design and implementation of a dedicated prototype gis for coal

7/29/2019 Design and Implementation of a Dedicated Prototype GIS for Coal

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Design and implementation of a dedicated prototype GIS for coal

fire investigations in North China

Anupma Prakasha,*, Zoltan Vekerdyb

aGeophysical Institute, University of Alaska Fairbanks, 903 Koyukuk Drive, Fairbanks, AK 99775-7320, USAbDepartment of Water Resources, International Institute for Geo-Information Science and Earth Observation (ITC), P.O. Box 6, 7500 AA

Enschede, The Netherlands

Received 21 May 2003; accepted 10 December 2003

Available online 18 March 2004

Abstract

This paper presents the design architecture and functioning of CoalMan, a tailor made Geographic Information System (GIS)

for managing surface and underground fires in coal mining areas. CoalMan is specially designed for and installed in the Rujigou

coal field in north-west China. It uses ILWIS as the supporting GIS package. It functions through its database and management

tools, processing and analysis tools and featured display tools. The processing and analysis tools are uniquely designed to

detect, map, and monitor coal mine fires in time. These tools also help to generate maps showing fire depth, fire risk and priority

for fire fighting. The display tools help to generate cross-sectional views along any selected profile line in the study area.CoalMan has a bilingual interface and has a potential to be adapted to other coal mining areas facing similar problems.

D 2004 Elsevier B.V. All rights reserved.

Keywords: Remote sensing; GIS; Coal; Fires; Monitoring; China

1. Introduction

The worlds coal deposits, the primary source of

fossil fuel, are endangered by fires. These fires

may start spontaneously, by mining related activi-

ties or other causes. They may occur on thesurface or underground in deeper layers of coal

deposits. If not detected and tackled at an early

stage, these fires burn uncontrollably, consuming

the precious non-renewable energy resource, ham-

pering mining activities, causing immense environ-

mental pollut- ion and proving to be a threat to the

neighboring areas.

Though the problem of coal mine fires is global in

nature, it takes a different magnitude in North China,

where the major coal deposits, with an eastwest span

of about 5000 km and a north south span of about 700km, are studded with occurrences of coal fires (Guan,

1989) (Fig. 1). These fires reportedly burn coal equiv-

alent to about a fifth of Chinas total coal export

causing millions of dollars of financial loss each year.

The carbon dioxide, methane and other gases released

from these fires are now envisaged as a significant

contributor to the global greenhouse effect.

Studying all aspects related to coal mine fires

requires a multidisciplinary approach and a Geograph-

ic Information System (GIS) powered scientific in-

0166-5162/$ - see front matterD 2004 Elsevier B.V. All rights reserved.doi:10.1016/j.coal.2003.12.009

* Corresponding author. Tel.: +1-907-474-1897; fax: +1-907-

474-7290.

E-mail address: [email protected] (A. Prakash).

www.elsevier.com/locate/ijcoalgeo

International Journal of Coal Geology 59 (2004) 107119


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vestigation tool. This paper discusses the concepts,

structure and implementation of CoalMan, a coal fire

monitoring and management information system, nowimplemented in a coal field in north-west China.

1.1. Objective

The broad objectives of the present study are

to demonstrate the applicability of remote sensing

and GIS-based tools to study and tackle the

problem of surface and underground fires in coal

mining areas;

to design a prototype GIS (CoalMan) meeting thespecific needs of coal mine fire analysis and

management; to implement and test the practical applicability of

the system in a selected test site in China.

1.2. The need for a GIS

Commercial GIS software packages generally lack

the basic functionality required to address the issues

pertinent to coal mine fires. The limitations surface at

different stages starting from the lack of a user-

friendly and user-specific interface; restricted spatio-

temporal data handling ability required to modeldynamic processes; finite potential for data analysis;

and inadequate representation capabilities to display

the models of reality (Thumerer et al., 2000; Vascon-

celos et al., 2002).

The above limitations along with other specific

requirements for the coal fire application domain,

call for the designing of a tailor-made GIS package.

CoalMan was designed with these requirements in

mind. Though the concepts of the package are

based on the well-known principles of GIS, special

tools, models, processing steps are incorporated sothat the system meets with the following specific

expectations.

