scholarly journals Machine learning to identify geologic factors associated with production in geothermal fields: A casestudy using 3D geologic data, Brady geothermal field, Nevada

2021 ◽  
Author(s):  
D. Siler ◽  
Jeff Pepin ◽  
Velimir Vesselinov ◽  
Maruti Mudunuru ◽  
Bulbul Ahmmed
Keyword(s):  
Machine Learning ◽  
Geothermal Field ◽  
Factors Associated ◽  
Geothermal Fields ◽  
Geologic Data

scholarly journals Machine learning to identify geologic factors associated with production in geothermal fields: a case-study using 3D geologic data, Brady geothermal field, Nevada

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Drew L. Siler ◽  
Jeff D. Pepin ◽  
Velimir V. Vesselinov ◽  
Maruti K. Mudunuru ◽  
Bulbul Ahmmed
Keyword(s):  
Machine Learning ◽  
Fluid Flow ◽  
Hydrothermal System ◽  
Geothermal Field ◽  
Hydrothermal Systems ◽  
Electricity Production ◽  
Fault System ◽  
Macro Scale ◽  
Geologic Data ◽  
Geologic Controls

AbstractIn this paper, we present an analysis using unsupervised machine learning (ML) to identify the key geologic factors that contribute to the geothermal production in Brady geothermal field. Brady is a hydrothermal system in northwestern Nevada that supports both electricity production and direct use of hydrothermal fluids. Transmissive fluid-flow pathways are relatively rare in the subsurface, but are critical components of hydrothermal systems like Brady and many other types of fluid-flow systems in fractured rock. Here, we analyze geologic data with ML methods to unravel the local geologic controls on these pathways. The ML method, non-negative matrix factorization with k-means clustering (NMFk), is applied to a library of 14 3D geologic characteristics hypothesized to control hydrothermal circulation in the Brady geothermal field. Our results indicate that macro-scale faults and a local step-over in the fault system preferentially occur along production wells when compared to injection wells and non-productive wells. We infer that these are the key geologic characteristics that control the through-going hydrothermal transmission pathways at Brady. Our results demonstrate: (1) the specific geologic controls on the Brady hydrothermal system and (2) the efficacy of pairing ML techniques with 3D geologic characterization to enhance the understanding of subsurface processes.

Identifying the underlying factors associated with antidepressant drug discontinuation: Content analysis of patients’ drug reviews (Preprint)

2020 ◽  
Author(s):  
Mohammad Alarifi ◽  
Somaieh Goudarzvand3 ◽  
Abdulrahman Jabour ◽  
Doreen Foy ◽  
Maryam Zolnoori
Keyword(s):  
Machine Learning ◽  
Antidepressant Drug ◽  
Prediction Method ◽  
Analytical Framework ◽  
Structured Data ◽  
Withdrawal Symptoms ◽  
Machine Learning Techniques ◽  
Drug Discontinuation ◽  
Factors Associated ◽  
Learning Techniques

BACKGROUND The rate of antidepressant prescriptions is globally increasing. A large portion of patients stop their medications which could lead to many side effects including relapse, and anxiety. OBJECTIVE The aim of this was to develop a drug-continuity prediction model and identify the factors associated with drug-continuity using online patient forums. METHODS We retrieved 982 antidepressant drug reviews from the online patient’s forum AskaPatient.com. We followed the Analytical Framework Method to extract structured data from unstructured data. Using the structured data, we examined the factors associated with antidepressant discontinuity and developed a predictive model using multiple machine learning techniques. RESULTS We tested multiple machine learning techniques which resulted in different performances ranging from accuracy of 65% to 82%. We found that Radom Forest algorithm provides the highest prediction method with 82% Accuracy, 78% Precision, 88.03% Recall, and 84.2% F1-Score. The factors associated with drug discontinuity the most were; withdrawal symptoms, effectiveness-ineffectiveness, perceived-distress-adverse drug reaction, rating, and perceived-distress related to withdrawal symptoms. CONCLUSIONS Although the nature of data available at online forums differ from data collected through surveys, we found that online patients forum can be a valuable source of data for drug-continuity prediction and understanding patients experience. The factors identified through our techniques were consistent with the findings of prior studies that used surveys.

