Publications | Erlend M. Viggen

You can find a searchable list of my publications below. My Google Scholar profile contains an up-to-date overview of my citations. I also have a ResearchGate profile with most of my full-texts.

I am a strong proponent of Open Access, especially after having spent more than four years as a researcher at an institution with a very limited number of journal subscriptions. For each entry below where I am legally allowed to share a full-text, you can find it as the first link of the entry.

acoustics auralisation lattice boltzmann machine learning speech enhancement well logging

2021

Viggen, Erlend Magnus; Arnestad, Håvard Kjellmo

Understanding sound radiation from surface vibrations moving at subsonic speeds Conference

Proceedings of the 44th Scandinavian Symposium on Physical Acoustics, Norwegian Physical Society, Online, 2021, ISBN: 978-82-8123-021-71, (Extended abstract).

BibTeX | Tags: acoustics | Links:

Arnestad, Håvard Kjellmo; Viggen, Erlend Magnus

A fast semi-analytical method for propagating leaky Lamb wavefields Conference

Proceedings of the 44th Scandinavian Symposium on Physical Acoustics, Norwegian Physical Society, Online, 2021, ISBN: 978-82-8123-021-71.

Abstract | BibTeX | Tags: acoustics | Links:

Viggen, Erlend Magnus; Løvstakken, Lasse; Merciu, Ioan Alexandru; Måsøy, Svein-Erik

Better automatic interpretation of cement evaluation logs through feature engineering Conference

SPE/IADC International Drilling Conference and Exhibition, 2021.

Abstract | BibTeX | Tags: acoustics, machine learning, well logging | Links:

@conference{viggen_better_2021a,
title = {Better automatic interpretation of cement evaluation logs through feature engineering},
author = {Erlend Magnus Viggen and Lasse Løvstakken and Ioan Alexandru Merciu and Svein-Erik Måsøy},
doi = {10.2118/204057-MS},
year = {2021},
date = {2021-03-09},
booktitle = {SPE/IADC International Drilling Conference and Exhibition},
pages = {28},
abstract = {We build systems to automatically interpret cement evaluation logs using supervised machine learning (ML). Such systems can provide instant rough interpretations that may then be used as a basis for human interpretation. Here, we compare the performance of two approaches: A previously published approach based on deep convolutional neural networks (CNNs) that autonomously learn to extract features from well log data, and a feature-engineering approach where we use our own domain knowledge to extract features.

We base this work on a dataset of around 60 km of well log data. Specialist interpreters have classified these logs according to the bond quality (6 ordinal classes) and hydraulic isolation (2 classes) of solids outside the casing. We train the ML systems to reproduce these reference interpretations in segments of 1 m length. The CNNs directly receive log data as a collection of 2D images and 1D curves. In the feature-engineering approach, we combine the extracted features with various classifiers.

For bond quality, the CNNs’ interpretation exactly matches the reference 51.6% of the time. 88.5% of the time, it does not miss by more than one class. For hydraulic isolation, the CNNs match the reference 86.7% of the time. The best-performing feature-based classifier, which is an ensemble of individual classifiers, provides better results of 57.4%, 89.5%, and 88.9%, respectively.

Our results indicate two main reasons why feature-based classifiers may perform particularly well on this task. First, there is some subjectivity inherent in the well log interpretations that are used to train and test ML systems. Second, well logs comprise many different and complex pieces of data. For these reasons, this dataset may be particularly liable to overfitting. This may favour approaches based on feature engineering, where we apply our domain knowledge to extract a few pieces of essential information from the data instead of leaving the job of understanding the data to an ML system that may misinterpret spurious patterns as generalisable. It may also favour simpler classifiers with less overfitting capacity.

This article shows how petroleum researchers and engineers can implement automatic interpretation systems for cement evaluation logs using ML methods that are relatively easy to apply and deploy, with better results than an approach based on autonomous feature extraction. This approach could also be adapted for automatic interpretation of other types of well log data.},
keywords = {acoustics, machine learning, well logging},
pubstate = {published},
tppubtype = {conference}
}

We build systems to automatically interpret cement evaluation logs using supervised machine learning (ML). Such systems can provide instant rough interpretations that may then be used as a basis for human interpretation. Here, we compare the performance of two approaches: A previously published approach based on deep convolutional neural networks (CNNs) that autonomously learn to extract features from well log data, and a feature-engineering approach where we use our own domain knowledge to extract features.

