Deep learning for Geophysical Applications
ChatGPT: The four most important learning points from the course “Deep Learning for Geophysical Applications”:
1. Understanding Deep Learning Workflows and Algorithms: Through exercises and case studies, you will learn how to apply deep learning to predict geological features such as lithology, pore fluids, and facies. You'll become familiar with the workflow of preprocessing data, training deep learning models, and using these models to make accurate predictions. Technologies like TensorFlow and Keras, and tools like Google Colab and Scikit-Learn, will be instrumental in this process.
2. Hands-On Application with Real Geophysical Data: The practical exercises are designed to provide a hands-on experience with actual geophysical data sets. You will learn how to balance input classes, standardize and normalize data, and apply various deep learning techniques—ensuring learning through practical engagement and not just theoretical understanding.
3. Recognition of Deep Learning Impact in Geosciences: By the end of the course, you will have a clear understanding of how deep learning, within the broader context of machine learning and artificial intelligence, is transforming the field of geosciences. The examples and case studies discussed in the course will showcase the potential and actual benefits of applying AI in different geophysical applications.
4. Skill Development for Future Applications: Besides current applications, the course is designed to equip you with skills that are transferable to future challenges and technologies. Given that AI and deep learning are rapidly evolving fields, the foundational knowledge and problem-solving abilities you gain will be crucial for adapting to new tools and techniques that may emerge in geosciences.
By engaging with these learning points, the intended audience, which includes geologists, geophysicists, petrophysicists, and reservoir engineers, will enhance their ability to integrate AI into their work, optimize data analysis, and contribute to advancements in their respective
Various kinds of geophysical data are available, sometimes summarised as Multi-Physics data. Usually, they are separated into Seismic and Non-Seismic. Seismic is, without any doubt, the dominant method used in the energy industry. But Non-Seismic data (gravity, magnetics, electrical, electromagnetics, etc) is the main source of information in shallow subsurface applications (engineering, mapping pollution, archaeology). It is also used in the early reconnaissance of new basins and plays and in mapping prospects below salt/basalt (Magneto-Telluric). However, seismic has its limitations and therefore also non-seismic methods are used successfully as complementary tools in subsurface evaluation. In combination with seismic data, they can significantly reduce the uncertainty of subsurface models as they measure different physical properties of the subsurface.
In the first part, various aspects of gravity will be discussed, such as the Earth gravity field, determining anomalies in the global field, establishing the depth of density anomalies, be it spherical or anticlinal and the resolution, which is limited because gravity is a “potential field”. Most promising is the development of gravity gradiometer, whereby gradients in the gravity field can be directly measured with great accuracy. These measurements are less sensitive to airplane and ship movement.
In the second part, the Earth’s magnetic field, also a “potential field” will be studied. Being mainly due to the internal dipole source, the inherently more difficult interpretation is simplified by applying a transformation to a monopole field (Reduction to the Pole or Equator). As different causative sources can produce the same surface measurements, non-uniqueness is allways present. However, promising developments to mitigate these issues will be shown.
Finally, we will discuss and apply the “latest and greatest” that is Machine Learning, a part of Artificial Intelligence, which is step-changing the geoscience world. We will use a standard package called Weka and familiarise ourselves with the “ultimate” open-source software Keras and TensorFlow. Exercises will be done using Weka and Google Colab. After the course, you should be able to try out Machine Learning on your own data. In addition we will experiment with ChatGPT.
Geophysics provides technology with which we can "look" into the subsurface. It is a key enabler of many activities in the search for hydrocarbons, minerals, fresh water, and geothermal energy. Of the many existing geophysical methods, three are important for monitoring CO2. These are Seismic and Electromagnetic and Gravity methods. CO2 injection can be done for sequestration as well as enhanced oil recovery
In the first method, high resolution seismic up to 250 Hz can be acquired with short offsets. For deeper layers, long offset seismic is collected. Long offsets are needed for Refraction Static corrections and in case Full Waveform Inversion is applied for obtaining the diving waves. For CO2 sequestration, the presence of fractures and their orientation, being natural or induced is a significant hazard that can be determined from seismic.
The second important geophysical method is electrical and electro-magnetic methods. Electrical or Direct Current surveys use grounded electrodes for source and receivers. They measure the potential difference using increasing receiver electrode spacing. Changes in measured potentials contain information on the resistivities of the subsurface, which can be related to changing pore fluids, like CO2 replacing brine. Electro-Magnetic can use either grounded or inductive sources (aerial surveys), but also natural sources as used in Magneto-Telluric surveys. These Electro-Magnetic sources can be a harmonic source (using a single frequency) and the measurement of the magnitude and phase delay, or real and imaginary responses are used. The other Electro-Magnetic source option is a step-off function. The subsurface information is then contained in the amplitude decay after shut-off.
The third approach is using Gravity measurements. These could be changes in the vertical gravity component Gz or the Full-Tensor-Gravity (FTG), which measures gradients in the gravity field. With the increasing accuracy of modern instruments, changes in the CO2-brine interface have been measured in oil (Prudhoe Bay, Schrader-Bluff and Troll field) and gas-storage (Izaute) fields
- Teacher: J.C. Mondt
We all have seen displays of seismic data in the form of sections or cubes of data. But what do they show and how are they acquired? In this course you will learn to understand that seismic data represents the movement of the surface, resulting from waves generated by a source, dynamite or vibrator, which are reflected by changes in the subsurface rocks. Hence, what we record is related to the properties of the rocks, not only rocks, but also its pore fluids. All information on the subsurface is contained in these records, but almost impossible to extract and understand. Therefor the records need to be processed to make it possible to interpret structure and content of the pore space. In this course, the basic principles of acquisition and processing will be discussed. But also, insights in advanced methods will be provided. These methods allow a much more accurate interpretation of seismic data. The aim is not to fully understand these methods, but to understand its importance in certain cases, to enable interpreters, reservoir engineers to formulate requests for these methods.