Creating a 3D model would isolate optimal zones for total reserves of economically extractable gold in mining industry, the groundwater contamination flow from waste infiltrated in environmental case, etc. - Rockware.com
it is highly useful for geography students in the field of remote sensing and it is in very simple and explanatory for the purpose of simplification with relevant images in this ppt.
it is highly useful for geography students in the field of remote sensing and it is in very simple and explanatory for the purpose of simplification with relevant images in this ppt.
About Remote Sensing
Introduction To Precipitation
Forms of Precipitation
Measurement & Measuring Methods of Precipitation
Outcomes of Extreme Precipitation
DEFINITION :
GIS is a powerful set of tools for collecting, storing , retrieving at will, transforming and displaying spatial data from the real world for a particular set of purposes
APPLICATION AREAS OF GIS
Agriculture
Business
Electric/Gas utilities
Environment
Forestry
Geology
Hydrology
Land-use planning
Local government
Mapping
11. Military
12. Risk management
13. Site planning
14. Transportation
15. Water / Waste water industry
COMPONENTS OF GIS
DATA INPUT
SPATIAL DATA MODEL
Data Model:
It describes in an abstract way how the data is represented in an information system or in DBMS
Spatial Data Model :
The models or abstractions of reality that are intended to have some similarity with selected aspects of the real world
Creation of analogue and digital spatial data sets involves seven levels of model development and abstraction
SPATIAL DATA MODEL
Conceptual model : A view of reality
Analog model : Human conceptualization leads to analogue abstraction
Spatial data models : Formalization of analogue abstractions without any conventions
Database model : How the data are recorded in the computer
Physical computational model : Particular representation of the data structures in computer memory
Data manipulation model : Accepted axioms and rules for handling the data
SPATIAL DATA MODEL
SPATIAL DATA MODEL
Objects on the earth surface are shown as continuous and discrete objects in spatial data models
Types of data models
Raster data model
vector data models
RASTER DATA MODEL
Basic Elements :
Extent
Rows
Columns
Origin
Orientation
Resolution: pixel = grain = grid cell
Ex: Bit Map Image (BMP),Joint Photographic Expert Group (JPEG), Portable Network Graphics(PNG) etc
RASTER DATA MODEL
VECTOR DATA MODEL
Basic Elements:
Location (x,y) or (x,y,z)
Explicit, i.e. pegged to a coordinate system
Different coordinate system (and precision) require different values
o e.g. UTM as integer (but large)
o Lat, long as two floating point numbers +/-
Points are used to build more complex features
Ex: Auto CAD Drawing File(DWG), Data Interchange(exchange) File(DXF), Vector Product Format (VPF) etc
VECTOR DATA MODEL
RASTER vs VECTORRaster is faster but Vector is corrector
TESSELLATIONS OF CONTINUOUS FIELDS
Triangular Irregular Network: (TIN)
TIN is a vector data structure for representing geographical information that is continuous
Digital elevation model
TIN is generally used to create Digital Elevation Model (DEM)
DIGITAL ELEVATION MODEL
DATA STRUCTURES
Data structure tells about how the data is stored
Data organization in raster data structures
Each cell is referenced directly
Each overlay Is referenced directly
Each mapping unit is referenced directly
Each overlay is separate file with general header
About Remote Sensing
Introduction To Precipitation
Forms of Precipitation
Measurement & Measuring Methods of Precipitation
Outcomes of Extreme Precipitation
DEFINITION :
GIS is a powerful set of tools for collecting, storing , retrieving at will, transforming and displaying spatial data from the real world for a particular set of purposes
APPLICATION AREAS OF GIS
Agriculture
Business
Electric/Gas utilities
Environment
Forestry
Geology
Hydrology
Land-use planning
Local government
Mapping
11. Military
12. Risk management
13. Site planning
14. Transportation
15. Water / Waste water industry
COMPONENTS OF GIS
DATA INPUT
SPATIAL DATA MODEL
Data Model:
It describes in an abstract way how the data is represented in an information system or in DBMS
Spatial Data Model :
The models or abstractions of reality that are intended to have some similarity with selected aspects of the real world
Creation of analogue and digital spatial data sets involves seven levels of model development and abstraction
SPATIAL DATA MODEL
Conceptual model : A view of reality
Analog model : Human conceptualization leads to analogue abstraction
Spatial data models : Formalization of analogue abstractions without any conventions