Detect and map coal fire areas using direct and

indirect indicators. Monitor the coal fires over time using multitemporal

remote sensing and field based information. Quantify the shape, size, and depth of fires. Generate and display coal fire risk maps. Prioritize and plan fire fighting activities.

Coal Fires

0 500 km

Fig. 1. Distribution of coal fires in North China (adapted from van Genderen and Guan, 1997).

A. Prakash, Z. Vekerdy / International Journal of Coal Geology 59 (2004) 107119108


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2. Study area

The Rujigou coal field in the Ninxia Hui Autono-

mous Province in north-west China (Fig. 2) wasselected as the study area for investigating the prob-

lem of coal mine fires and for implementing the

prototype GIS. This coalfield, extending from lati-

tudes 39j01VN to 39j08VN and longitude 106j03VE

to 106j11VE, makes an ideal test site. It has a modest

size of about 54 km2, is well connected to the major

cities by roads and has established mining bureaus

and offices. It has three main mining areas, viz.,

Bajigou, Dafeng and Rujigou located in the north,

center and the south of the coalfield, respectively. It

contains an estimated reserve of 0.65 billion tons of

coal, ranking from low volatile bituminous coal to

high quality meta-anthracite, of which 4.5 million tons

per year is threatened by more than 20 individual coal

fires (Rosema et al., 1999). The area has both private

sector and government mines which operate on the

surface as well as underground.

The test area offers the complexity of a mountain-

ous terrain (elevation ranging from 1800 to 2500 m),which helps to model fires occurring in rugged high

altitude areas. The area is relatively dry with barren to

scanty vegetation, which facilitates remote detection

of coal fire areas.

3. The design of CoalMan

CoalMan is a PC-based open GIS system running

under Windows 95 or a higher operating system. It is

designed to store, retrieve, analyze and manage

tabular, vector and raster data and to support the

user in planning fire fighting strategies by providing

information pertinent for decision making (Vekerdy

and van Genderen, 1999a). It uses the Integrated

Fig. 2. Location of the study area.

A. Prakash, Z. Vekerdy / International Journal of Coal Geology 59 (2004) 107119 109


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Land and Watershed Information Management Sys-

tem (ILWIS) as the supporting GIS software pack-

age. For details on the structure and functioning of

ILWIS, the user is referred to the ILWIS website(ILWIS, 2002). From the CoalMan interface, written

in Visual Basic, ILWIS functions are invoked by

Dynamic Data Exchange (DDE) calls. Map and

image data are stored in ILWIS format, while a large

number of tabular data are stored in a separate MS

Access database. Fig. 3 shows the database structure

and information flow in CoalMan.

The functioning of CoalMan can best be catego-

rized in the following three main components.

The database and management tools Processing and analysis tools Featured display tools

Each of these components is discussed here in

further detail.

3.1. Database and management tools

The original input data, intermediate processing

results, final analyses results, and the archived data

all form a part of the database. Of special interest is the

metadata and the tools for data management and

integrity checks.

3.1.1. Input data: types and models

To understand the phenomenon of coal mine fire

and the ways to tackle it requires a multidisciplin-

ary approach. Various data sets, all with differing

spatial resolution, accuracy and data models need to

be analyzed in an integrated environment. The

DATA SOURCE

DataPre-processingand archiving

PROCEDURES& MODELS

Analysis

Report preparation DATA ARCHIVE

META-DATABASE

BACKUP DATABASE

Regular backup/Restore in case ofdatabase failure

DATABASE OFORIGINAL DATA

BACKGROUNDTABULAR

DATABASE

DATABASE OFANALYSIS RESULTS

Archiving

SYMBOLS:

data in ILWIS format

data in

MS Access

format

MAPS,GRAPHS &REPORTS

Fig. 3. Database structure and information flow in CoalMan. All the data searches, processing and analyses are carried out via the meta-

database. Three separate databases, which can be conveniently upgraded and archived, store the original maps and imagery, the tabular data and

the analyses results separately (after Vekerdy et al., 1999b).



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geology of deposit, geophysical and geochemical

analysis of coal type, mine working plans, topo-

graphic information, ground based measurements,

remote sensing information, field observation andavailable reports, all form important primary input

for analysis.

