A Seismic Network to Monitor the 2020 EGS Stimulation in the Espoo/Helsinki Area, Southern Finland

2021 ◽  
Author(s):  
Annukka E. Rintamäki ◽  
Gregor Hillers ◽  
Tommi A. T. Vuorinen ◽  
Tuija Luhta ◽  
Jonathan M. Pownall ◽  
...  
Keyword(s):  
Seismic Analysis ◽  
Signal To Noise Ratio ◽  
Public Awareness ◽  
Seismic Network ◽  
Geothermal System ◽  
Induced Earthquakes ◽  
Data Set ◽  
Short Period ◽  
Wide Range ◽  
Good Signal

Abstract We present the deployment of a seismic network in the Helsinki capital area of Finland that was installed to monitor the response to the second stimulation phase of an ∼6-kilometer-deep enhanced geothermal system in 2020. The network consists of a dozen permanent broadband stations and more than 100, predominantly short-period, temporary stations. This 2020 deployment is characterized by a mix of single stations and arrays with diverse configurations. It covers a larger area and exhibits a smaller azimuthal gap compared with the network that monitored the first stimulation in 2018. We surveyed the outcropping rocks at one of the large array sites to study surface expressions of shear or weakness zones that are possibly connected to the stimulated volume at depth. We link the relatively large number of macroseismic reports received during the stimulation to an increased public awareness of the project together with an increased sensitivity because the second stimulation occurred during the local COVID-19 mobility restrictions. The spatial distribution of the reports seems to be controlled by the radiation pattern of the induced earthquakes and hence by the stress state in the reservoir. The continuous records contain strong energy at high frequencies above 50 Hz that is attributed to anthropogenic processes in the densely populated urban area. However, the exceptionally low attenuation of the bedrock yields good signal-to-noise ratio seismograms of the induced small events, the largest of which was magnitude ML 1.2. The signal quality of the obtained noise correlation functions is similarly very good. The data set has been collected to underpin a wide range of seismic analysis techniques for complementary scientific studies of the evolving reservoir processes and the induced event properties. These scientific studies should inform the legislation and educate the public for transparent decision making around geothermal power generation.

Determination of amplitude and duration magnitudes and site residuals from short-period seismographs in northern California

1992 ◽  
Vol 82 (2) ◽  
pp. 533-579 ◽  
Author(s):  
Jerry P. Eaton
Keyword(s):  
Processing System ◽  
Site Amplification ◽  
Seismic Network ◽  
Northern California ◽  
Data Set ◽  
Reference Instrument ◽  
Duration Magnitude ◽  
Short Period ◽  
Bedrock Lithology

Abstract Equations for determining amplitude magnitude (MX) and duration magnitude (MF) that employ all calibrated instruments in the USGS short-period telemetered seismic network in northern California (NCSN) were developed and tested against a set of 1276 earthquakes from 1986 to 1990 that were analyzed on the Caltech-USGS processing system (CUSP). The expressions for decay of amplitude and record duration in these equations are functions of distance alone. Sensitivity corrections for both MX and MF are simply the logarithms of the ratios of the magnification of the reference instrument to that of the instrument actually used. Component corrections were chosen so as to minimize the dependence of instrument site residuals on instrument component. MF site residuals were found to be closely linked to MX site residuals in a manner that suggests both depend primarily on site amplification. Both MX and MF site residuals vary systematically with bedrock lithology: older well-consolidated rocks produce negative residuals (smaller amplitudes and shorter durations) and younger unconsolidated rocks produce positive residuals (larger amplitudes and longer durations). Average station magnitude residuals are virtually independent of distance from the epicenter to at least 800 km; and MX-MF, averaged over 0.5 unit magnitude intervals, is less than 0.05 from M0.5 to M5.5. Comparison of MX and MBK (ML, UC Berkeley) for 293 events in both the CUSP data set and the Berkeley catalog shows that calculated MX s are marginally larger than the corresponding MBK s. MX-MBK averages about + 0.04. The characteristics of the standard Wood-Anderson seismograph employed to calculate MX are: free period 0.8 sec, damping constant 0.8, and static magnification 2080.

