P-wave fracture prediction algorithm using data with limited azimuthal distribution

Hydrogen energy vehicles are being increasingly widely used. To ensure the safety of hydrogenation stations, research into the detection of hydrogen leaks is required. Offline analysis using data machine learning is achieved using Spark SQL and Spark MLlib technology. In this study, to determine the safety status of a hydrogen refueling station, we used multiple algorithm models to perform calculation and analysis: a multi-source data association prediction algorithm, a random gradient descent algorithm, a deep neural network optimization algorithm, and other algorithm models. We successfully analyzed the data, including the potential relationships, internal relationships, and operation laws between the data, to detect the safety statuses of hydrogen refueling stations.

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Genetic Prediction of Male Pattern Baldness

10.1101/072306 ◽

2016 ◽

Author(s):

Saskia P Hagenaars ◽

W David Hill ◽

Sarah E Harris ◽

Stuart J Ritchie ◽

Gail Davies ◽

...

Keyword(s):

Hair Loss ◽

Genetic Architecture ◽

Prediction Algorithm ◽

Uk Biobank ◽

Psychosocial Effects ◽

Male Pattern Baldness ◽

Adverse Health Outcomes ◽

Common Genetic Variants ◽

Using Data ◽

Male Pattern

AbstractMale pattern baldness can have substantial psychosocial effects, and it has been phenotypically linked to adverse health outcomes such as prostate cancer and cardiovascular disease. We explored the genetic architecture of the trait using data from over 52,000 male participants of UK Biobank, aged 40-69 years. We identified over 250 independent novel genetic loci associated with severe hair loss. By developing a prediction algorithm based entirely on common genetic variants, and applying it to an independent sample, we could discriminate accurately (AUC = 0.82) between those with no hair loss from those with severe hair loss. The results of this study might help identify those at the greatest risk of hair loss and also potential genetic targets for intervention.

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A waveform correlation method for identifying quarry explosions

Bulletin of the Seismological Society of America ◽

10.1785/bssa0810062395 ◽

1991 ◽

Vol 81 (6) ◽

pp. 2395-2418

Author(s):

D. B. Harris

Keyword(s):

Linear Combination ◽

Correlation Method ◽

P Wave ◽

Large Signal ◽

Source Regions ◽

Single Station ◽

Using Data ◽

Element Array ◽

Test Ban ◽

Master Event

Abstract A waveform correlation method is presented for identifying quarry explosions by attributing them to known mines characterized by multiple master events. The objective is to provide a reliable automatic procedure for screening the large number of quarry explosions likely to be detected by networks of in-country stations monitoring compliance with test-ban treaties. The method generalizes existing correlation techniques to compare waveforms from an unlocated event recorded at an array of sensors with a linear combination of master event waveforms recorded at the same array. The use of a linear combination reduces the chance of a missed location caused by some variation in mechanism or spectral excitation between the events being compared. The weights in the linear combination are filters, offering some compensation for variations in source time functions and errors of waveform alignment. The use of array data reduces the likelihood of false attribution by reducing bias and variance in the correlation measurement. In a test conducted with P-wave data segments recorded at a 13-element array, the method successfully resolves two source regions separated by 4 km at a range of 150 km. Resolution with single-station waveform correlations is marginal due to the limited amount of data. The statistics of the sample waveform correlation coefficient are developed and demonstrate that single-station waveform correlations are unreliable unless estimated with large signal durations T or bandwidths B. A time-bandwidth TB product exceeding 100 (or smaller TB with more stations) is necessary for reliable event attribution. The related problem of separating superimposed waveforms from two events in different source regions may be solved by cancellation. The waveforms of one event are again approximated by a linear combination of waveforms from master events in the same mine. The residual signals, obtained by subtracting the approximation from the superimposed waveforms, estimate the waveforms from the second event. This method achieves significant separation of waveforms from events 6 km apart at a range of 150 km, using data from the 13-element array. Its resolution exceeds that of conventional beam-forming methods.

