scholarly journals A Stratigraphic Prediction Method Based on Machine Learning

2019 ◽  
Vol 9 (17) ◽  
pp. 3553 ◽  
Author(s):  
Cuiying Zhou ◽  
Jinwu Ouyang ◽  
Weihua Ming ◽  
Guohao Zhang ◽  
Zichun Du ◽  
...  
Keyword(s):  
Machine Learning ◽  
Evaluation Method ◽  
Three Dimensional ◽  
Prediction Method ◽  
Geological Modeling ◽  
Prediction Ability ◽  
3D Geological Modeling ◽  
Planning And Design ◽  
Novel Approach ◽  
Stratum Type

Simulation of a geostratigraphic unit is of vital importance for the study of geoinformatics, as well as geoengineering planning and design. A traditional method depends on the guidance of expert experience, which is subjective and limited, thereby making the effective evaluation of a stratum simulation quite impossible. To solve this problem, this study proposes a machine learning method for a geostratigraphic series simulation. On the basis of a recurrent neural network, a sequence model of the stratum type and a sequence model of the stratum thickness is successively established. The performance of the model is improved in combination with expert-driven learning. Finally, a machine learning model is established for a geostratigraphic series simulation, and a three-dimensional (3D) geological modeling evaluation method is proposed which considers the stratum type and thickness. The results show that we can use machine learning in the simulation of a series. The series model based on machine learning can describe the real situation at wells, and it is a complimentary tool to the traditional 3D geological model. The prediction ability of the model is improved to a certain extent by including expert-driven learning. This study provides a novel approach for the simulation and prediction of a series by 3D geological modeling.

A stacking methodology of machine learning for 3D geological modeling with geological-geophysical datasets, Laochang Sn camp, Gejiu (China)

2021 ◽  
pp. 104754
Author(s):  
Ran Jia ◽  
Yikai Lv ◽  
Gongwen Wang ◽  
EmmanuelJohnM. Carranza ◽  
Yongqing Chen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Geological Modeling ◽  
3D Geological Modeling

scholarly journals Summary on Several Key Techniques in 3D Geological Modeling

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Gang Mei
Keyword(s):  
Mesh Generation ◽  
Spatial Interpolation ◽  
Three Dimensional ◽  
Geological Modeling ◽  
3D Geological Modeling ◽  
Surface Intersection ◽  
Key Techniques

Several key techniques in 3D geological modeling including planar mesh generation, spatial interpolation, and surface intersection are summarized in this paper. Note that these techniques are generic and widely used in various applications but play a key role in 3D geological modeling. There are two essential procedures in 3D geological modeling: the first is the simulation of geological interfaces using geometric surfaces and the second is the building of geological objects by means of various geometric computations such as the intersection of surfaces. Discrete geometric surfaces that represent geological interfaces can be generated by creating planar meshes first and then spatially interpolating; those surfaces intersect and then form volumes that represent three-dimensional geological objects such as rock bodies. In this paper, the most commonly used algorithms of the key techniques in 3D geological modeling are summarized.

Rapid 3D geological modeling to assess and visualize uncertainties in a web application

2021 ◽  
Author(s):  
Daniel Pflieger ◽  
Miguel de la Varga Hormazabal ◽  
Simon Virgo ◽  
Jan von Harten ◽  
Florian Wellmann
Keyword(s):  
Web Application ◽  
High Performance ◽  
Three Dimensional ◽  
Cloud Services ◽  
Management Approach ◽  
Geological Modeling ◽  
Web Browser ◽  
3D Geological Modeling ◽  
State Management ◽  
Wide Range