Modeling delay-fired explosion spectra at regional distances

1992 ◽  
Vol 82 (6) ◽  
pp. 2430-2447
Author(s):  
M. C. Chapman ◽  
G. A. Bollinger ◽  
M. S. Sibol
Keyword(s):  
Source Region ◽  
Total Duration ◽  
Recording Site ◽  
Data Set ◽  
Low Frequencies ◽  
Eastern Kentucky ◽  
Independent Amplitude ◽  
Short Period ◽  
Amplitude Spectra ◽  
Good Signal

Abstract The objectives of this study are to model the observed seismic spectra from large industrial explosions using information obtained from blaster's logs and to compare the explosion spectra with those of small earthquake signals from the same source region. The data set consists of digital waveforms from four mining explosions (200,000 + lb. of explosives each) and two earthquakes (M = 3.5 and 4.0) in eastern Kentucky. The data were recorded on a short-period regional network at distances ranging from 180 to 400 km and have good signal-to-noise ratios at frequencies from 0.5 to 10 Hz. The explosion amplitude spectra differ markedly from those of the earthquakes, by exhibiting strong time-independent amplitude modulations. This spectral modulation is directly attributable to the explosive charge geometry and firing sequence and is largely independent of source-station path and recording site. Modeling of the explosion source spectra shows that the major contributor to the modulated character of the spectra are amplitude minima at frequencies related to the total duration of the explosion sequence. Another important effect is amplitude reinforcement at low frequencies (e.g., 5 Hz) due to the comparatively long delay (0.2 sec) between the firing of individual rows of explosives. These features dominate both Pg and Lg amplitude spectra at frequencies less than 7 Hz. Accurate modeling of the observed spectra at frequencies greater than a few Hertz requires that the azimuth of the recording site be taken into account. Also, the spectra at higher frequencies become sensitive to random variations in the firing times of any of the various subexplosions.

Passive monitoring and 3D imaging of the bedrock response to the 2018 Espoo/Helsinki geothermal stimulation

2020 ◽  
Author(s):  
George Taylor ◽  
Gregor Hillers
Keyword(s):  
Seismic Velocity ◽  
Imaging Techniques ◽  
Body Wave ◽  
Coda Wave ◽  
Geothermal System ◽  
Enhanced Geothermal Systems ◽  
Induced Earthquakes ◽  
Borehole Data ◽  
Passive Monitoring ◽  
Short Period

<p>In recent years several deep geothermal energy projects have been forced to close following the occurrence of large seismic events associated with the stimulation of the surrounding bedrock. In 2018, an enhanced geothermal system (EGS) experiment performed in Helsinki, Finland concluded with no seismicity exceeding the threshold magnitude and thus provides an intriguing showcase for future stimulation experiments in similar environments. During the 49 days of the experiment, the five-stage injection of ~18,000 cubic meters water stimulated many thousands of earthquakes. Like in all previous stimulation cases the earthquake data constitute the primary source for the assessment of the scientific and operational aspects of the reservoir response. Here we apply ambient noise based monitoring and imaging techniques to data collected by 100 short period three-component stations that were organized in three large arrays consisting of nominally 25 stations, in addition to three small four-station arrays, and 10 single stations, during a 100 day period. We compute daily nine-component noise correlations between all stations pairs except for the intra-array pairs in a frequency range between 0.5 and 10 Hz. We measure waveform delays within our correlation functions as a function of frequency and lag time using the Continuous Wavelet Transform. We then invert these observations using a Markov chain Monte Carlo method to obtain the temporal variation in seismic velocity dv/v during the injection. By exploiting the variable spatial sensitivities of the surface- and body-wave components at different coda-wave lapse times and frequencies, we are able to image the medium response to the stimulation in both time and space. We compare the estimated seismic velocity variations to other observations such as H<sup>2</sup>/V<sup>2</sup>, as well as dv/v observations obtained from collocated borehole data. Importantly, we also compare the observed medium response to seismicity and pumping parameters. Our results suggest that we are able to resolve medium changes that are not solely associated with the induced earthquakes, but also potential signatures of fluid content or pressure changes in the bedrock. The combined observations of seismicity, pumping parameters and dv/v changes collected in this experiment can further advance passive monitoring techniques in the context of enhanced geothermal systems, and facilitate a more comprehensive analysis of fluid-rock interactions that may occur aseismically.</p>

Method for Determination of Focal Mechanisms of Magnitude 2.5–4.0 Earthquakes Recorded by a Sparse Regional Seismic Network

