Applied Mathematics

Seismic Data in Exploration Geoscience

The recovery and production of hydrocarbon resources begins with an exploration of the earth’s subsurface, often through the use of seismic data collection and analysis. In a typical seismic data survey, a series of seismic sources (e.g. dynamite explosions) are initiated on the surface of the earth. These create vibrational waves that travel into the earth, bounce off geological structures in the subsurface, and reflect back to the surface where the vibrations are recorded as data on geophones. Computer analysis of the recorded data can produce highly accurate images of these geological structures which can indicate the presence of reservoirs that could contain hydrocarbon fluids. High quality images with an accurate analysis by a team of geoscientists can lead to the successful discovery of valuable oil and gas resources. Spectral analysis of the seismic data may reveal additional information beyond the geological image. For instance, selective attenuation of various seismic frequencies is a result of varying rock properties, such as density, elasticity, porosity, pore size, or fluid content. In principle this information is present in the raw data, and the challenge is to find effective algorithms to reveal these rock properties.

Spectral Analysis

Through the Fourier transform, frequency content of a seismic signal can be observed. The Short Time Fourier transform is an example of a time-frequency method that decomposes a signal into individual frequency bands that evolve over time. Such time-frequency methods have been successfully used to analyze complex signals with rich frequency content, including recordings of music, animal sounds, radio-telescope data, amongst others. These time-frequency methods show promise in extracting detailed information about seismic events, as shown in Figure 1, for instance.

Figure 1: Sample time-frequency analysis of a large seismic event (earthquake). From Hotovec, Prejean, Vidale, Gomberg, in J. of Volcanology and Geothermal Research, V. 259, 2013.

Problem Description

Are existing time-frequency analytical techniques effective in providing robust estimation of physical rock parameters that are important to a successful, economically viable identification of oil and gas resources? Can they accurate measure frequency-dependent energy attenuation, amplitude-versus-offset effects, or other physical phenomena which are a result of rock and fluid properties?

Using both synthetic and real seismic data, the goal is to evaluate the effectiveness of existing time-frequency methods such as Gabor and Stockwell transforms, discrete and continuous wavelet transforms, basis and matching pursuit, autoregressive methods, empirical mode decomposition, and others. Specifically, we would like to determine whether these methods can be utilized to extract rock parameters, and whether there are modifications that can make them particularly effective for seismic data.

The source data will include both land-based seismic surveys as well as subsurface microseismic event recordings, as examples of the breadth of data that is available for realistic analysis.

Figure 2: (a). Seismic data set from a sedimentary basin in Canada. The erosional surface and channels are highlighted by arrows. The same frequency attribute are extract from short time Fourier transform (b), continuous wavelet transform (c) and empirical mode decomposition (d).

In applications such as image processing, computer vision or image compression, often times accuracy and precision are less important than processing speed as the input data is noisy and the decision making process is robust against minor perturbations. For instance, the human visual system (HVS) makes pattern recognition decisions even though the data is blurry, noisy or incomplete and lossy image compression is based on the premise that we cannot distinguish minor differences in images. In this project we study the tradeoff between accuracy and system complexity as measured by processing speed and hardware complexity.

Knowledge of linear algebra, computer science, and familiarity with software tools such as Matlab or Python is desirable. Familiarity with image processing algorithms is not required.

Fig. 1: error diffusion halftoning using Shiau-Fan error diffusion

Fig. 2: error diffusion halftoning using a single lookup table

References:
1. Wu, C. W., "Locally connected processor arrays for matrix multiplication and linear transforms," Proceedings of 2011 IEEE International Symposium on Circuits and Systems (ISCAS), pp.2169,2172, 15-18 May 2011.

2. Wu, C. W., Stanich, M., Li, H., Qiao, Y., Ernst, L., "Fast Error Diffusion and Digital Halftoning Algorithms Using Look-up Tables," Proceedings of NIP22: International Conference on Digital Printing Technologies, Denver, Colorado, pp. 240-243, September 2006.

Cyber Optics designs and manufacturers some of the most capable 3D measurement systems in the world. These systems are based on Phase Profilometry, and sample a large amount of data, to construct a single 3D Height Image. The ratio of data collected to data output can be as high as 30 to 1. This process of sensing massive amounts of data and outputting a small fraction of the sensed data, is a common problem in sensor design. In the past decade a new branch of mathematics and sensor design has emerged called compressed sensing that specifically addresses this problem. In compressed sensing the sensor designer exploits these massive compressive ratios to “sense” a more limited data set.