<p>Three dimensional modeling is a rapidly developing field in geological scientific and commercial applications. The combination of modeling and uncertainty analysis aides in understanding and quantitatively assessing complex subsurface structures. In recent years, many methods have been developed to facilitate this combined analysis, usually either through an extension of existing desktop applications or by making use of Jupyter notebooks as frontends. We evaluate here if modern web browser technology, linked to high-performance cloud services, can also be used for these types of analyses.</p><p>For this purpose, we developed a web application as proof-of-concept with the aim to visualize three dimensional geological models provided by a server. The implementation enables the modification of input parameters with assigned probability distributions. This step enables the generation of randomized realizations of models and the quantification and visualization of propagated uncertainties. The software is implemented using HTML Web Components on the client side and a Python server, providing a RESTful API to the open source geological modeling tool “GemPy”. Encapsulating the main components in custom elements, in combination with a minimalistic state management approach and a template parser, allows for high modularity. This enables rapid extendibility of the functionality of the components depending on the user’s needs and an easy integration into existing web platforms.</p><p>Our implementation shows that it is possible to extend and simplify modeling processes by creating an expandable web-based platform for probabilistic modeling, with the aim to increase the usability and to facilitate access to this functionality for a wide range of scientific analyses. The ability to compute models rapidly and with any given device in a web browser makes it flexible to use, and more accessible to a broader range of users.</p>

Abstract 2917: Outcome Prediction With A Population-based Ischemic Stroke Atlas

Stroke ◽  
2012 ◽  
Vol 43 (suppl_1) ◽  
Author(s):  
Wieslaw L Nowinski ◽  
Varsha Gupta ◽  
Guoyu Qian ◽  
Wojciech Ambrosius ◽  
Jie He ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Outcome Prediction ◽  
Three Dimensional ◽  
Prediction Method ◽  
Population Based ◽  
Novel Approach ◽  
History Of ◽  
High Prediction ◽  
Predicted Values ◽  
One Year

Outcome prediction is critical in stroke patient management. We propose a novel approach combining imaging with parameters (including history, hospitalization, demographics, clinical and outcome) for a population of patients in the Probabilistic Stroke Atlas (PSA) along with prediction engine. The PSA aggregates multiplicity of data for a population of stroke patients and presents them in image format. The PSA is composed from a series of three-dimensional (3D) image volumes including scans and parameters. A cohort of over 700 ischemic stroke generally treated patients with 176 parameters per patient, and CT scan performed at admission and on day 7 was acquired. Outcome measurements were assessed up to one year after stroke onset. Cases with old infarcts, infarcts in both hemispheres, and hemorrhagic transformations were rejected. This data was post-processed to build the PSA and then the PSA was used for prediction. The infarcts were delineated on CT scans and their 3D surface models constructed and normalized. The PSA was calculated from the normalized 3D infarct models as frequency of stroke occurrence. Similar maps were calculated for the following parameters: Age; Sex; Survival; NIH Stroke Scale (NIHSS); Barthel Index (BI) at 30, 90, 180, 360 days; modified Rankin Scale (mRS) at 7, 30, 90, 180, 360 days; White blood cell count; C-reative protein; Glucose at emergency department; History of hypertension; and History of diabetes. The PSA was used for prediction of mRS and BI for 50 stroke subjects. For a given case to be predicted, the infarct was delineated and analyzed by the PSA mapped on the scan. The predicted values of the parameters from the PSA were compared with the actual values of the parameters measured in up to 1-year neurological follow up. The accuracy was defined as 100*(1-(actual value-predicted value)/actual value)%. The mean prediction accuracy of mRS at (7, 30, 90, 180, 360) days is (89.7, 90.7, 92.1, 87.0, 83.3)% and that for BI at (30, 90, 180, 360) days is (90.0, 95.4, 94.4, 92.2)% respectively. This novel prediction method has high prediction rates. It can be applied to any other parameters. The PSA is dynamic and its power can increase with additional cases.

scholarly journals Integration of 3D Geological Modeling and Geothermal Field Analysis for the Evaluation of Geothermal Reserves in the Northwest of Beijing Plain, China

Water ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 638
Author(s):  
Zhenzhou Zhu ◽  
Xiaodong Lei ◽  
Nengxiong Xu ◽  
Dongyue Shao ◽  
Xingyu Jiang ◽  
...  
Keyword(s):  
Heat Flow ◽  
Three Dimensional ◽  
Scientific Basis ◽  
Geothermal Field ◽  
Field Analysis ◽  
Geological Modeling ◽  
Geothermal Resources ◽  
3D Geological Modeling ◽  
Beijing Plain ◽  
Distribution Of Resources

