on local and galactic scales. In this paper, we investigate the numerical resolution needed to inflate an energy-driven stellar wind bubble in an external medium. We find that the radius of the wind injection region, rinj, must be below a maximum value, rinj,max, in order for a bubble to be produced, but must be significantly below this value if the bubble properties are to closely agree with analytical predictions. The final bubble momentum is within 25 per cent of the value from a higher resolution reference model if χ = rinj/rinj,max = 0.1. Our work has significance for the amount of radial momentum that a wind-blown bubble can impart to the ambient medium in simulations, and thus on the relative importance of stellar wind feedback.

Ionospheric Pc1 waves during a storm recovery phase observed by the China Seismo-Electromagnetic Satellite

During the storm recovery phase on 27 August 2018, the China Seismo-Electromagnetic Satellite (CSES) detected Pc1 wave activities in both the Northern Hemisphere and Southern Hemisphere in the high-latitude, post-midnight ionosphere with a central frequency of about 2 Hz. Meanwhile, the typical Pc1 waves were simultaneously observed for several hours by the Sodankylä Geophysical Observatory (SGO) stations on the ground. In this paper, we study the propagation characteristics and possible source regions of those waves. Firstly, we find that the Pc1 waves observed by the satellites exhibited mixed polarisation, and the wave normal is almost parallel with the background magnetic field. The field-aligned Poynting fluxes point downwards in both hemispheres, implying that the satellites are close to the wave injection regions in the ionosphere at about L=3. Furthermore, we also find that the estimated position of the plasmapause calculated by models is almost at L=3. Therefore, we suggest that the possible sources of waves are near the plasmapause, which is consistent with previous studies in that the outward expansion of the plasmasphere into the ring current during the recovery phase of geomagnetic storms may generate electromagnetic ion cyclotron (EMIC) waves, and these EMIC waves propagate northwards and southwards along the background magnetic field to the ionosphere at about L=3. Additionally, the ground station data show that Pc1 wave power attenuates with increasing distance from L=3, supporting the idea that the CSES observes the wave activities near the injection region. The observations are unique in that the Pc1 waves are observed in the ionosphere in nearly conjugate regions where transverse Alfvén waves propagate down into the ionosphere.

Dispersion upscaling in highly heterogeneous aquifers: The prediction of tracer dispersion at the Macrodispersion Experiment (MADE) site

&lt;p&gt;We derive an upscaled model for the prediction of the plume evolution in highly heterogeneous aquifers based a stochastic transport representation in terms of continuous time random walks. Transport is modeled through advective motion of idealized solute particles, which changes their speed at fixed distances. The series of particles speeds is modeled as a stationary Markov chain. The derived model is parameterized by the correlation length, mean and variance of the log-hydraulic conductivity, the mean hydraulic gradient and porosity. Furthermore, it can be conditioned on the conductivity and tracer data at the injection region. The model predicts the non-Fickian evolution of the longitudinal concentration profile observed during the MADE-1 experiment. The mass distribution is characterized by strong localization at the injection region and a strong forward tail. These features are explained by conductivity heterogeneity at the injection region, and the correlated motion of particles according to spatially persistent Eulerian flow speeds.&amp;#160;&lt;/p&gt;

Statistical Study of Pitch Angle Diffusion during Substorm Injections

&lt;p&gt;Energetic Electron Precipitation (EEP) associated with substorm injections typically occurs when magnetospheric waves, particularly whistler-mode waves, resonantly interact with electrons to affect their equatorial pitch angle. This can be considered as a diffusion process that scatters particles into the loss cone. In this study, we investigate whistler-mode wave generation in conjunction with electron injections using in-situ wave measurements by the Themis mission. We calculate the pitch angle diffusion coefficient exerted by the observed wave activity using the quasi-linear diffusion approximation and estimate scattering efficiency in the substorm injection region to constrain where and how much scattering happens typically during these events.&lt;/p&gt;

Ionospheric Pc1 waves during a storm recovery phase observed by CSES

During the storm recovery phase on August 27, 2018, the China Seismo-Electromagnetic Satellite (CSES) detected Pc1 wave activities both in the Northern and Southern hemispheres in the high latitude post-midnight ionosphere with a central frequency about 2 Hz. Meanwhile, the typical Pc1 waves were simultaneously observed by the Sodankylä Geophysical Observatory (SGO) stations on the ground for several hours. In this paper, we study the propagation characteristics and possible source regions of those waves. Firstly, we find that the satellites observed Pc1 waves exhibit mixed polarization and the wave normal is almost parallel with the background magnetic field. The field-aligned Poynting fluxes point downward in both hemispheres, implying the satellites are close to the wave injection regions in the ionosphere at about L = 3. Furthermore, we also find that the estimated position of the plasmapause calculated by models is almost at L = 3. Therefore, we suggest the possible sources of waves are near the plasmapause, which is consistent with previous studies that the outward expansion of the plasmasphere into the ring current during the recovery phase of geomagnetic storms may generate electromagnetic ion cyclotron (EMIC) waves and then these EMIC waves propagate along the background magnetic field northward and southward to the ionosphere at about L = 3. Additionally, the ground station data show that Pc1 wave power attenuates with increasing distance from L = 3, supporting the idea that CSES observes the wave activities near the injection region. The observations are unique in that the Pc1 waves are observed in the ionosphere in nearly conjugate regions, where transvers Alfven waves propagate down into the ionosphere.

Physical Interpretation of Machine Learning Models Applied to Film Cooling Flows

Current turbulent heat flux models fail to predict accurate temperature distributions in film cooling flows. The present paper focuses on a machine learning (ML) approach to this problem, in which the gradient diffusion hypothesis (GDH) is used in conjunction with a data-driven prediction for the turbulent diffusivity field αt. An overview of the model is presented, followed by validation against two film cooling datasets. Despite insufficiencies, the model shows some improvement in the near-injection region. The present work also attempts to interpret the complex ML decision process, by analyzing the model features and determining their importance. These results show that the model is heavily reliant of distance to the wall d and eddy viscosity νt, while other features display localized prominence.