More specifically, the following form the data

input for the coal fire studies.

Raster data: These include mainly optical, thermal

and radar images acquired by satellite and airborne

platforms, various maps, digital elevation model

and field-based scanner images. Vector data: These include primarily the topograph-

ic maps with location of mining areas, residential

areas, communication network and other important

features and published geological maps. Point data in tabular format: These include Global

Positioning System (GPS) measurements, borehole

data, coal samples information, geological field

observations, field photographs, etc.

Others: Selected temperature profiles, information

from published reports and articles.

3.1.2. MetadataMetadata is primarily data about data. It con-

tains information on the location, content, quality and

other relevant characteristics of the data. The metadata

facilitates in the inventorying, browsing, selecting and

transfer of data as per the users needs.

3.1.3. Database management

Database management addresses the issues of data

entry in CoalMan via special interfaces. Fig. 4 shows

how data on coal seam elevations can be recorded in

CoalMan. The data input is automatically stored in

structured file folders, especially allocated for this

purpose. To browse the database, CoalMan provides

several forms based on the Structured Query Lan-

guage (SQL). Information on all input data such as

satellite images, aerial photographs, DEM, maps, etc.

can be browsed via the metadatabase. The selected

Fig. 4. Example of the data input interface in CoalMan.



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data can then be processed either by the special

processing tools (discussed in Section 3.2) or by

invoking the image processing functionalities of

ILWIS via the DDE calls.

3.1.4. Integrity check

The purpose of the integrity check is to ascertain

that all data stored in the database are duly registered

in the metadatabase. Integrity check is performed

automatically by comparing the files available in the

database with those registered in the metadatabase. In

case of discrepancy, CoalMan sends a warning to the

user and starts the metadata management functions.

This management function prompts the user, either to

delete the registration of missing data or contrarily to

register any new data it happens to locate in the

database.

3.2. Processing and analysis tools

As mentioned earlier, CoalMan uses ILWIS as the

basic image processing and GIS software package.

Standard image processing operations such as image

registration, pre-processing, enhancement, classifica-

tion can be performed in CoalMan via ILWIS. To use

the full image processing and GIS functionality of the

system, the user ought to be familiar with the ILWISpackage. For other important routine analysis of coal

fires, specialized tools have been developed which run

independent of ILWIS. This makes it easier for the

less trained users to operate CoalMan. The following

sections highlight the functioning of these specialized

tools.

3.2.1. Fire detection and mapping

Remote sensing detection of coal mine fires dates

back to early 1960s when several workers in the US

used it to detect fires in the Pennsylvanian coal fields(Slavecki, 1964; Greene et al., 1969). In the 1980s and

1990s, the thermal remote sensing data was digitally

processed and was extensively used to detect fires in

the Indian coalfields (Reddy et al., 1992; Mansor et

al., 1994; Prakash et al., 1995a). More recently, this

was used to detect coal fires in North China (van

Genderen and Guan, 1997; Zhang, 1998).

In principle, areas over underground coal fires tend

to be warmer than the surrounding areas. In the

thermal infrared (TIR) region (8 12 Am), these

warmer areas emit more energy than the background

regions and therefore show up as brighter spots on the

images acquired in this part of the spectrum. Night

time and pre-dawn images have proven to be mostuseful for fire detection as at this time the differential

heating effect of the solar illumination factor on the

ground is the minimum (Short, 2002). For studies in

China, the night time Landsat Thematic Mapper (TM)

band 6 images and the TIR images acquired from

special airborne campaign have been found to be most

useful.

In the simplest form, digital detection of fire areas

is based on a thresholding mechanism to delineate the

hot spots from background areas. The problem with

this technique is that the threshold is based on trial-

and-error. It relies heavily on the field knowledge and

constant interaction of the user. In CoalMan, the fire

detection is made semi-automated. The detection tool

uses a statistical parameter to define threshold on

small subsets of the first derivative of the TIR images

(Rosema et al., 1999). This gives a more realistic fire

picture and area estimate. However, there may still be

false signals showing up or an occasional need to

modify the threshold for which the users field knowl-

edge is a big guiding force. It is for this reason that

CoalMan takes a semi-automated approach for fire

detection and the user has the possibility to modifythresholds, ask the system to ignore a detected hot

spot in further computation or even change the size of

the subset window used for processing.