scholarly journals Fluid activity detection in geothermal areas using a single seismic station by monitoring horizontal-to-vertical spectral ratios

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kyosuke Okamoto ◽  
Hiroshi Asanuma ◽  
Hiro Nimiya
Keyword(s):  
Carbon Capture ◽  
Seismic Station ◽  
Carbon Capture And Storage ◽  
Fluid Flows ◽  
Temporal Variations ◽  
Geothermal Field ◽  
Spectral Ratios ◽  
Subsurface Structures ◽  
Geothermal Fields ◽  
Single Station

AbstractSubsurface structure survey based on horizontal-to-vertical (H/V) spectral ratios is widely conducted. The major merit of this survey is its convenience to obtain a stable result using a single station. Spatial variations of H/V spectral ratios are well-known phenomena, and it has been used to estimate the spatial fluctuation in subsurface structures. It is reasonable to anticipate temporal variations in H/V spectral ratios, especially in areas like geothermal fields, carbon capture and storage fields, etc., where rich fluid flows are expected, although there are few reports about the temporal changes. In Okuaizu Geothermal Field (OGF), Japan, dense seismic monitoring was deployed in 2015, and continuous monitoring has been consistent. We observed the H/V spectral ratios in OGF and found their repeated temporary drops. These drops seemed to be derived from local fluid activities according to a numerical calculation. Based on this finding, we examined a coherency between the H/V spectral ratios and fluid activities in OGF and found a significance. In conclusion, monitoring H/V spectral ratios can enable us to grasp fluid activities that sometimes could lead to a relatively large seismic event.

O-203 Application of machine learning to predict aneuploidy and mosaicism in embryos from in vitro fertilization (IVF) cycles

2021 ◽  
Vol 36 (Supplement_1) ◽  
Author(s):  
J A Ortiz ◽  
R Morales ◽  
B Lledo ◽  
E Garcia-Hernandez ◽  
A Cascales ◽  
...  
Keyword(s):  
Machine Learning ◽  
Predictive Model ◽  
Predictive Models ◽  
Maternal Age ◽  
The Other ◽  
Predictor Variables ◽  
Learning Models ◽  
Male Factor ◽  
Factors Associated ◽  
Machine Learning Models