My team will determine the feasibility of compressed sensing as applied to the Cyber Optics Phase Profilometry sensor. I (the presenter) have no experience with compressed sensing, but I have 4 years of experience working with phase profilometry, and 20+ years working as a mathematician in industry. We will explore the literature of compressed sensing, and present a “go – no-go” analysis of phase profilometry to my management. That is, we will attempt to address the question, “can compressed sensing be used to reduce the data collection requirements of our sensors by at least an order of magnitude”?

Seismic Data in Exploration Geoscience

The recovery and production of hydrocarbon resources begins with an exploration of the earth’s subsurface, often through the use of seismic data collection and analysis. In a typical seismic data survey, a series of seismic sources (e.g. dynamite explosions) are initiated on the surface of the earth. These create vibrational waves that travel into the earth, bounce off geological structures in the subsurface, and reflect back to the surface where the vibrations are recorded as data on geophones. Computer analysis of the recorded data can produce highly accurate images of these geological structures which can indicate the presence of reservoirs that could contain hydrocarbon fluids. High quality images with an accurate analysis by a team of geoscientists can lead to the successful discovery of valuable oil and gas resources. Spectral analysis of the seismic data may reveal additional information beyond the geological image. For instance, selective attenuation of various seismic frequencies is a result of varying rock properties, such as density, elasticity, porosity, pore size, or fluid content. In principle this information is present in the raw data, and the challenge is to find effective algorithms to reveal these rock properties.

Spectral Analysis

Through the Fourier transform, frequency content of a seismic signal can be observed. The Short Time Fourier transform is an example of a time-frequency method that decomposes a signal into individual frequency bands that evolve over time. Such time-frequency methods have been successfully used to analyze complex signals with rich frequency content, including recordings of music, animal sounds, radio-telescope data, amongst others. These time-frequency methods show promise in extracting detailed information about seismic events, as shown in Figure 1, for instance.

Figure 1: Sample time-frequency analysis of a large seismic event (earthquake). From Hotovec, Prejean, Vidale, Gomberg, in J. of Volcanology and Geothermal Research, V. 259, 2013.

Problem Description

Are existing time-frequency analytical techniques effective in providing robust estimation of physical rock parameters that are important to a successful, economically viable identification of oil and gas resources? Can they accurate measure frequency-dependent energy attenuation, amplitude-versus-offset effects, or other physical phenomena which are a result of rock and fluid properties?

Using both synthetic and real seismic data, the goal is to evaluate the effectiveness of existing time-frequency methods such as Gabor and Stockwell transforms, discrete and continuous wavelet transforms, basis and matching pursuit, autoregressive methods, empirical mode decomposition, and others. Specifically, we would like to determine whether these methods can be utilized to extract rock parameters, and whether there are modifications that can make them particularly effective for seismic data.

The source data will include both land-based seismic surveys as well as subsurface microseismic event recordings, as examples of the breadth of data that is available for realistic analysis.

Figure 2: (a). Seismic data set from a sedimentary basin in Canada. The erosional surface and channels are highlighted by arrows. The same frequency attribute are extract from short time Fourier transform (b), continuous wavelet transform (c) and empirical mode decomposition (d).

In applications such as image processing, computer vision or image compression, often times accuracy and precision are less important than processing speed as the input data is noisy and the decision making process is robust against minor perturbations. For instance, the human visual system (HVS) makes pattern recognition decisions even though the data is blurry, noisy or incomplete and lossy image compression is based on the premise that we cannot distinguish minor differences in images. In this project we study the tradeoff between accuracy and system complexity as measured by processing speed and hardware complexity.

Knowledge of linear algebra, computer science, and familiarity with software tools such as Matlab or Python is desirable. Familiarity with image processing algorithms is not required.

Fig. 1: error diffusion halftoning using Shiau-Fan error diffusion

Fig. 2: error diffusion halftoning using a single lookup table

References:
1. Wu, C. W., "Locally connected processor arrays for matrix multiplication and linear transforms," Proceedings of 2011 IEEE International Symposium on Circuits and Systems (ISCAS), pp.2169,2172, 15-18 May 2011.

2. Wu, C. W., Stanich, M., Li, H., Qiao, Y., Ernst, L., "Fast Error Diffusion and Digital Halftoning Algorithms Using Look-up Tables," Proceedings of NIP22: International Conference on Digital Printing Technologies, Denver, Colorado, pp. 240-243, September 2006.