Kamal al-Yahya, senior V.P., GeoSoftware & Smart Data Solutions, said: “Throughout their long careers, Brian and Dan have shown unstinting dedication to innovation and the advancement of our geophysical industry. He is currently investigating the use of Deep Neural Networks to enhance and extend the inversion process. Dan is the originator of several innovations in the field of seismic processing, including the Generalized Linear Inverse approach to refraction statics analysis and the Parabolic Radon Transform noise and multiple attenuation algorithm. Brian Russell is an internationally recognized expert in seismic inversion, amplitude variation with offset (AVO), and seismic attribute analysis, and provides training courses on these subjects throughout the world.
It now delivers deep learning in the form of Deep Feed-forward Neural Networks for more detailed prediction of reservoir properties.ĭan and Brian founded Hampson-Russell Software in 1987 and, as vice presidents within CGG GeoSoftware, continue to guide its strategy and the development of reservoir characterization software. GeoSoftware’s HampsonRussell Emerge technology has implemented machine learning methods for over 20 years to address complex geological challenges. Brian Russell will speak at the event about the ‘History of Inversion’ while Jon Downton, senior research advisor for CGG GeoSoftware, will give a technical talk on the latest innovations in ‘Machine Learning Inversion’. The theme of this year’s GSH-SEG Symposium, held April 16-17 in Houston, USA, is ‘The Resurgence of Seismic Inversion’. Initially, we will therefore look at the basic convolutional model of the seismic trace in the time and frequency domains, considering the thre e components of this model: reflectivity, seismic wavelet, and noise.PARIS - Dan Hampson and Brian Russell from GeoSoftware, part of CGG’s Geoscience Division, are being honored for their groundbreaking achievements in geophysics by the Geophysical Society of Houston and the Society of Exploration Geophysicists at their jointly sponsored 2019 GSH-SEG Spring Symposium and Exhibition being held in Houston this week. To understand seismic inversion, we must first understand the physical processes involved in the creation of seismic data. The relationship between forward and inverse modelling is shown in Figure 1.1. As such, it can be considered as the opposite of the forward modelling technique, which involves creating a synthetic seismic section based on a model of the earth (or, in the simplest case, using a sonic log as a one-dimensional model). Thus, in this course we shall primarily restrict our discussion to those inversion methods which attempt to recover a broadband pseudo-acoustic impedance log from a band-limited seismic trace.Īnother way to look at inversion is to consider it as the technique for creating a model of the earth using the seismic data as input.
The above definition is so broad that it encompasses virtually all the work that is done in seismic analysis and interpretation. Geophysical inversion involves mapping the physical structure and properties of the subsurface of the earth using measurements made on the surface of the earth. The most general definition is as fol1ows:
It would therefore seem appropriate to begin by defining what is meant by seismic inversion.
This course is intended as an overview of the current techniques used in the inversion of seismic data.