Abstract Study question Is it possible to predict the likelihood of an IVF embryo being aneuploid and/or mosaic using a machine learning algorithm? Summary answer There are paternal, maternal, embryonic and IVF-cycle factors that are associated with embryonic chromosomal status that can be used as predictors in machine learning models. What is known already The factors associated with embryonic aneuploidy have been extensively studied. Mostly maternal age and to a lesser extent male factor and ovarian stimulation have been related to the occurrence of chromosomal alterations in the embryo. On the other hand, the main factors that may increase the incidence of embryo mosaicism have not yet been established. The models obtained using classical statistical methods to predict embryonic aneuploidy and mosaicism are not of high reliability. As an alternative to traditional methods, different machine and deep learning algorithms are being used to generate predictive models in different areas of medicine, including human reproduction. Study design, size, duration The study design is observational and retrospective. A total of 4654 embryos from 1558 PGT-A cycles were included (January-2017 to December-2020). The trophoectoderm biopsies on D5, D6 or D7 blastocysts were analysed by NGS. Embryos with ≤25% aneuploid cells were considered euploid, between 25-50% were classified as mosaic and aneuploid with >50%. The variables of the PGT-A were recorded in a database from which predictive models of embryonic aneuploidy and mosaicism were developed. Participants/materials, setting, methods The main indications for PGT-A were advanced maternal age, abnormal sperm FISH and recurrent miscarriage or implantation failure. Embryo analysis were performed using Veriseq-NGS (Illumina). The software used to carry out all the analysis was R (RStudio). The library used to implement the different algorithms was caret. In the machine learning models, 22 predictor variables were introduced, which can be classified into 4 categories: maternal, paternal, embryonic and those specific to the IVF cycle. Main results and the role of chance The different couple, embryo and stimulation cycle variables were recorded in a database (22 predictor variables). Two different predictive models were performed, one for aneuploidy and the other for mosaicism. The predictor variable was of multi-class type since it included the segmental and whole chromosome alteration categories. The dataframe were first preprocessed and the different classes to be predicted were balanced. A 80% of the data were used for training the model and 20% were reserved for further testing. The classification algorithms applied include multinomial regression, neural networks, support vector machines, neighborhood-based methods, classification trees, gradient boosting, ensemble methods, Bayesian and discriminant analysis-based methods. The algorithms were optimized by minimizing the Log_Loss that measures accuracy but penalizing misclassifications. The best predictive models were achieved with the XG-Boost and random forest algorithms. The AUC of the predictive model for aneuploidy was 80.8% (Log_Loss 1.028) and for mosaicism 84.1% (Log_Loss: 0.929). The best predictor variables of the models were maternal age, embryo quality, day of biopsy and whether or not the couple had a history of pregnancies with chromosomopathies. The male factor only played a relevant role in the mosaicism model but not in the aneuploidy model. Limitations, reasons for caution Although the predictive models obtained can be very useful to know the probabilities of achieving euploid embryos in an IVF cycle, increasing the sample size and including additional variables could improve the models and thus increase their predictive capacity. Wider implications of the findings Machine learning can be a very useful tool in reproductive medicine since it can allow the determination of factors associated with embryonic aneuploidies and mosaicism in order to establish a predictive model for both. To identify couples at risk of embryo aneuploidy/mosaicism could benefit them of the use of PGT-A. Trial registration number Not Applicable

scholarly journals Using the Paratunsky geothermal field to provide heating for Kamchatka

2019 ◽  
pp. 21-33
Author(s):  
A. V. Kiryukhin ◽  
N. B. Zhuravlev
Keyword(s):  
Waste Water ◽  
Thermal Energy ◽  
Thermal Water ◽  
Discharge Rate ◽  
Local Heating ◽  
Geothermal Field ◽  
Heat Energy ◽  
Heating Systems ◽  
Geothermal Fields ◽  
Potential Market

The Paratunsky geothermal field has been in operation since 1964, mostly in a self-flowing mode, with a discharge rate of approximately 250 kg/s of thermal water at temperatures of 70–90°С (47 Mw, with the waste water having a temperature of 35°С). The water drawn from the field is used for local heating, spa heating, and for greeneries in the villages of Paratunsky and Termal’nyi (3000 residents). The potential market of thermal energy in Kamchatka includes Petropavlovsk-Kamchatskii (180000 residents), Elizovo (39 000), and Vilyuchinsk (22 000). The heat consumption in the centralized heating systems for Petropavlovsk-Kamchatskii is 1 623 000 GCal per annum (216 Mw). A thermohydrodynamic model developed previously is used to show that the Paratunsky geothermal reservoir can be operated in a sustainable mode using submersible pumps at an extraction rate of as much as 1375 kg/s, causing a moderate decrease in pressure (by no more than 8 bars) and temperature (by no more than 4°С) in the reservoir. Additional geothermal sources of heat energy may include the Verkhne-Paratunsky and Mutnovsky geothermal fields.

Explainable Machine Learning Analysis of Neuro-Behavioral Factors Associated With Marijuana Use Outcomes

2020 ◽  
Vol 87 (9) ◽  
pp. S393
Author(s):  
Gregory Niklason ◽  
Eric Rawls ◽  
Sisi Ma ◽  
Erich Kummerfeld ◽  
Sheila Specker ◽  
...  
Keyword(s):  
Machine Learning ◽  
Marijuana Use ◽  
Behavioral Factors ◽  
Factors Associated ◽  
Learning Analysis

Identifying Factors Associated With Severe Intellectual Disabilities in Teenagers With Cerebral Palsy Using a Predictive Learning Model