Database model : How the data are recorded in the computer
Physical computational model : Particular representation of the data structures in computer memory
Data manipulation model : Accepted axioms and rules for handling the data
SPATIAL DATA MODEL
SPATIAL DATA MODEL
Objects on the earth surface are shown as continuous and discrete objects in spatial data models
Types of data models
Raster data model
vector data models
RASTER DATA MODEL
Basic Elements :
Extent
Rows
Columns
Origin
Orientation
Resolution: pixel = grain = grid cell
Ex: Bit Map Image (BMP),Joint Photographic Expert Group (JPEG), Portable Network Graphics(PNG) etc
RASTER DATA MODEL
VECTOR DATA MODEL
Basic Elements:
Location (x,y) or (x,y,z)
Explicit, i.e. pegged to a coordinate system
Different coordinate system (and precision) require different values
o e.g. UTM as integer (but large)
o Lat, long as two floating point numbers +/-
Points are used to build more complex features
Ex: Auto CAD Drawing File(DWG), Data Interchange(exchange) File(DXF), Vector Product Format (VPF) etc
VECTOR DATA MODEL
RASTER vs VECTORRaster is faster but Vector is corrector
TESSELLATIONS OF CONTINUOUS FIELDS
Triangular Irregular Network: (TIN)
TIN is a vector data structure for representing geographical information that is continuous
Digital elevation model
TIN is generally used to create Digital Elevation Model (DEM)
DIGITAL ELEVATION MODEL
DATA STRUCTURES
Data structure tells about how the data is stored
Data organization in raster data structures
Each cell is referenced directly
Each overlay Is referenced directly
Each mapping unit is referenced directly
Each overlay is separate file with general header
John McGaughey, CEO/President of Mira Geoscience offers his thoughts and the practices of integrated geophysical interpretation at the 3D Interest Group
3d hydrogeological conceptual model building in denmarkTorben Bach
Lessons learned from the Danish groundwater mapping campaign
Presented October 4th at the 2017 Groundwater Resources Association meeting in Sacramento, CA
Presenter: Torben Bach, I-GIS
Presentation from EuroSDR 113th meeting, Cardiff, October 2008. An overview of some of the geospatial research carried out by the different departments, centres and groups at UCL.
Robust 3D Geological Models: Hard Data is KeyFF Explore 3D
Understanding and incorporating 2D data, whether from surface field work or underground mine mapping, should always be the starting point of an integrated and coherent 3D geologic model, especially for areas with great geometric contrasts. Without this valuable data, 3D modelling is essentially performed with blinders on, and its absence results in a model that is too theory-driven, and lacks input from geologists and “real” field data.
Three-dimensional geologic models require complete, homogeneous and valid databases. The resulting 3D models are directly based on and rely on high-quality data. The data comprises both surface and underground observations. “Raw” or “hard” data should always be assigned more weight and act as rigid control points in 3D models. Hard data should always be distinguishable from interpreted data in 3D models. Investing the necessary time to learn how to homogenize and structure raw data in a rigorous way will be paid back during the 3D interpretation process.
Once 3D models are completed, they should be used as an exploration tool, populating their cells with user-chosen properties. Both quantitative and qualitative properties can be interpolated throughout the cells of the 3D model for further querying and questioning. Thus, the extra benefit of 3D map models is their use as dynamic interactive tools to help define new mineral exploration targets at depth.
A 3D map model is not a goal but a tool that should be dynamic, modified, questioned, shared and updated. Its future usefulness is determined by how well it can be utilized by a multi-disciplinary team of geologists, geophysicists, geochemists, engineers, metallurgists and environmental experts.
This talk opened the geospatial track of the Apache Big Data conference. The geospatial track aimed to increase the benefits of implementing open source consistent with open geospatial standards.
After an introduction of the geospatial track this talk focused on these topics:
- Applications of Big Geo Data
- Geospatial Open Standards
- Big Geo Use Cases
- Open Source and Open Standards.
Inventorying and Acquiring Existing GIS ResourcesDaniele Baker
Presentation on acquiring and using GIS resources for Texas projects. Presented at the Texas Watershed Planning Project training hosted by Texas Water Resources Institute (TWRI) in conjunction with Texas Commission on Environmental Quality (TCEQ).
