Modeling Microgravity Anomalies That Accounts of the Pore Water Drainage Inferred from The Dc-Resistivity Sounding Data Over a Coal Panel

2021 ◽  
Vol 26 (2) ◽  
pp. 145-152
Author(s):  
Ersin Büyük ◽  
Abdullah Karaman
Keyword(s):  
Water Level ◽  
Longwall Mining ◽  
Local Density ◽  
Dc Resistivity ◽  
Water Drainage ◽  
Data Set ◽  
Legal Cases ◽  
Resistivity Sounding ◽  
Environmental Impact Assessments ◽  
Level Information

A microgravity data set presented in a previous study exhibits distinct short-wavelength anomalies over a longwall coal mine panel at Soma-Darkale Coalfield. Nevertheless, our preliminary models suggest that the wavelength after the coal removal from a panel at a moderate depth and fracturing alone should be incomparably greater than that of the measured anomalies. Understanding the mechanism that causes these anomalies usually becomes critical to provide credible evidence for longwall mining-related legal cases. This study improves the model by including the post-subsidence drainage as it occurs because of fracturing that causes an increase in water storage and local density change. Since no water-level information was available at the site, we attempted to infer the drained zone from the dc-resistivity sounding measurements acquired shortly before the gravity field survey. The wavelengths of the model anomalies became reasonably comparable with that of the residual anomalies after the inclusion of the inferred drainage information. This presented approach that does not require water level measurements shows that the inclusion of the inferred drained zone to the model became an amplifying indicator of a coal panel at a moderate depth. Therefore, it may easily find application in settling the mining-related legal cases, understanding the longwall mining-related geohazard, and environmental impact assessments.

scholarly journals Do galactic bars depend on environment?: an information theoretic analysis of Galaxy Zoo 2

2020 ◽  
Vol 501 (1) ◽  
pp. 994-1001
Author(s):  
Suman Sarkar ◽  
Biswajit Pandey ◽  
Snehasish Bhattacharjee
Keyword(s):  
Spatial Distribution ◽  
Mutual Information ◽  
Local Density ◽  
Statistical Significance ◽  
Distribution Functions ◽  
Cumulative Distribution ◽  
Host Galaxy ◽  
Data Sets ◽  
Data Set ◽  
Information Theoretic

ABSTRACT We use an information theoretic framework to analyse data from the Galaxy Zoo 2 project and study if there are any statistically significant correlations between the presence of bars in spiral galaxies and their environment. We measure the mutual information between the barredness of galaxies and their environments in a volume limited sample (Mr ≤ −21) and compare it with the same in data sets where (i) the bar/unbar classifications are randomized and (ii) the spatial distribution of galaxies are shuffled on different length scales. We assess the statistical significance of the differences in the mutual information using a t-test and find that both randomization of morphological classifications and shuffling of spatial distribution do not alter the mutual information in a statistically significant way. The non-zero mutual information between the barredness and environment arises due to the finite and discrete nature of the data set that can be entirely explained by mock Poisson distributions. We also separately compare the cumulative distribution functions of the barred and unbarred galaxies as a function of their local density. Using a Kolmogorov–Smirnov test, we find that the null hypothesis cannot be rejected even at $75{{\ \rm per\ cent}}$ confidence level. Our analysis indicates that environments do not play a significant role in the formation of a bar, which is largely determined by the internal processes of the host galaxy.

Magnetotelluric static shift: Estimation and removal using the cokriging method

Geophysics ◽  
2007 ◽  
Vol 72 (1) ◽  
pp. F25-F34 ◽  
Author(s):  
Benoit Tournerie ◽  
Michel Chouteau ◽  
Denis Marcotte
Keyword(s):  
Apparent Resistivity ◽  
A Priori ◽  
Shift Factor ◽  
Static Shift ◽  
Geostatistical Model ◽  
Data Set ◽  
Resistivity Sounding ◽  
The Difference ◽  
Cross Variogram

We present and test a new method to correct for the static shift affecting magnetotelluric (MT) apparent resistivity sounding curves. We use geostatistical analysis of apparent resistivity and phase data for selected periods. For each period, we first estimate and model the experimental variograms and cross variogram between phase and apparent resistivity. We then use the geostatistical model to estimate, by cokriging, the corrected apparent resistivities using the measured phases and apparent resistivities. The static shift factor is obtained as the difference between the logarithm of the corrected and measured apparent resistivities. We retain as final static shift estimates the ones for the period displaying the best correlation with the estimates at all periods. We present a 3D synthetic case study showing that the static shift is retrieved quite precisely when the static shift factors are uniformly distributed around zero. If the static shift distribution has a nonzero mean, we obtained best results when an apparent resistivity data subset can be identified a priori as unaffected by static shift and cokriging is done using only this subset. The method has been successfully tested on the synthetic COPROD-2S2 2D MT data set and on a 3D-survey data set from Las Cañadas Caldera (Tenerife, Canary Islands) severely affected by static shift.

scholarly journals The effect of water level in vertical aquaculture systems on production performance, biochemistry, hematology, and histology of Anguilla bicolor bicolor