2019 ◽  
Vol 34 (4) ◽  
pp. 221-229 ◽  
Author(s):  
Carlo M. Bertoncelli ◽  
Paola Altamura ◽  
Edgar Ramos Vieira ◽  
Domenico Bertoncelli ◽  
Susanne Thummler ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cerebral Palsy ◽  
Intellectual Disability ◽  
Prediction Model ◽  
Motor Skills ◽  
Adaptive Functioning ◽  
Learning Model ◽  
Intelligence Scale ◽  
Factors Associated ◽  
Machine Learning Model

Background: Intellectual disability and impaired adaptive functioning are common in children with cerebral palsy, but there is a lack of studies assessing these issues in teenagers with cerebral palsy. Therefore, the aim of this study was to develop and test a predictive machine learning model to identify factors associated with intellectual disability in teenagers with cerebral palsy. Methods: This was a multicenter controlled cohort study of 91 teenagers with cerebral palsy (53 males, 38 females; mean age ± SD = 17 ± 1 y; range: 12-18 y). Data on etiology, diagnosis, spasticity, epilepsy, clinical history, communication abilities, behaviors, motor skills, eating, and drinking abilities were collected between 2005 and 2015. Intellectual disability was classified as “mild,” “moderate,” “severe,” or “profound” based on adaptive functioning, and according to the DSM-5 after 2013 and DSM-IV before 2013, the Wechsler Intelligence Scale for Children for patients up to ages 16 years, 11 months, and the Wechsler Adult Intelligence Scale for patients ages 17-18. Statistical analysis included Fisher’s exact test and multiple logistic regressions to identify factors associated with intellectual disability. A predictive machine learning model was developed to identify factors associated with having profound intellectual disability. The guidelines of the “Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis Statement” were followed. Results: Poor manual abilities ( P ≤ .001), gross motor function ( P ≤ .001), and type of epilepsy (intractable: P = .04; well controlled: P = .01) were significantly associated with profound intellectual disability. The average model accuracy, specificity, and sensitivity was 78%. Conclusion: Poor motor skills and epilepsy were associated with profound intellectual disability. The machine learning prediction model was able to adequately identify high likelihood of severe intellectual disability in teenagers with cerebral palsy.

scholarly journals Geochemical response of deep geothermal processes in the Litang region, Western Sichuan

2018 ◽  
Vol 37 (2) ◽  
pp. 626-645
Author(s):  
Wei Zhang ◽  
Guiling Wang ◽  
Linxiao Xing ◽  
Tingxin Li ◽  
Jiayi Zhao
Keyword(s):  
Groundwater Recharge ◽  
Hot Springs ◽  
Cold Water ◽  
Isotope Analysis ◽  
Geothermal Field ◽  
Geothermal System ◽  
Calcite Dissolution ◽  
Western Sichuan ◽  
Geothermal Fields ◽  
Positive Correlations

The geochemical characteristics of geothermically heated water can reveal deep geothermal processes, leading to a better understanding of geothermal system genesis and providing guidance for improved development and utilization of such resources. Hydrochemical and hydrogen oxygen isotope analysis of two geothermal field (district) hot springs based on regional geothermal conditions revealed that the thermal water in the Litang region is primarily of the HCO3Na type. The positive correlations found between F−, Li2+, As+, and Cl− indicated a common origin, and the relatively high Na+ and metaboric acid concentrations suggested a relatively long groundwater recharge time and a slow flow rate. The values of δD and δ18O were well distributed along the local meteoric line, indicating a groundwater recharge essentially driven by precipitation. The thermal reservoir temperature (152°C–195°C) and thermal cycle depth (3156–4070 m) were calculated, and the cold water mixing ratio (60%–68%) was obtained using the silica-enthalpy model. Finally, hydrogeochemical pathway simulation was used to analyze the evolution of geothermal water in the region. The results were further supported by the high metasilicate content in the region. Of the geothermal fields in the region, it was found that the Kahui is primarily affected by albite, calcite precipitation, and silicate, while the Gezha field is primarily affected by calcite dissolution, dolomite precipitation, and silicate.

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