The TIR images used for coal fire detection often

have a crude spatial resolution (for Landsat TM band

6 the pixel size is 120*120 m) making it difficult to

visualize the location of the hot spot. In order to map

the coal fire areas, the detected hot spots are digitally

fused with high resolution images to generate the coal

fire maps (Prakash et al., 1995a). Fig. 5 shows the

detected hot spots and the final coal fire location mapafter image fusion.

3.2.2. Temporal monitoring of fires

Integrating the time component in spatial databases

increases the complexity of the data structure (Raza et

al., 1998; Abraham and Roddick, 1999). The geore-

ferenced spatial data has to be translated to a spatio-

temporal domain. CoalMan stores information on an

area as a snap shot in time. For a multitemporal

analysis, it is guided by the user needs. The user has



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the possibility of retrieving data of specified times

from the metadatabase, run processing tools to map

fire areas and let the system display the information

from different times in one plane as different vector

overlays. Different codes and attributes can beassigned to each vector layer for optimal display

and representation (Fig. 6). The comprehensive pic-

ture so generated proves very useful to monitor the

direction and speed of coal fire migration, to check for

new fires and also to monitor the success rate of fire

fighting operations (Prakash et al., 1999).

3.2.3. Fire depth estimation

Depth estimation of coal fires is a challenging task

and requires numerical modeling. The simplest mod-

els assume coal fire as a point source at a certain

depth, the overlying material as one of uniform

physical property and the heat conduction to the

surface based on the principles of linear heat flow in

a semi infinite medium (Cassells, 1997; Mukherjee etal., 1991). However, these models are far from reality.

In the field, the transfer of heat to the surface is

both by conduction and convection. Modeling the

convective component is far more complex as it

involves a detail analysis of crack patterns, their size,

interconnectivity, distance from the heat source, etc.

Prakash et al. (1995b) modeled for the convective

component of coal fires by attributing an equivalent

higher conductive component value to the heat trans-

fer. In CoalMan too, speculations of the convective

Fig. 5. Detection and mapping of coal fires. (a) Density sliced color coded night time TM band 6 image of the study area. Green, yellow and red

represent successively higher temperatures associated with underground coal fires. Blue represents the background temperature. Note that due to

the coarse spatial resolution of the thermal image (120 m), it is impossible to visually identify the location of these fires. (b) Fusion product of

the same (a) with a higher resolution IRS optical image (5-m spatial resolution). On the fused image, the fire areas show up well against the gray

background which clearly shows the location of streams, mining areas and other surface features of the study area.



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heat transfer from the fires have been made in the

depth estimation calculations, but a greater depth of

research is required to refine the estimates to account

for the complex convective modeling. The best thesystem can presently do is assign ranges of estimated

fire depths with intervals of 10 m. In some parts of the

world where dozens of interlayered coal seams occur,

such crude estimates would be meaningless for target-

ing fire fighting operations. However in the Rujigou

coalfield which primarily has only four major coal

seams, this estimate helps to ascertain which seam is

under fire.

3.2.4. Coal fire risk maps

The two important risk maps generated by Coal-Man are (i) risk of occurrence of coal fires (ii) risk of

damage to infrastructure and lives.

The main factors that govern the risk of occurrence

of new coal fires are the presence or absence of

mining activity, the access to air, and the propensity

of the coal to spontaneous combustion, which in turn

depends on the geochemical property of the coal,

particle size, porosity, moisture content, amount of

other impurities, etc (Rosema et al., 2000). In general,

inferior quality coal fragments exposed to sunlight, air

and a bit of moisture are much more likely to catch

fire than a thick seam of high quality coal. Once the

coal fragments catch fire, it is easy for this fire to

propagate and ignite a neighboring coal seam.The risk of damage to infrastructure and life depends

on the magnitude, direction and speed of migration of

an existing fire, the proximity of the infrastructure to

the fire areas, the presence or absence of natural or man

made barriers that may affect the further migration of

the fire and whether or not the area was already

subjected to underground mining in the past.