We base this work on a dataset of around 60 km of well log data. Specialist interpreters have classified these logs according to the bond quality (6 ordinal classes) and hydraulic isolation (2 classes) of solids outside the casing. We train the ML systems to reproduce these reference interpretations in segments of 1 m length. The CNNs directly receive log data as a collection of 2D images and 1D curves. In the feature-engineering approach, we combine the extracted features with various classifiers.

For bond quality, the CNNs’ interpretation exactly matches the reference 51.6% of the time. 88.5% of the time, it does not miss by more than one class. For hydraulic isolation, the CNNs match the reference 86.7% of the time. The best-performing feature-based classifier, which is an ensemble of individual classifiers, provides better results of 57.4%, 89.5%, and 88.9%, respectively.

Our results indicate two main reasons why feature-based classifiers may perform particularly well on this task. First, there is some subjectivity inherent in the well log interpretations that are used to train and test ML systems. Second, well logs comprise many different and complex pieces of data. For these reasons, this dataset may be particularly liable to overfitting. This may favour approaches based on feature engineering, where we apply our domain knowledge to extract a few pieces of essential information from the data instead of leaving the job of understanding the data to an ML system that may misinterpret spurious patterns as generalisable. It may also favour simpler classifiers with less overfitting capacity.

This article shows how petroleum researchers and engineers can implement automatic interpretation systems for cement evaluation logs using ML methods that are relatively easy to apply and deploy, with better results than an approach based on autonomous feature extraction. This approach could also be adapted for automatic interpretation of other types of well log data.

Viggen, Erlend Magnus; Løvstakken, Lasse; Måsøy, Svein-Erik; Merciu, Ioan Alexandru

Better automatic interpretation of cement evaluation logs through feature engineering Journal Article Forthcoming

SPE Journal, Forthcoming, ISSN: 1930-0220.

Abstract | BibTeX | Tags: acoustics, machine learning, well logging | Links:

@article{viggen_better_2021b,
title = {Better automatic interpretation of cement evaluation logs through feature engineering},
author = {Erlend Magnus Viggen and Lasse Løvstakken and Svein-Erik Måsøy and Ioan Alexandru Merciu},
doi = {10.2118/204057-PA},
issn = {1930-0220},
year = {2021},
date = {2021-01-23},
journal = {SPE Journal},
abstract = {We investigate systems to automatically interpret cement evaluation logs using supervised machine learning (ML). Such systems can provide instant rough interpretations that may then be used as a basis for human interpretation. Here, we compare the performance of two approaches, one previously published and one new. The previous approach is based on deep convolutional neural networks (CNNs) that autonomously learn to extract features from well log data, whereas the new approach uses feature engineering, in which we use our own domain knowledge to extract features.

We base this work on a data set of approximately 60 km of well log data. Specialist interpreters have classified these logs according to the bond quality (BQ; six ordinal classes) and hydraulic isolation (HI; two classes) of solids outside the casing. We train the ML systems to reproduce these reference interpretations in segments of 1 m in length. The CNNs directly receive log data as a collection of 2D images and 1D curves. In the feature-engineering approach, we combine the extracted features with various classifiers.

For BQ, the CNNs' interpretation exactly matches the reference 51.6% of the time. It does not miss by more than one class 88.5% of the time. For HI, the CNNs match the reference 86.7% of the time. The best-performingfeature-based classifier, which is an ensemble of individual classifiers, provides better results of 57.4, 89.5, and 88.9%, respectively.

Our results indicate two main reasons why feature-based classifiers may perform particularly well on this task. First, there is some subjectivity inherent in the well log interpretations that are used to train and test ML systems. Second, well logs comprise many different and complex pieces of data. For these reasons, this data set may be particularly liable to overfitting. This may favor approaches based on feature engineering, where we apply our domain knowledge to extract a few pieces of essential information from the data instead of leaving the job of understanding the data to an ML system that may misinterpret spurious patterns as generalizable. It may also favor simpler classifiers with less overfitting capacity.