With the increasing demand for energy and the growing concern for atmospheric pollution in Beijing, China, the exploitation and utilization of geothermal resources are becoming more desirable. The study combined three-dimensional geological modeling with geothermal field analysis to make clear the potential and distribution of geothermal resources in the northwest of the Beijing plain, which could provide a scientific basis for rational utilization in the study area. Based on the analysis of the geological data and geothermal conditions, we created a 3D geological model of the study area, and then added isothermal surfaces into the model and analyzed the heat flow to enhance the understanding of the present geothermal field. After that, the volumes of different temperature intervals of heat reservoirs were calculated accurately and automatically by the integration of the model and the isothermal surfaces. Finally, the geothermal reserves were calculated by the improved volumetric method, and the distribution of resources was analyzed comprehensively. The results showed that, in the study area, the heat flow values ranged from 49 to 99 mW m−2, and the average elevations of 25 °C, 40 °C, and 60 °C isothermal surfaces were at −415 m, −1282 m, and −2613 m, respectively. The geothermal reserves were 5.42 × 1019 J and the volume of the heat reservoir was 4.88 × 1011 m3. The geothermal resources of the study area had good potential and could support local green development.

AUTONOMOUS BEHAVIORS OF GRAPHICAL AVATARS BASED ON MACHINE LEARNING

2012 ◽  
Vol 26 (02) ◽  
pp. 1251002
Author(s):  
YUESHENG HE ◽  
YUAN YAN TANG
Keyword(s):  
Machine Learning ◽  
Hierarchical Structure ◽  
Learning Algorithm ◽  
Three Dimensional ◽  
Research Area ◽  
Machine Learning Techniques ◽  
3D Animation ◽  
Graphical Environment ◽  
Novel Approach ◽  
The Difference

Graphical avatars have gained popularity in many application domains such as three-dimensional (3D) animation movies and animated simulations for product design. However, the methods to edit avatars' behaviors in the 3D graphical environment remained to be a challenging research topic. Since the hand-crafted methods are time-consuming and inefficient, the automatic actions of the avatars are required. To achieve the autonomous behaviors of the avatars, artificial intelligence should be used in this research area. In this paper, we present a novel approach to construct a system of automatic avatars in the 3D graphical environments based on the machine learning techniques. Specific framework is created for controlling the behaviors of avatars, such as classifying the difference among the environments and using hierarchical structure to describe these actions. Because of the requirement of simulating the interactions between avatars and environments after the classification of the environment, Reinforcement Learning is used to compute the policy to control the avatar intelligently in the 3D environment for the solution of the problem of different situations. Thus, our approach has solved problems such as where the levels of the missions will be defined and how the learning algorithm will be used to control the avatars. In this paper, our method to achieve these goals will be presented. The main contributions of this paper are presenting a hierarchical structure to control avatars automatically, developing a method for avatars to recognize environment and presenting an approach for making the policy of avatars' actions intelligently.

scholarly journals PPI_SVM: Prediction of protein-protein interactions using machine learning, domain-domain affinities and frequency tables

2011 ◽  
Vol 16 (2) ◽  
Author(s):  
Piyali Chatterjee ◽  
Subhadip Basu ◽  
Mahantapas Kundu ◽  
Mita Nasipuri ◽  
Dariusz Plewczynski
Keyword(s):  
Machine Learning ◽  
Protein Interactions ◽  
Three Dimensional ◽  
Prediction Method ◽  
Protein Sequences ◽  
Dimensional Structure ◽  
Support Vector ◽  
Interacting Proteins ◽  
Protein Protein Interactions ◽  
Protein Functions