Though quantitative values can be assigned to

some of the input factors mentioned above, the risk

map generated by CoalMan is still qualitative in

nature, zoning the study area in low, medium andhigh risk areas (Fig. 7) (Rosema et al., 1999).

3.2.5. Decision tools for prioritizing and planning fire

fighting

Extinguishing coal fires requires careful and real-

istic planning. When several fires occur at different

locations and levels (depths) in a coalfield, putting

them all out at one time with the available, usually

limited, resources is next to impossible. This is not

even a target. The first step is to prioritize the fire

Fig. 6. Coal fire monitoring. Night time Landsat TM thermal band 6 images of 1989, 1995 and 1997 were used to monitor coal fires. Panel (a)

shows a condition where the fire spread out due to increase in opencast mining in the area. Panel (b) is an important observation for fire fighting,

as this defines a more recent fire which did not exist in 1989. Panel (c) defines a stable fire, possibly effecting a very thick coal layer, and thus

remaining relatively stationery. Panel (d) represents a dangerous fire which is migrating in a south-east direction and moving closer to the main

road and mining village of Rujigou. Note that the background of all the images is derived from high resolution optical image draped over by a

color coded digital elevation model of the study area.



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fighting activities. A smaller and more recent fire ispotentially more harmful in a long run than a persis-

tent fire in a thick coal seam. The former is also often

easier to put out compared to the latter and therefore

takes the highest priority in the priority ranking

assigned by CoalMan.

It is worthwhile to mention at this stage that the

decision support that CoalMan provides to the fire

fighters is based on a limited number of known input

parameters and set rules. In the absence of any other

field based information, this serves as a very useful

starting point to plan fire fighting activities. However,the local experience and knowledge of people work-

ing and living in and around the coal field should not

be ignored or underestimated.

3.3. Featured display functions

Coal fires are typically three dimensional (3D)

phenomena, which can be the best represented with

3D data models (e.g. voxels). The ILWIS software

package on which CoalMan is based does not have

complete 3-D display functionality. To handle and

display 3-D data in CoalMan would therefore require

either interfacing the GIS with another standard 3-D

software package or specially programming to allowthe system to perform such operations. With the

financial and time constraints of the present project,

both these options were not feasible.

In CoalMan, the optimal solution to deal with this

was found by using 2-D data structure to represent 3-D

phenomenon. The system allows the user to select any

section line across the 2-D study area (x and y planes)

and then project the depth information (z-plane) across

this section line onto a cross-section plane. As an

example, Fig. 8 shows how CoalMan displays the

general topography and the information on the depth

of coal seams and interbedded sandstone horizons in

the study area across a user defined cross- section line.

Other information (e.g. hot spot information from TIR

satellite images; location of mining areas, residential

areas, etc.) can also be overlaid and simultaneously

displayed on the same cross-section plane.

This approach proves to be extremely useful for the

fire fighting teams as it gives a simple pictorial

representation which helps them to target boreholes

for fire fighting operations.

3.4. Special features of CoalMan

As CoalMan was designed primarily for Chinese

users, it required to be adapted to meet the local needs.

The foremost importance was to have a Chinese

interface to the system besides the default English

interface. The bilingual support is implemented as a

look-up table in the database (Wang et al., 1999).

The other important aspect was to make sure that

the computerized system would replace the traditional

working system, with minimal impact to the organi-

zational set-up. CoalMan is now set up in the Provin-cial mining office in Ningxia and its primary user the

Fire Fighting and Prevention Team of Ningxia Hui

Autonomous Region. To ensure this smooth transition

and acceptance of a computerized system, CoalMan

was designed with constant interaction with the Chi-

nese counterparts throughout the course of the project.

The local staff of the fire fighting team received

special training for the use of CoalMan in and outside

China. A follow up visit was made after the final

implementation of the system, and a feedback and

Fig. 7. Map showing the risk of occurrence of new coal fires (after

Rosema et al., 2000).