This paper shows how petroleum researchers and engineers can implement automatic interpretation systems for cement evaluation logs using ML methods that are easier to apply and deploy while also performing better than an approach based on autonomous feature extraction. This approach could also be adapted for automatic interpretation of other types of well log data.},
keywords = {acoustics, machine learning, well logging},
pubstate = {forthcoming},
tppubtype = {article}
}

We investigate systems to automatically interpret cement evaluation logs using supervised machine learning (ML). Such systems can provide instant rough interpretations that may then be used as a basis for human interpretation. Here, we compare the performance of two approaches, one previously published and one new. The previous approach is based on deep convolutional neural networks (CNNs) that autonomously learn to extract features from well log data, whereas the new approach uses feature engineering, in which we use our own domain knowledge to extract features.

We base this work on a data set of approximately 60 km of well log data. Specialist interpreters have classified these logs according to the bond quality (BQ; six ordinal classes) and hydraulic isolation (HI; two classes) of solids outside the casing. We train the ML systems to reproduce these reference interpretations in segments of 1 m in length. The CNNs directly receive log data as a collection of 2D images and 1D curves. In the feature-engineering approach, we combine the extracted features with various classifiers.

For BQ, the CNNs' interpretation exactly matches the reference 51.6% of the time. It does not miss by more than one class 88.5% of the time. For HI, the CNNs match the reference 86.7% of the time. The best-performingfeature-based classifier, which is an ensemble of individual classifiers, provides better results of 57.4, 89.5, and 88.9%, respectively.

Our results indicate two main reasons why feature-based classifiers may perform particularly well on this task. First, there is some subjectivity inherent in the well log interpretations that are used to train and test ML systems. Second, well logs comprise many different and complex pieces of data. For these reasons, this data set may be particularly liable to overfitting. This may favor approaches based on feature engineering, where we apply our domain knowledge to extract a few pieces of essential information from the data instead of leaving the job of understanding the data to an ML system that may misinterpret spurious patterns as generalizable. It may also favor simpler classifiers with less overfitting capacity.

This paper shows how petroleum researchers and engineers can implement automatic interpretation systems for cement evaluation logs using ML methods that are easier to apply and deploy while also performing better than an approach based on autonomous feature extraction. This approach could also be adapted for automatic interpretation of other types of well log data.

2020

Viggen, Erlend Magnus; Merciu, Ioan Alexandru; Løvstakken, Lasse; Måsøy, Svein-Erik

Automatic interpretation of cement evaluation logs from cased boreholes using supervised deep neural networks Journal Article

Journal of Petroleum Science and Engineering, 195 , pp. 17, 2020, ISSN: 0920-4105.

Abstract | BibTeX | Tags: acoustics, machine learning, well logging | Links:

@article{viggen_automatic_2020,
title = {Automatic interpretation of cement evaluation logs from cased boreholes using supervised deep neural networks},
author = {Erlend Magnus Viggen and Ioan Alexandru Merciu and Lasse Løvstakken and Svein-Erik Måsøy},
url = {https://www.sciencedirect.com/science/article/pii/S0920410520306100},
doi = {10.1016/j.petrol.2020.107539},
issn = {0920-4105},
year = {2020},
date = {2020-12-01},
journal = {Journal of Petroleum Science and Engineering},
volume = {195},
pages = {17},
abstract = {The integrity of cement in cased boreholes is typically evaluated using well logging. However, well logging results are complex and can be ambiguous, and decisions associated with significant risks may be taken based on their interpretation. Cement evaluation logs must therefore be interpreted by trained professionals. To aid these interpreters, we propose a system for automatically interpreting cement evaluation logs, which they can use as a basis for their own interpretation. This system is based on deep convolutional neural networks, which we train in a supervised manner using a dataset of around 60 km of interpreted well log data. Thus, the networks learn the connections between data and interpretations during training. More specifically, the task of the networks is to classify the bond quality (among 6 ordinal classes) and the hydraulic isolation (2 classes) in each 1 m depth segment of each well based on the surrounding 13 m of well log data. We quantify the networks' performance by comparing over all segments how well the networks' interpretations of unseen data match the reference interpretations. For bond quality, the networks’ interpretation exactly matches the reference 51.6% of the time and is off by no more than one class 88.5% of the time. For hydraulic isolation, the interpretations match the reference 86.7% of the time. For comparison, a random-guess baseline gives matches of 16.7%, 44.4%, and 50%, respectively. We also compare with how well human reinterpretations of the log data match the reference interpretations, finding that the networks match the reference somewhat better. This may be linked to the networks learning and sharing the biases of the team behind the reference interpretations. An analysis of the results indicates that the subjectivity inherent in the interpretation process (and thereby in the reference interpretations we used for training and testing) is the main reason why we were not able to achieve an even better match between the networks and the reference.},
keywords = {acoustics, machine learning, well logging},
pubstate = {published},
tppubtype = {article}
}

