e-Courses Catalog

Boost your credentials with a certificate

Enhance your CV with a certification in machine learning.  For select courses, upon successful completion of assessments, you will receive a certificate, which can be listed among your LinkedIn credentials.  Optional short answer/essay questions provide the opportunity to obtain a certificate with distinction, showing an additional mastery of the course material. Simply click “Enroll with Certification”.

Machine Learning Essentials

The course is ideal for geoscientists, engineers, and data analysts at all experience levels. Concepts are supported with ample illustrations and case studies, complemented by mathematical rigor benefiting the subject. Aspects of supervised learning, unsupervised learning, classification, and reclassification are introduced to illustrate how these methods apply to seismic data. For this version of the class, assessments are given at intervals throughout the course to gauge comprehension. Upon completion with a passing total score, a certificate is issue, certified by Geophysical Insights. Read more

Instructor: Dr. Tom Smith, President and CEO, Geophysical Insights
Certification Available: Yes
Total classroom time: 12 hours
Cost: $75 (with certification), $50 (without certification)

Single-trace Attributes E-course
Single Trace Attributes

The 49-minute short-course focuses on Single trace seismic attributes, which include two general varieties:  instantaneous and banded, sometimes called Wavelet attributes.  The material starts with an organization of seven principal groups or types of attributes and proceeds to set out five primary groups of Single Trace attributes, including Instantaneous, the ‘Tool Kit’ attributes, Instantaneous Layer attributes, Banded attributes, and additional Banded attributes on phase breaks.

Instructor: Dr. Tom Smith, President and CEO, Geophysical Insights
Certification Available: Yes
Total classroom time: 1 hour
Cost: $30 (with certification), $20 (without certification)

Seismic Attributes for the Environment of Deposition
Seismic Attributes for the Environment of Deposition

The evaluation of seismic attributes is a powerful tool in the interpretation of different geologic environments of deposition. Seismic attributes, specifically geometric and spectral decomposition attributes, provide a framework for interpreting geologic features that define depositional environments. This video course identifies the appropriate seismic attributes for various geologic settings and describes how these attributes are applied. Lecture and demonstrations cover the use of attributes in interpretation workflows and manipulate attribute parameters to highlight geologic features. The last video segment of the course describes how sets of attributes are analyzed and classified using multi-attribute, Machine Learning processes to extract more information from the seismic response. Read more

Instructors: Dr. Kurt Marfurt, The University of Oklahoma | Dr. ChingWen Chen, Geophysical Insights | Rocky Roden, Geophysical Insights
Certification Available: No
Total classroom time: 5 hours
Cost: $40

Energy Absorption and Traveling Waves
Energy Absorption and Traveling Waves

Waves of elastic energy travel through the Earth with the same physical principles as other waves travel through different mediums. This video focuses on the physics of traveling waves and why energy absorption is important to an understanding of seismic data. Starting with a liner second-order vibrating system as a mathematical model, the 50-minute short course presents the classes of waves and their associated measurements. The basics of energy loss in traveling waves are described, as well as the relationship between vibration and energy loss. A simplified model of the seismic geophone is used to described attenuation and damping ratio. Read more

Instructor: Dr. Tom Smith, President and CEO, Geophysical Insights
Certification Available: No
Total classroom time: 1 hour
Cost: $20

Introduction to Machine Learning for Interpreters
Introduction to Machine Learning for Interpreters

Every day our lives are intertwined with applications, services, orders, products, research, and objects that are incorporated, produced, or effected in some way by Artificial Intelligence and Machine Learning. Buzz words like Deep Learning, Big Data, Supervised and Unsupervised Learning are employed routinely to describe Machine Learning, but how do these applications relate to geoscience interpretation and finding oil and gas? More importantly, do these Machine Learning methods produce better results than conventional interpretation approaches? This course will initially wade through the vernacular of Machine Learning and Data Science as it relates to the geoscientist. An overview of how these methods are being employed, as well as, interpretation case studies of different machine learning applications will be presented. An overview of how high-performance computing and the utilization of Cloud Services related to Machine Learning will be described. Machine Learning is a disruptive technology that holds great promise and this course will be presented from an interpreter’s perspective, not a data scientist. This course will provide an understanding of how Machine Learning for interpretation is being utilized today and provide insights on future directions and trends.

Instructor: Rocky Roden, Sr. Consulting Geophysicist, Geophysical Insights
Certification Available: No
Total classroom time: 1 hour
Cost: $20

Leveraging Deep Learning in Extracting Features of Interest from Seismic Data Thumbnail
Leveraging Deep Learning in Extracting Features of Interest from Seismic Data (NEW)

Mapping and extracting features of interest is one of the most important objectives in seismic data interpretation. Due to the complexity of seismic data, geologic features identified by interpreters on seismic data using visualization techniques are often challenging to extract. With the rapid development in GPU computing power and the success obtained in computer vision, deep learning techniques, represented by convolutional neural networks (CNN), start to entice seismic interpreters in various applications. The main advantages of CNN over other supervised machine learning methods are its spatial awareness and automatic attribute extraction. The high flexibility in CNN architecture enables researchers to design different CNN models to identify different features of interest.

Instructor: Dr. Tao Zhao
Certification Available: No
Total classroom time: 45 minutes
Cost: Free

Machine Learning for Incomplete Geoscientists
Machine Learning for Incomplete Geoscientists (COMING SOON)

This course covers big-picture machine learning buzz words with both humor and unassailable frankness. The goal of the course is for every geoscientist to gain confidence in these important concepts and how they add to our well-established practices, particularly seismic interpretation. Presentation topics include a machine learning historical perspective, what makes it different, a fish factory, Shazam, comparison of supervised and unsupervised machine learning methods with examples, tuning thickness, deep learning, hard/soft attribute spaces, seismic wavelets and multi-attribute samples, and several interpretation examples. On conclusion, you may not know how to run machine learning algorithms, but you should be able to appreciate their value and some of their limitations.

Instructor: Dr. Tom Smith, President and CEO, Geophysical Insights
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