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Eko Harianto ◽  
Eddy Supriyono ◽  
Tatag Budiardi ◽  
Ridwan Affandi ◽  
Yani Hadiroseyani
Keyword(s):  
Water Level ◽  
Normal Values ◽  
Stocking Density ◽  
Water Levels ◽  
Production Performance ◽  
Fish Weight ◽  
Important Study ◽  
Level Information ◽  
Gill Filaments ◽  
Skin Epidermis

AbstractThe water level in the cultivation of eel (Anguilla bicolor bicolor) is an important study in order to provide the optimal water level for cultivation. Optimizing the water level will affect the substitution of respiration energy with energy to grow. In addition, the water level information is related to the efficiency of water use for eel production in the future. Information on water level for eel production is still very limited, so this research is necessary to do. A total of 120 eel elver (initial weight 13.66 ± 0.09 g) were collected from eel companies in Bogor City, Indonesia. Fish were reared in vertical aquaculture systems with a stocking density of 10 fish per container for 60 days. The artificial feed containing 55% protein given as much as 3–5% of the biomass. Absorption and water replacement were done 20% per day. The result of this research showed that fish weight increased with an average of 33.45 ± 0.33 g. Different water levels had an impact to KKb, SGOT, ALP, and He. There was erosion of the skin epidermis and necrosis of the gill filaments due to the adaptation process. Water quality was within the optimum range for all treatments and 1.5 cm water level is recommended for maintenance (SGOT, ALP and He values were closest to normal values).

Artificial Intelligence Coreflooding Simulator for Special Core Data Analysis

2021 ◽  
pp. 1-29
Author(s):  
Eric Sonny Mathew ◽  
Moussa Tembely ◽  
Waleed AlAmeri ◽  
Emad W. Al-Shalabi ◽  
Abdul Ravoof Shaik
Keyword(s):  
Artificial Intelligence ◽  
Steady State ◽  
History Matching ◽  
Water Saturation ◽  
Flow Behavior ◽  
Model Performance ◽  
Analysis Data ◽  
Coefficient Of Determination ◽  
Water Drainage ◽  
Data Set

Two of the most critical properties for multiphase flow in a reservoir are relative permeability (Kr) and capillary pressure (Pc). To determine these parameters, careful interpretation of coreflooding and centrifuge experiments is necessary. In this work, a machine learning (ML) technique was incorporated to assist in the determination of these parameters quickly and synchronously for steady-state drainage coreflooding experiments. A state-of-the-art framework was developed in which a large database of Kr and Pc curves was generated based on existing mathematical models. This database was used to perform thousands of coreflood simulation runs representing oil-water drainage steady-state experiments. The results obtained from the corefloods including pressure drop and water saturation profile, along with other conventional core analysis data, were fed as features into the ML model. The entire data set was split into 70% for training, 15% for validation, and the remaining 15% for the blind testing of the model. The 70% of the data set for training teaches the model to capture fluid flow behavior inside the core, and then 15% of the data set was used to validate the trained model and to optimize the hyperparameters of the ML algorithm. The remaining 15% of the data set was used for testing the model and assessing the model performance scores. In addition, K-fold split technique was used to split the 15% testing data set to provide an unbiased estimate of the final model performance. The trained/tested model was thereby used to estimate Kr and Pc curves based on available experimental results. The values of the coefficient of determination (R2) were used to assess the accuracy and efficiency of the developed model. The respective crossplots indicate that the model is capable of making accurate predictions with an error percentage of less than 2% on history matching experimental data. This implies that the artificial-intelligence- (AI-) based model is capable of determining Kr and Pc curves. The present work could be an alternative approach to existing methods for interpreting Kr and Pc curves. In addition, the ML model can be adapted to produce results that include multiple options for Kr and Pc curves from which the best solution can be determined using engineering judgment. This is unlike solutions from some of the existing commercial codes, which usually provide only a single solution. The model currently focuses on the prediction of Kr and Pc curves for drainage steady-state experiments; however, the work can be extended to capture the imbibition cycle as well.

Comparisons of Environmental Ordinations with Principal Component Vegetational Ordinations for Sets of Data Having Different Degrees of Complexity

1971 ◽  
Vol 1 (2) ◽  
pp. 99-112 ◽  
Author(s):  
J. K. Jeglum ◽  
C. F. Wehrhahn ◽  
J. M. A. Swan
Keyword(s):  
Principal Component Analysis ◽  
Water Level ◽  
Principal Component ◽  
Low Complexity ◽  
Component Analysis ◽  
Two Dimensional ◽  
High Complexity ◽  
Water Conductivity ◽  
Data Set ◽  
Total Data

Data from a survey of lowland, mainly peatland, vegetation were subjected to environmental ordination based on measurements of water level and water conductivity, and to vegetational ordination derived from principal component analysis (P.C.A.). Analyzed were the total set of the data ("all types"), half sets ("nonwoody" and "woody types") and quarter sets (stands of "marshes", "meadows", "shrub fens", and "other woody types"); the number of distinct physiognomic groups in a set of data, and presumably the amount of contained heterogeneity, decreased at each segmentation.The effectiveness of the ordination models was tested by correlating measured distances in two-dimensional ordination models with 2W/(A + B) indices of vegetational similarity for randomly selected pairs of types or stands. As the physiognomic complexity decreased, the effectiveness of the P.C.A. vegetational ordination increased whereas that of the environmental ordination decreased. The environmental ordination seemed most appropriate to the data encompassing high complexity (total data set), while the P.C.A. vegetational ordination seemed most appropriate to data with low complexity (quarter sets of the data).