2020 ◽  
Vol 110 (2) ◽  
pp. 715-726
Author(s):  
Nawa R. Dahal ◽  
John E. Ebel
Keyword(s):  
Focal Mechanism ◽  
Signal To Noise Ratio ◽  
Moment Tensor ◽  
Focal Depth ◽  
Focal Mechanisms ◽  
Seismic Network ◽  
Seismic Waveforms ◽  
Virginia Earthquake ◽  
Using Data ◽  
Good Signal

ABSTRACT Focal mechanisms of earthquakes with magnitudes Mw 4.0 and less recorded by a sparse seismic network are usually poorly constrained due to the lack of an appropriate method applicable to finding these parameters with a sparse set of observations. We present a new method that can accurately determine focal mechanisms of earthquakes with Mw (3.70–3.04) using data from a few regional seismic stations. We filter the observed seismograms as well as synthetic seismograms through a frequency band of 1.5–2.5 Hz, which has a good signal-to-noise ratio for small earthquakes of the magnitudes with which we are working. The waveforms are processed to their envelopes to make the waveforms relatively simple for modeling. To find the optimal focal mechanism for an event, a nonlinear moment tensor inversion in addition to a coarse grid search over the possible dip, rake, and strike angles at a fixed value of focal depth and a fixed value of scalar moment is performed. We tested the method on 18 aftershocks of Mw (3.70–2.60) of the 2011 Mw 5.7 Mineral, Virginia, earthquake and on five aftershocks of Mw (3.62–2.63) of the 2013 Mw 4.5 Ladysmith, Quebec, earthquake. Our method obtains accurate focal mechanisms for 16 out of the 21 events that have previously reported focal mechanisms. Tests of our method for different crustal models show that event focal mechanism determinations vary with an average Kagan angle of 30° with the different crustal models. This means that the event focal mechanism determinations are only somewhat sensitive to the uncertainties in the crustal models tested. This study confirms that our method of modeling envelopes of seismic waveforms can be used to extract accurate focal mechanisms of earthquakes with short-time functions (Mw<4.0) using at least three regional seismic network stations at epicentral distances of 60–350 km.

Processed ground-motion records from induced earthquakes for use in engineering applications

2020 ◽  
Vol 47 (1) ◽  
pp. 96-108 ◽  
Author(s):  
Karen Assatourians ◽  
Gail M. Atkinson
Keyword(s):  
Ground Motion ◽  
Signal To Noise Ratio ◽  
Response Spectra ◽  
Induced Earthquakes ◽  
Engineering Applications ◽  
Time Histories ◽  
The Time Domain ◽  
Record Selection ◽  
Ground Motion Records ◽  
Good Signal

We compile and process an electronic database of ground motions recorded on accelerometers and broadband seismographic instruments for induced earthquakes of M ≥ 4 at distances <50 km in central and eastern North America. Most of the data are from Oklahoma, with some records from Alberta. Our focus is on the subset of available records that are of most interest for engineering analyses aimed at evaluation of the potential hazards from induced events, which is a pressing issue in western Canada and other regions experiencing induced seismicity. We considered all records to 50 km for events of M ≥ 4.5. For events of M 4 to 4.5, we select records at close distance (<10 km), having good signal strength (PGA > ∼3%g), to allow high-quality time histories to be obtained. These records have strong signal-to-noise ratio, making them suitable for engineering applications, such as dynamic analysis, after proper scaling. The selected records are windowed, filtered, and instrument-corrected to compile a set of records having acceptable acceleration, velocity, and displacement time histories. The records and their response spectra are provided as an electronic supplement at http://www.seismotoolbox.ca/IS_Strong_Motions/ . We note that the record set is not suitable as a response spectra database for development of ground-motion prediction equations, because for M < 4.5 the record selection is biased to records with higher amplitudes. Rather, the intended use of the records is as seed records, which can be readily scaled in the time domain to approximately represent induced-event target scenarios for engineering applications.

Spherical multifocusing method for irregular topography

Geophysics ◽  
2019 ◽  
Vol 84 (4) ◽  
pp. V233-V243
Author(s):  
Dingyue Chang ◽  
Cai Zhang ◽  
Tianyue Hu ◽  
Dan Wang
Keyword(s):  
Signal To Noise Ratio ◽  
Seismic Exploration ◽  
Data Set ◽  
Near Surface ◽  
The Past ◽  
Wide Range ◽  
Common Reflection Surface ◽  
Static Corrections ◽  
The Common ◽  
Irregular Topography