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Temporal Analysis of COVID - 19 Pandemic in India and R0 prediction

10.21203/rs.3.rs-40655/v1 ◽

2020 ◽

Author(s):

Kshitij Patil ◽

Anirudh Murali ◽

Piyali Ganguli ◽

Sutanu Nandi ◽

Ram Rup Sarkar

Keyword(s):

Temporal Analysis ◽

Prediction Algorithm ◽

Actual Number ◽

Expected Number ◽

Short Term ◽

Using Data ◽

Novel Strategy

Abstract India now ranks 3rd in the list of countries affected due to the COVID-19 pandemic. With more than 0.7 million confirmed COVID–19 cases and a gradual withdrawal of nation-wide lockdown, it is expected India may see a further surge in the number of cases. So, predicting the expected number of cases is the need of the hour. Most of the existing methodologies work well for the short term but perform poorly when it comes to predicting the long term. Hence, in this study, we propose a novel strategy for the prediction of COVID–19 cases by employing Multiple Aggregation Prediction Algorithm (MAPA) with two different ways of predictions called the Principal and the Exponential Predictions. Exponential Prediction has been performed using MAPA on the number of cases, derived from predicted R0. From the Principal and Exponential prediction, a third prediction called Mean prediction was derived by averaging both. We have validated this strategy using data of the different states of India and show that Principal, Mean, and the Exponential Predictions together capture a range in which the actual number of cases lies. We have used this strategy to forecast the range of COVID-19 cases for 45 days.

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Coda suppression capabilities of the beam and mixed-signal processor

Bulletin of the Seismological Society of America ◽

10.1785/bssa0640020415 ◽

1974 ◽

Vol 64 (2) ◽

pp. 415-426

Author(s):

T. J. Cohen

Keyword(s):

P Wave ◽

Mixed Signal ◽

Preliminary Results ◽

Large Aperture ◽

Coda Attenuation ◽

Aperture Array ◽

The Difference ◽

Using Data ◽

Ray Parameter ◽

Signal Processor

abstract Using data recorded on 7- and 19-element subarrays at TFO, we compare the relative abilities of the beam and a mixed-signal processor to attenuate P-wave codas. The P-wave signals from various earthquakes are time-shifted to simulate arrivals from different azimuths and 60° distance. These signals are assumed to mask an explosion detonated at Semipalatinsk, about 96° distant from TFO. The analysis consists of determining how much of signal 1 (the earthquake) leaks into our estimate of signal 2 (the event being masked). Because the beam and mixed-signal processor are linear processors, signal 2 need not actually be present; we need only determine how much of signal 1 is present in our estimate of signal 2. The coda attenuation capability of the mixed-signal processor is found to exceed that of the beam. Up to 14-db improvement over the beam is obtained, although improvement is generally on the order of 3 to 5 db for both subarrays. A nominal value of 18 db is representative of the maximum coda attenuation obtained using the mixed-signal processor and the 19-element subarray; for the 7-element subarray, maximum attenuation is roughly 14 db. Coda attenuation obtained using the 7-element subarray and the mixed-signal processor is comparable to that obtained using the 19-element subarray and the beam. Preliminary results suggest that when the difference in the ray parameter vectors is small, a large-aperture array and the mixed-signal processor are required for significant (∼9 db) coda attenuation.

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Teleseismic P-Wave Coda Autocorrelation Imaging of Crustal and Basin Structure, Bighorn Mountains Region, Wyoming, U.S.A.

Bulletin of the Seismological Society of America ◽

10.1785/0120200177 ◽

2020 ◽

Author(s):

Steven M. Plescia ◽

Anne F. Sheehan ◽

Seth S. Haines ◽

Lindsay L. Worthington ◽

Scott Cook ◽

...

Keyword(s):

Seismic Data ◽

High Frequency ◽

Vertical Component ◽

P Wave ◽

Seismic Profiles ◽

Crustal Structures ◽

Use Phase ◽

Using Data ◽

Teleseismic Events ◽

Bighorn Mountains

ABSTRACT We demonstrate successful crustal imaging via teleseismic P-wave coda autocorrelation, using data recorded on a 261 station array of vertical-component high-frequency geophones in the area of the Bighorn Mountains, Wyoming, U.S.A. We autocorrelate the P-wave coda of 30 teleseismic events and use phase-weighted stacking to yield seismic profiles comparable to low-passed versions of those produced via controlled-source vertical seismic reflection. Our process recovers reflections from the bottoms of the Bighorn and Powder River basins that flank the Bighorn Mountains. We also identify a mid-crustal reflector that aligns with a region of increased reflectivity, previously interpreted as a Precambrian province boundary. Our results demonstrate the utility of crustal imaging with teleseismic P-wave coda energy using modern large-array seismic data, and they corroborate previous interpretations of crustal structures in the study area.

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