DSD-NL 2018 Evolutie in het leveren van ruimtelijke en temporele water gerela...Deltares
Presentatie door Arnold Dekker, SatDek, Australian National University, op de Delft-FEWS NL Gebruikersdag 2018, tijdens de Deltares Software Dagen - Editie 2018. Dinsdag, 5 juni 2018, Delft.
Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
For more technical information, visit our website https://intellaparts.com
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
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Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
4. Introduction
How to determine the subsurface of Banda Aceh City ?
No directional survey
• Geophysics
• Geological Map Cross-Section
Directional survey
• Civil Engineering (Boring)
• Well Exploration
• Pit Testing for mineral exploration
10. We need :
• Data collection (e.g. Geophysics, Geology, Mining, Geotechnical, Environment)
• That data must have X, Y, Z (spatial) and others value of main parameter
• Computer and Software ( Ex: Rockworks and ArcGIS)
• Basics and advanced concept about Geostatistics
• Lithology and Stratigraphy knowledge for a Geoscientist
12. • The Distribution of Soils/Rocks vertically or horizontally
• Visualization such as : cross-section, fence diagrams, block diagrams
• Calculation of Volumetrics
Important : Creating a 3D model would isolate optimal zones for total reserves of
economically extractable gold in mining industry, the groundwater contamination
flow from waste infiltrated in environmental case, etc.
13. Data Management X : 10.000 (mU)
Y : 12.000 (mT)
Z : 15 (m dpl)
Total Depth of Dowhole : 120 m
Other Information Borehole data
16. Lithology versus Stratigraphy
• Lithology : rocks description based on that characteristics (Jackson, 1985)
• Rock classification into 4 types : Igneous, Sedimentary, Metamorphic, and
Pyroclastics
17. • Viewed into a tab : listing depth to depth, and rock or material type
Rock typeIn the Field Depth to Depth (m)
Lithology versus Stratigraphy
18. • Often repeating others depth
Location 1 Location 2 Location 3
Tab Correlation
Lithology versus Stratigraphy
19. • Stratigraphy : rock layers and stratification based on lithology, environment and
depositional age
• Sub-Fields into 3 types : Lithostratigraphy, Biostratigraphy, Chronostratigraphy
Lithology versus Stratigraphy
LithostratigraphyandChronostratigraphy
Biostratigraphy and Chronostratigraphy
21. • Never repeat others depth
Tab Correlation
Lithology versus Stratigraphy
22. Modelling
• 2D/3D-Modelling is depend upon the number of independent variables
• In a two-dimensional array, the dependent variable (z) is a function of the horizontal
(x,y) coordinates. Also referred to as a “GRID MODEL”
23. Modelling
• In a three-dimensional array, the dependent variable(g) is a function of the horizontal
(x,y) and vertical coordinates (z). Also referred to as a “SOLID MODEL”
24. Geostastistics Basics
• 2D Modelling
Popular method to
Interpolate is “IDW”
Inversed Distance Weigh
Longitude
Latitude
Z Value
25. Popular method to Interpolate is “IDW”
Inversed Distance Weigh
Geostastistics Basics
26. Geostastistics Basics
• 2D Modelling
Used to model topography,
stratigraphic contacts,
isopachs, and water
levels
27. Geostastistics Basics
• 3D Modelling
There is simply the three-
dimensional version of
gridding.
The creation of Supercomputers
Used to model geochemistry, contaminants, ore
grades, metals, and geotechnical properties
28. Visualization
• Diagrams of grids and solids are more than "eye candy“
• Viewed in cross-section, fence diagrams, or block diagrams
• Comparing the original data with interpolated model
31. Volumetric Computation
• Model has been created
• Computing the volume : simple mathematical operation with cell values
• Usually in a “isopach map” layer selected
=
“isopach map” selected
• All cells added together (Total = 272 m), and multiplied by cell size ( 50 m x 50
m) ; referred as “Length x width” . The volume is 680.000 m3
• Then multiplied by density (Rho) of material was conversion factor (e.g. density
of coal = 1.826 metric tons per cubic meter). The tonnage is 1,2m metric tons)
32. Volumetric Computation
=
“isopach map” selected
• Volume = 272 m x 50 m x 50 m
= 680.000 m3
• Tonnage = 680.000 m3 x 1,826 metric tons / m3
= 1.241.680 metrics tons (1,2m metric tons)