Moveout correction for irregular topography has been a longstanding challenge in processing seismic exploration data. Irregular topography usually results in large moveout among traces, a low signal-to-noise ratio (S/N), and difficulty in modeling near-surface velocities. Conventional normal moveout (NMO) corrections and elevation static methods are imprecise and tend to introduce significant errors for large offsets. Over the past two decades, several multiparameter time corrections and stacking techniques to reduce noise and improve resolution have been proposed in place of the classic NMO and common-midpoint stack. These include the common-reflection-surface (CRS), common-offset CRS, nonhyperbolic CRS, implicit CRS, multifocusing (MF), irregular surface MF (IS-MF), spherical MF (SMF), and common-offset MF methods. Various CRS-type operators that consider the top-surface topography have been proposed. For MF-type operators, only IS-MF can be applied directly to the irregular topography with no elevation statics required. In this study, we have developed a new MF formulation, modifying the SMF method to consider nonzero elevations of sources and receivers and we corrected moveout of nonplanar data directly without prior elevation static corrections. The proposed extension combines the sensitivity to spherical reflectors of SMF with the applicability of the IS-MF method to irregular topography. We investigated the behavior of the new operator using a physical model data set and compared the results with those from the conventional IS-MF method. The results revealed that the new operator is more robust over a wide range of source and receiver elevations and has advantages on strongly curved interfaces. We also confirmed the potential of the proposed approach by comparing stacking results for a real-land data set with a low S/N.

Three-component analysis of regional phases at NORESS and ARCESS: Polarization and phase identification

1991 ◽  
Vol 81 (6) ◽  
pp. 2419-2440
Author(s):  
Anne Suteau-Henson
Keyword(s):  
Multivariate Data Analysis ◽  
Signal To Noise Ratio ◽  
Phase Identification ◽  
Signal To Noise ◽  
Data Set ◽  
Intelligent Monitoring ◽  
Short Period ◽  
Regional Phases ◽  
Motion Characteristics ◽  
P Type

Abstract Particle motion characteristics of short-period three-component (3-C) data are compared for various seismic phases at NORESS and ARCESS, and their usefulness for phase identification is evaluated. Continuous recordings at the arrays of 3-C elements were processed during routine operation of the Intelligent Monitoring System (IMS). The data set used in this study consists of 3822 arrivals extracted from the IMS database and covers a period of about 2.5 months. First, polarization attributes and azimuth of the dominant linear motion are compared for local/regional phases (Pn, Pg, Sn, Lg) at the two arrays. P-type arrivals have larger angles of incidence at ARCESS than at NORESS, on average, for similar ranges of distance and signal-to-noise ratio (SNR). This can be partly explained by higher crustal velocities under the ARCESS array. Also, at ARCESS the ratio of horizontal to vertical power is similar for Sn and Lg, on average, while at NORESS it is larger for Sn, Sn and Lg azimuths at ARCESS (and Lg azimuth at NORESS, with more scatter) provide good estimates of backazimuth (with a 180° ambiguity) and indicate predominance of SH motion at ARCESS. In the second part of this study, multivariate data analysis is performed to obtain phase identifications (with associated confidence), using polarization attributes as predictors. P- and S-type phases are distinguished with a success rate of 82% at NORESS and 89% at ARCESS. The performance is even better for P-type arrivals with 3-C SNR &gt; 2 (96 and 98%, respectively). Sn and Lg are correctly identified for 74% of S-type phases at NORESS and 64% at ARCESS. Contamination of Lg by Sn coda at shorter ranges and dominant SH motion for both S-type phases at ARCESS affect the performance. This study shows the importance of evaluating each 3-C station individually.

Structure Guided Molecular Docking Assisted Alignment Dependent 3DQSAR Study on Steroidal Aromatase Inhibitors (SAIs) as Anti-breast Cancer Agents

2019 ◽  
Vol 16 (7) ◽  
pp. 808-817 ◽  
Author(s):  
Laxmi Banjare ◽  
Sant Kumar Verma ◽  
Akhlesh Kumar Jain ◽  
Suresh Thareja
Keyword(s):  
Breast Cancer ◽  
Molecular Docking ◽  
Aromatase Inhibitors ◽  
Direct Synthesis ◽  
3D Qsar ◽  
Chemotherapeutic Agents ◽  
Modeling Tools ◽  
Data Set ◽  
Wide Range ◽  
Steroidal Aromatase Inhibitors

