Mining presents unique challenges to data scientists. They must walk a thin line between preserving scientific objectivity and extracting the greatest benefit from data. Data scientists and geoscientists must collaborate to correctly transform the data and achieve meaningful results.

1. A Miner’s Drift
A Journal of Occasional Explorations
Joseph Starwood – Digital Advisor, Geologist, & Geophysicist
Volume 01
Issue 01
2018-MAR-27
Deep Learning &
Artificial Intelligence
in Mining
It starts with the data

2. Deep Learning & Artificial
Intelligence in Mining
It starts with the data
Mining presents unique challenges to data scientists. They must walk a
thin line between preserving scientific objectivity and extracting the
greatest benefit from data. However, in specific situations, data
scientists must understand the source data including its structure and
the transformations performed upon it. Without that understanding,
artificial intelligence efforts yield poor or even meaningless results.

3. 2018-MAR-27 A Miner’s Drift Volume 01 Issue 01 Copyright 2018 All rights reserved
Mining professionals rely on a
staggering volume and variety of
data. From exploration through
extraction and beneficiation to final
shipment, miners utilize devices,
systems, and applications that
produce a wide range of data. This
data may include geologic
observations, geochemical results,
geophysical measurements,
engineering criteria, assay results,
mine block models, mine planning
details, productions yields, financials,
and more.
This assortment of data presents the
usual problems to data scientists.
These include vendor proprietary
data schemas and exchange formats,
custom developed data schemas and
exchange formats, structural and
semantic differences across schemas
and formats, missing data values,
and so on. Through experience, data
scientists are well-prepared to deal
with these problems.
GEOSCIENTIFIC DATA
Geoscientific data, however, presents
a unique challenge. Geologists,
geochemists, and geophysicists
observe and measure many different
rock types, alteration types,
minerals, and elements.
To accommodate this, they utilize
applications and databases that have
a very high degree of data field reuse.
In other words, a given field may
mean one thing in the first record
and another thing in the second
record.
To the geoscientist, this is very
familiar and convenient. But, for the
data scientist, it is particularly vexing.
THE SOURCE DATA PROBLEM
Let’s look at this more closely. Say
you are creating a new artificial
intelligence (AI) solution to help a
mining company find better
exploratory drilling targets.
Suppose that the core log database
records the lithology, the three most
prevalent alteration types with their
intensities, and the five most
prevalent elements and their values
among many other data items.
The data record fields would look
something like this:
Core ID, . . . Lithology, AltType1,
AltType1-Int, AltType2, AltType2-Int,
AltType3, AltType3-Int, Elem1,
Elem1Value, Elem2, Elem2Value,
Elem3, Elem3Value, Elem4,
Elem4Value, Elem5, Elem5Value, . . .
In the first record, the lithology is
Greywacke. The most prevalent
alteration isTourmaline, which has a
low intensity. Gold is the most
prevalent element at 20 ppm. The
remaining four elements in order are
Silicon, Sulphur,Arsenic, and Iron.
In the next record, we have Schist.
This time, the most prevalent
alteration is Silica, which has a high
intensity.The second most prevalent
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4. 2018-MAR-27 A Miner’s Drift Volume 01 Issue 01 Copyright 2018 All rights reserved
alteration is Chloritization which has
a moderate intensity. Silicon is the
most prevalent at 35%, Chlorine,
Sulphur, Iron, and Copper follow.
That AltType1 is ‘Tourmaline’ and
AltType1Int is ‘low’ in the first record
while AltType1 is ‘Silica’ and
AltType1Int is ‘high’ in the second
record presents a real problem.
Comparing the raw source data in
such records is like comparing apples
to oranges. This data is not ready for
use in an artificial intelligence or
advanced analytics solution.
FIXINGTHE PROBLEM
Ideally, records should have fields for
each alteration type and each
element in which the respective
values are stored. This logic applies
to any reused data field in the record.
To fix this source data problem, each
data value must be read and then
written to a new data record such
that the value goes into the field
intended for that specific data. This
process is referred to as ‘flattening’
the data. The new data record is
referred to as ‘flattened’ data.
The data record fields would look
something like this:
Core ID, . . . Lithology, ChorAlt, . . .
EpidAlt, OxidAlt, PyriAlt, SerpAlt,
SiliAlt, . . .TourAlt, . . .Ac, Ag, Al, Am, Ar,
As, At, Au, . . .Yb, Zn, Zr . . .
There are 94 naturally occurring
elements. A given mining
environment may exhibit a dozen
alteration types. As you can see,
flattening the data results in each
data record having perhaps 200+
fields.
Deciphering the source data records
is not easy. Mining and geosciences
have domain-specific languages and
notations. Partnering with a
geologist or geotechnician helps the
data scientist accelerate the data
flattening and ensure correctness.
BETTER ARTIFICIAL INTELLIGENCE
Results from several mining projects
around the world demonstrate the
differences between raw and
flattened data on artificial
intelligence outcomes.
The typical artificial intelligence
solution utilized MicrosoftAzure
including Machine Learning, Blob
Storage, SQL Database, and Power
BI.
The precision and accuracy for
predicting high-grade ore were in the
mid-70 percent range for the raw
(non-flattened) data. Here the non-
reused fields appear to have played a
role. For the flattened data, the
precision and accuracy for predicting
high-grade ore were in the high-80
percent range.
PAGE 4

5. CONCLUSION
Artificial intelligence can play a powerful role in mining. The results
depend upon the completeness and correctness of the input data. In
many cases, the raw data must first be transformed to make in amenable
to deep learning and artificial intelligence. Data scientists and
geoscientists must collaborate to correctly transform the data and
achieve meaningful results.

6. During his early career, Joseph worked as an exploration geologist and
geophysicist. He focused on gold, silver, copper, and molybdenum deposits in
the western United States. His work included Kuroko style gold systems. He
also worked in various capacities for coal mining, oil & gas, and heavy
construction companies.
Joseph has presented at:
▪ InfraGardGreat Lakes Regional Conference – 2014
▪ Society for Mining, Metallurgy, and Exploration Annual Meeting – 2014
▪ Great Lakes Software ExcellenceConference (GLSEC) – 2011 & 2013
▪ Calvin College Colloquium Series – 2010
▪ Integrate: Uniting theWorld of IT Symposium – 2010
▪ SEI ArchitectureTechnology User Network (SATURN) – 2019
▪ Financial Services Solutions Symposium (FSSS) – 2018 & 2019
He also contributed to:
• Software Engineering Institute (SEI) – Hard Problems in SOA Workshop – 2018
Contact theAuthor:
• LinkedIn: www.linkedin.com/in/josephstarwood
• e-Mail: JosephStarwood@JosephStarwood.com
Known within Microsoft Corporation as 'The Mining
Guy', Joseph Starwood is a Digital Advisor who helps
mining companies, equipment suppliers, and service
providers extract value from their information
technology (IT) and operational technology (OT)
investments and assets. He co-authored the Mining
Book of Dreams as well as the Microsoft Upstream
Reference Architecture for Mining (MURA-Mining).
PLEASE NOTE
The author is an employee of Microsoft Corporation. The views, thoughts, and opinions expressed in this text belong wholly to the author, and do not necessarily reflect those of anyone else (the author’semployer, or any other group or individual).
All product names, logos, and brands are property of their respective owners. All company, product and service names used in this text are for identification purposes only. Use of these names, logos, and brands does not imply endorsement.
The information in this text is provided without representations or warranties, express or implied.

7. ThankYou!