Modeling and inversion of magnetic and VLF-EM data with an application to basement fractures: A case study from Raigarh, India

Geophysics ◽  
2007 ◽  
Vol 72 (5) ◽  
pp. B133-B140 ◽  
Author(s):  
V. Ramesh Babu ◽  
Subhash Ram ◽  
N. Sundararajan
Keyword(s):  
Spatial Distribution ◽  
Low Frequency ◽  
Analytical Signal ◽  
Magnetic Data ◽  
Fracture Zones ◽  
Very Low Frequency ◽  
Subsurface Resistivity ◽  
Spatial Locations ◽  
And Inversion

We present modeling of magnetic and very low frequency electromagnetic (VLF-EM) data to map the spatial distribution of basement fractures where uranium is reported in Sambalpur granitoids in the Raigarh district, Chhattisgarh, India. Radioactivity in the basement fractures is attributed to brannerite, [Formula: see text] complex, and uranium adsorbed on ferruginous matter. The amplitude of the 3D analytical signal of the observed magnetic data indicates the trend of fracture zones. Further, the application of Euler 3D deconvolution to magnetic data provides the spatial locations and depth of the source. Fraser-filtered VLF-EM data and current density pseudosections indicate the presence of shallow and deep conductive zones along the fractures. Modeling of VLF-EM data yields the subsurface resistivity distribution of the order of less than 100 ohm-m of the fractures. The interpreted results of both magnetic and VLF-EM data agree well with the geologic section obtained from drilling.

scholarly journals Very Low Frequency Electromagnetic Induction Surveys in Hydrogeological Investigations; Case Study from Poland

2016 ◽  
Vol 64 (6) ◽  
pp. 2322-2336 ◽  
Author(s):  
Szymon Oryński ◽  
Marta Okoń ◽  
Wojciech Klityński
Keyword(s):  
Electromagnetic Induction ◽  
Low Frequency ◽  
Very Low Frequency

Machine learning-based fracture-hit detection algorithm using LFDAS signal

2019 ◽  
Vol 38 (7) ◽  
pp. 520-524 ◽  
Author(s):  
Ge Jin ◽  
Kevin Mendoza ◽  
Baishali Roy ◽  
Darryl G. Buswell
Keyword(s):  
Machine Learning ◽  
Low Frequency ◽  
Detection Algorithm ◽  
Data Sets ◽  
Fracture Zones ◽  
Learning Techniques ◽  
Distributed Acoustic Sensing ◽  
Probability Of Fracture ◽  
Simple Neural Network

Low-frequency distributed acoustic sensing (LFDAS) signal has been used to detect fracture hits at offset monitor wells during hydraulic fracturing operations. Typically, fracture hits are manually identified, which can be subjective and inefficient. We implemented machine learning-based models using supervised learning techniques in order to identify fracture zones, which demonstrate a high probability of fracture hits automatically. Several features are designed and calculated from LFDAS data to highlight fracture-hit characterizations. A simple neural network model is trained to fit the manually picked fracture hits. The fracture-hit probability, as predicted by the model, agrees well with the manual picks in training, validation, and test data sets. The algorithm was used in a case study of an unconventional reservoir. The results indicate that smaller cluster spacing design creates denser fractures.

Observation and inversion of very-low-frequency seismo-acoustic fields in the South China Sea

2020 ◽  
Vol 148 (6) ◽  
pp. 3992-4001
Author(s):  
Shuyuan Du ◽  
Jingpu Cao ◽  
Shihong Zhou ◽  
Yubo Qi ◽  
Lei Jiang ◽  
...  
Keyword(s):  
South China Sea ◽  
South China ◽  
Low Frequency ◽  
The South China Sea ◽  
Acoustic Fields ◽  
The South ◽  
Very Low Frequency ◽  
China Sea ◽  
And Inversion

scholarly journals Pre-Emption of Affliction Severity Using HRV Measurements from a Smart Wearable; Case-Study on SARS-Cov-2 Symptoms

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7068
Author(s):  
Gatha Tanwar ◽  
Ritu Chauhan ◽  
Madhusudan Singh ◽  
Dhananjay Singh
Keyword(s):  
High Frequency ◽  
Viterbi Algorithm ◽  
Low Frequency ◽  
Very Low Frequency ◽  
Internal States ◽  
Hidden States ◽  
Complications And Mortality ◽  
Smart Wearable

Smart wristbands and watches have become an important accessory to fitness, but their application to healthcare is still in a fledgling state. Their long-term wear facilitates extensive data collection and evolving sensitivity of smart wristbands allows them to read various body vitals. In this paper, we hypothesized the use of heart rate variability (HRV) measurements to drive an algorithm that can pre-empt the onset or worsening of an affliction. Due to its significance during the time of the study, SARS-Cov-2 was taken as the case study, and a hidden Markov model (HMM) was trained over its observed symptoms. The data used for the analysis was the outcome of a study hosted by Welltory. It involved the collection of SAR-Cov-2 symptoms and reading of body vitals using Apple Watch, Fitbit, and Garmin smart bands. The internal states of the HMM were made up of the absence and presence of a consistent decline in standard deviation of NN intervals (SSDN), the root mean square of the successive differences (rMSSD) in R-R intervals, and low frequency (LF), high frequency (HF), and very low frequency (VLF) components of the HRV measurements. The emission probabilities of the trained HMM instance confirmed that the onset or worsening of the symptoms had a higher probability if the HRV components displayed a consistent decline state. The results were further confirmed through the generation of probable hidden states sequences using the Viterbi algorithm. The ability to pre-empt the exigent state of an affliction would not only lower the chances of complications and mortality but may also help in curbing its spread through intelligence-backed decisions.

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