The inverse problem of resistivity sounding

Geophysics ◽  
1984 ◽  
Vol 49 (12) ◽  
pp. 2143-2158 ◽  
Author(s):  
Robert L. Parker
Keyword(s):  
Inverse Problem ◽  
Apparent Resistivity ◽  
Single Point ◽  
Quadratic Program ◽  
Data Set ◽  
Regularization Technique ◽  
Resistivity Sounding ◽  
Best Fitting ◽  
Electrical Data ◽  
Fitting Model

The electric potential due to a single point electrode at the surface of a layered conducting medium is calculated by means of a linear combination of the potentials associated with a set of two‐layer systems. This new representation is called the bilayer expansion for the Green’s function. It enables the forward problem of resistivity sounding to be solved very efficiently, even for complicated profiles. Also, the bilayer expansion facilitates the solution of the resistivity inverse problem: the coefficients in the expansion are linearly related to apparent resistivity as it is measured and they are readily mapped into parameters for a model. Specifically, I consider models comprising uniformly conducting layers of equal thickness; for a given finite data set a quadratic program can be used to find the best‐fitting model in this class for any specified thickness. As the thickness is reduced, models of this kind can approximate arbitrary profiles with unlimited accuracy. If there is a model that satisfies the data well, there are other models equally good or better whose variation takes place in an infinitesimally thin zone near the surface, below which there is a perfectly conducting region. This extraordinary class of solutions underscores the serious ambiguity in the interpretation of apparent resistivity data. It is evident that strong constraints from outside the electrical data set must be applied if reliable solutions are to be discovered. Previous work seems to have given a somewhat overly optimistic impression of the resolving abilities of this kind of data. I consider briefly a regularization technique designed to maximize the smoothness of models found with the bilayer inversion.

scholarly journals Strong Gravitational Lensing on the Hubble Deep Field

1999 ◽  
Vol 186 ◽  
pp. 439-445
Author(s):  
R. D. Blandford
Keyword(s):  
Gravitational Lensing ◽  
Small Area ◽  
Solid Angle ◽  
Local Density ◽  
Data Set ◽  
Strong Gravitational Lensing ◽  
Unique Data ◽  
Hubble Deep Field

The Hubble Deep Field, henceforth HDF, (Williams et al 1996) is a unique data set for studies of faint galaxies. A small area was chosen well away from known local density enhancements and sources of obscuration. It was imaged for 30 complete orbits in each of four filters centered on wavelengths λ300, 450, 606, 814 nm. There are three, contiguous WFPC2 frames and a solid angle 15,500 arcsec2 is usable. This tiny region is teeming with galaxies. At least 2500 significant features can be identified down to R = 30, though it is not clear that these are all entire galaxies.

Seepage Monitoring of an Embankment Dam Using Resistivity Method: A Case Study of LUSI Mud Volcano P.79 - P.82 Embankment

2015 ◽  
Vol 771 ◽  
pp. 213-217 ◽  
Author(s):  
Alwi Husein ◽  
Bagus Jaya Santosa ◽  
Ayi Syaeful Bahri
Keyword(s):  
Water Level ◽  
Resistivity Measurement ◽  
Mud Volcano ◽  
Dc Resistivity ◽  
Embankment Dam ◽  
Water Seepage ◽  
Resistivity Method ◽  
Embankment Height ◽  
Non Destructive

Since the Lusi Mud volcano erupts in Sidoarjo, East Java, on May 2006, soil embankments have been built to keep hot mud within the ponds. Unfortunately, since the embankments were sitting on a poorly weak ground, land subsidence intensively occurred around the embankments. These subsidences are mainly caused by water seepage penetrating the embankment. To observe the part of the embankment that is vulnerable to water seepage, DC Resistivity method, being a non-destructive and versatile method, is used to monitor the subsurface condition of the embankment. P.79 - P.82 embankment, which has collapsed on December 2010 is highlighted in this study because it has the lowest height of all part the embankment, directly adjacent with water from the mud volcano in the inside and field crop on the outside. The research will show the fluctuating results of the water seepage in the embankment during the timeline of February 2012 - October 2013 which, unlike the result of another area, is highly affected by the water level at each measurement. The embankment height during each DC Resistivity measurement is also measured to compare the subsurface result with the condition on the surface. The resistivity subsurface cross section shows increasing water seepage in each measurement (February 2012, July 2012 and October 2013). Alarming seepage level displayed in December 2012 which is presumably triggered by the increase of water level during rainy season is also followed by cracks occurred in the surface.

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