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Fig. 8. (a) Shows a contour map of the study area overlaid on a thermal image as displayed in the viewing window of CoalMan. The user

defined profile line (AB) can be selected on any such image window. (b) Shows the section along the selected profile line that CoalMan

generates. Here the topographic surface and height information are derived from the contour lines; the coal layers/seams are plotted from

borehole data which had a separation distance of about 500 m; and the thermal profile and hotspot information is taken from calibrated satellite

imagery.



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communication system allowed for resolving of any

queries, doubts or questions from the end-users.

4. Lessons learned

If a tailor made GIS package is to realize its full

potential for the application domain, several issues

need to be addressed.

Regular interaction with the end-user community is

very important at all stages during the development

of an operational GIS system. Even after the

implementation of the system, a regular feedback

from the end-users, decision makers and skilled

experts should serve as an input to improve and

fine tune the system. Such a system must be both simple to use and of

real practical value to the user community. It should have a user friendly interface preferably

with a bi- or multilingual capability depending on

the needs of the region where it will be used. Decision makers are by and large overwhelmed by

the remote sensing data volumes, jargon of scientific

terms and the complexity of GIS operations

(Thumerer et al., 2000). They should be made to

realize that even though the complex processes runin the background, all the end-user needs to know

are the simple logics operating the complex

processes, the way to input new data and the way

to derive and use the end results. As the study of coal mine fires, like many other

application domains, has a multidisciplinary na-

ture, it requires current data from a large range of

sources. Various organizations involved in data

collection should agree on general data exchange

formats and should be encouraged to have an open

data sharing policy. Special tools such as the coal fire detection tool can

be made to run in a completely automated fashion.

However, such automations run the risk of giving

over optimistic fire area estimates. The automated

processing without any field knowledge also may

result in either a large number of false alarms or

conversely, omission of some important target

areas. In the context of coal fire studies, consid-

ering the complexity of the nature of the problem

and the limitations of the operational satellite data

availability, a semi-automated system, such as the

one implemented in CoalMan, takes preference

over either a completely manual or completely

automated system.

5. Scope and future directions

This paper has illustrated the development of Coal-

Man, a prototype GIS for coal fire management. The

special processing tools are based on the current

knowledge of surface and underground coal mine

fires. Further research is required to improve the

understanding of the coal fire phenomenon, its detec-

tion, monitoring, depth estimation and management.

Accordingly, these tools need to be refined and tuned

to keep pace with new research findings and user

requirements.

Use of higher spatial resolution satellite data in the

thermal region will certainly be a key factor in

improving the detection capability of underground

coal fires. For monitoring the fires in the Rujigou

coalfield, we relied on Landsat 5 TM band 6 data

which had a spatial resolution of 120 m. The im-

proved 60-m resolution of the Landsat 7 TM band 6

data improves the accuracy of mapping and monitor-

ing fires. The multispectral thermal bands on TerrasASTER instrument have the additional advantage that

emissivity values can be calculated using image data

(Schmugge et al., 2002). This helps in improved

temperature estimation and analysis of fires. Updated

versions of CoalMan will be required to link tools for

emissivity estimation and for processing newer satel-

lite data as they become available.

The design of CoalMan also has room for im-

provement, especially to accommodate a more effi-

cient means of spatio-temporal data handling and for

an enhanced three dimensional data handling anddisplay. The expansion of this prototype system to

cover the whole of North China and finally to set up a

global coal fire mapping and monitoring system is the

next major challenge to meet.

Acknowledgements

The authors wish to thank Professor J.L. van

Genderen, a protagonist of coal fire research at ITC.



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CoalMan was developed during the course of a project

funded jointly by the Netherlands Development

Agency (ORET/MILIEV) and the Chinese Govern-

ment (MOFTEC). The project partners, namely, theEngineering Bureau of Environmental Analysis and

Remote Sensing (EARS), the Netherlands Institute for

Applied Geosciences (NITG-TNO) and the Beijing

Remote Sensing Corporation (BRSC) are thanked for

their cooperation and support. The help rendered by the

Fire Fighting Department of the Coal Bureau of the

Ningxia Autonomous Region and the local residents of

the area is duly acknowledged. A. Prakash would like

to thank the Geophysical Institute at the University of

Alaska Fairbanks for providing the time and support in

publishing this article.

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