AbstractProtein-protein interactions (PPI) control most of the biological processes in a living cell. In order to fully understand protein functions, a knowledge of protein-protein interactions is necessary. Prediction of PPI is challenging, especially when the three-dimensional structure of interacting partners is not known. Recently, a novel prediction method was proposed by exploiting physical interactions of constituent domains. We propose here a novel knowledge-based prediction method, namely PPI_SVM, which predicts interactions between two protein sequences by exploiting their domain information. We trained a two-class support vector machine on the benchmarking set of pairs of interacting proteins extracted from the Database of Interacting Proteins (DIP). The method considers all possible combinations of constituent domains between two protein sequences, unlike most of the existing approaches. Moreover, it deals with both single-domain proteins and multi domain proteins; therefore it can be applied to the whole proteome in high-throughput studies. Our machine learning classifier, following a brainstorming approach, achieves accuracy of 86%, with specificity of 95%, and sensitivity of 75%, which are better results than most previous methods that sacrifice recall values in order to boost the overall precision. Our method has on average better sensitivity combined with good selectivity on the benchmarking dataset. The PPI_SVM source code, train/test datasets and supplementary files are available freely in the public domain at: http://code.google.com/p/cmater-bioinfo/.

Three Dimensional Fine Structure Modeling of Haqian1 Wellblock, in Northern Dzungaria Basin, China

2013 ◽  
Vol 734-737 ◽  
pp. 488-492
Author(s):  
Chen Qiang Dong ◽  
Fang Ding ◽  
Wei Wei Ren
Keyword(s):  
Three Dimensional ◽  
Fault Model ◽  
Structure Modeling ◽  
Good Prospect ◽  
Geological Model ◽  
Structure Model ◽  
Geological Modeling ◽  
Geological Exploration ◽  
3D Geological Modeling ◽  
Set Up

Haqian wellblock has a very good prospect in Dzungaria Basin, as it developed many faults and some formations are truncated, the development situation of it is very complicated, in this paper, we applied 3D geological modeling method which is one of the most important technology methods in describing the underground development situation, to illustrate the intricate structure. This geological model involved computer modeling and visualization of geological fault in 3D, the type of data of geological faults based on geological exploration is analyzed, after the fault model and horizon model are built, a whole structure model is finally set up.

Study on Algorithm for Grid Subdivision and Encryption Based on Technology of Three Dimensional Geological Modeling

2013 ◽  
Vol 336-338 ◽  
pp. 1416-1421
Author(s):  
Wei He ◽  
Wen Li Wu
Keyword(s):  
Cross Sections ◽  
Optimal Path ◽  
Three Dimensional ◽  
Surrounding Rock ◽  
Geological Modeling ◽  
Survey Area ◽  
Uniform Size ◽  
3D Geological Modeling ◽  
3D Space ◽  
The Relationship

To achieve 3D grid models which have a non-uniform size and varying properties, we proposed the algorithm of grid subdivision and encryption by human-computer interaction. This algorithm was the technology based on 3D geological modeling, and achieving process has following three steps. Firstly, we converted many 2D cross sections to 3D space, and reconstructed 3D vector models using the algorithm of optimal path suture, and set the property of abnormal body and surrounding rock. Then, achieving 3D grids subdivided according to the relationship between the center of 3D grid and 3D vector models, the properties of 3D grids were determined. Finally, we encrypted grids in the survey area and expansion area, and modified the properties. The results show that the algorithm can realize the conversion from 3D vector models to 3D grid models, and this process is reliable and efficient.

scholarly journals DEVELOPMENT OF THE METHODOLOGY OF 3D GEOLOGICAL MODELING FOR THE RESERVOIRS OF THE PALEOZOIC BASEMENT OF WEST SIBERIA

2017 ◽  
pp. 36-40
Author(s):  
A. I. Tseplyaeva
Keyword(s):  
Economic Effect ◽  
Three Dimensional ◽  
West Siberia ◽  
Dual Porosity ◽  
Geological Model ◽  
Geological Modeling ◽  
3D Geological Modeling ◽  
Paleozoic Basement ◽  
Geological Models

The represented method allows to create three-dimensional geological models of collectors of paleozoic basement, which provides a significant economic effect in the subsequent deposit explorations for typical russian companies - subsoil users, having a limited amount of data. In geological modeling of the collectors of paleozoic basement, the application of the method of dual porosity (double medium) is most relevant. The created approach allows to refine the geological model with an increase of geological reserves by 30 % in reservoirs with natural fracturing.

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