Background: In spite of the availability of various treatment approaches including surgery, radiotherapy, and hormonal therapy, the steroidal aromatase inhibitors (SAIs) play a significant role as chemotherapeutic agents for the treatment of estrogen-dependent breast cancer with the benefit of reduced risk of recurrence. However, due to greater toxicity and side effects associated with currently available anti-breast cancer agents, there is emergent requirement to develop target-specific AIs with safer anti-breast cancer profile. Methods: It is challenging task to design target-specific and less toxic SAIs, though the molecular modeling tools viz. molecular docking simulations and QSAR have been continuing for more than two decades for the fast and efficient designing of novel, selective, potent and safe molecules against various biological targets to fight the number of dreaded diseases/disorders. In order to design novel and selective SAIs, structure guided molecular docking assisted alignment dependent 3D-QSAR studies was performed on a data set comprises of 22 molecules bearing steroidal scaffold with wide range of aromatase inhibitory activity. Results: 3D-QSAR model developed using molecular weighted (MW) extent alignment approach showed good statistical quality and predictive ability when compared to model developed using moments of inertia (MI) alignment approach. Conclusion: The explored binding interactions and generated pharmacophoric features (steric and electrostatic) of steroidal molecules could be exploited for further design, direct synthesis and development of new potential safer SAIs, that can be effective to reduce the mortality and morbidity associated with breast cancer.

Integrative Data Analysis from a Unifying Research Synthesis Perspective

2018 ◽  
Author(s):  
Eun-Young Mun ◽  
Anne E. Ray
Keyword(s):  
Data Analysis ◽  
Large Scale ◽  
Research Synthesis ◽  
Alcohol Intervention ◽  
Data Set ◽  
Integrative Data Analysis ◽  
Level Data ◽  
Model Complex ◽  
Wide Range ◽  
Individual Participant

Integrative data analysis (IDA) is a promising new approach in psychological research and has been well received in the field of alcohol research. This chapter provides a larger unifying research synthesis framework for IDA. Major advantages of IDA of individual participant-level data include better and more flexible ways to examine subgroups, model complex relationships, deal with methodological and clinical heterogeneity, and examine infrequently occurring behaviors. However, between-study heterogeneity in measures, designs, and samples and systematic study-level missing data are significant barriers to IDA and, more broadly, to large-scale research synthesis. Based on the authors’ experience working on the Project INTEGRATE data set, which combined individual participant-level data from 24 independent college brief alcohol intervention studies, it is also recognized that IDA investigations require a wide range of expertise and considerable resources and that some minimum standards for reporting IDA studies may be needed to improve transparency and quality of evidence.

scholarly journals A Self-Spatial Adaptive Weighting Based U-Net for Image Segmentation

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 348
Author(s):  
Choongsang Cho ◽  
Young Han Lee ◽  
Jongyoul Park ◽  
Sangkeun Lee
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Feature Maps ◽  
Data Set ◽  
Feature Map ◽  
Adaptive Weighting ◽  
Spatially Adaptive ◽  
Wide Range ◽  
Decoder Architecture

Semantic image segmentation has a wide range of applications. When it comes to medical image segmentation, its accuracy is even more important than those of other areas because the performance gives useful information directly applicable to disease diagnosis, surgical planning, and history monitoring. The state-of-the-art models in medical image segmentation are variants of encoder-decoder architecture, which is called U-Net. To effectively reflect the spatial features in feature maps in encoder-decoder architecture, we propose a spatially adaptive weighting scheme for medical image segmentation. Specifically, the spatial feature is estimated from the feature maps, and the learned weighting parameters are obtained from the computed map, since segmentation results are predicted from the feature map through a convolutional layer. Especially in the proposed networks, the convolutional block for extracting the feature map is replaced with the widely used convolutional frameworks: VGG, ResNet, and Bottleneck Resent structures. In addition, a bilinear up-sampling method replaces the up-convolutional layer to increase the resolution of the feature map. For the performance evaluation of the proposed architecture, we used three data sets covering different medical imaging modalities. Experimental results show that the network with the proposed self-spatial adaptive weighting block based on the ResNet framework gave the highest IoU and DICE scores in the three tasks compared to other methods. In particular, the segmentation network combining the proposed self-spatially adaptive block and ResNet framework recorded the highest 3.01% and 2.89% improvements in IoU and DICE scores, respectively, in the Nerve data set. Therefore, we believe that the proposed scheme can be a useful tool for image segmentation tasks based on the encoder-decoder architecture.

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