The integrity of cement in cased boreholes is typically evaluated using well logging. However, well logging results are complex and can be ambiguous, and decisions associated with significant risks may be taken based on their interpretation. Cement evaluation logs must therefore be interpreted by trained professionals. To aid these interpreters, we propose a system for automatically interpreting cement evaluation logs, which they can use as a basis for their own interpretation. This system is based on deep convolutional neural networks, which we train in a supervised manner using a dataset of around 60 km of interpreted well log data. Thus, the networks learn the connections between data and interpretations during training. More specifically, the task of the networks is to classify the bond quality (among 6 ordinal classes) and the hydraulic isolation (2 classes) in each 1 m depth segment of each well based on the surrounding 13 m of well log data. We quantify the networks' performance by comparing over all segments how well the networks' interpretations of unseen data match the reference interpretations. For bond quality, the networks’ interpretation exactly matches the reference 51.6% of the time and is off by no more than one class 88.5% of the time. For hydraulic isolation, the interpretations match the reference 86.7% of the time. For comparison, a random-guess baseline gives matches of 16.7%, 44.4%, and 50%, respectively. We also compare with how well human reinterpretations of the log data match the reference interpretations, finding that the networks match the reference somewhat better. This may be linked to the networks learning and sharing the biases of the team behind the reference interpretations. An analysis of the results indicates that the subjectivity inherent in the interpretation process (and thereby in the reference interpretations we used for training and testing) is the main reason why we were not able to achieve an even better match between the networks and the reference.

Viggen, Erlend Magnus (Ed.)

Proceedings of the 43rd Scandinavian Symposium on Physical Acoustics Book

Norwegian Physical Society, 2020, ISBN: 978-82-8123-020-0.

BibTeX | Tags: acoustics | Links:

Viggen, Erlend Magnus; Hårstad, Erlend; Kvalsvik, Jørgen

Getting started with acoustic well log data using the dlisio Python library on the Volve Data Village dataset Conference

Proceedings of the 43rd Scandinavian Symposium on Physical Acoustics, Norwegian Physical Society, Geilo, Norway, 2020, ISBN: 978-82-8123-020-0.

Abstract | BibTeX | Tags: acoustics, well logging | Links:

@conference{viggen_getting_2020,
title = {Getting started with acoustic well log data using the dlisio Python library on the Volve Data Village dataset},
author = {Erlend Magnus Viggen and Erlend Hårstad and Jørgen Kvalsvik},
editor = {Erlend Magnus Viggen and Lars Hoff},
url = {https://www.researchgate.net/publication/340645995_Getting_started_with_acoustic_well_log_data_using_the_dlisio_Python_library_on_the_Volve_Data_Village_dataset, Full-text on ResearchGate
https://github.com/equinor/dlisio-notebooks/blob/master/acoustic.ipynb, Companion Jupyter Notebook},
isbn = {978-82-8123-020-0},
year = {2020},
date = {2020-04-15},
booktitle = {Proceedings of the 43rd Scandinavian Symposium on Physical Acoustics},
pages = {36},
publisher = {Norwegian Physical Society},
address = {Geilo, Norway},
abstract = {Three issues have long impeded academic research and teaching on well logging. First, real measured data has been hard to come by. This has now been alleviated by Equinor's 2018 release of the Volve Data Village dataset. Among its 5 TB of data, it contains 16.3 GB of various well log data, plots, and analyses. Second, no free and effective software tools to programmatically read DLIS files, one of the most common file formats for well log data today and by far the most common format in the Volve Data Village, have been available. This has now been remedied by the free and open-source Python library dlisio, first released by Equinor in 2018 and still under heavy development. Third, the data is often difficult to understand, as sufficient documentation is often not publicly available. As different tools measure, process, and store their data differently, different tools must be understood individually. This article aims to stimulate research into well logging, by showing how to use dlisio to investigate well log data from the Volve Data Village dataset. While the investigative methods used here can be adapted to other kinds of data, this article focuses on acoustic integrity logs. Specifically, we investigate data from a sonic tool (DSLT) and an ultrasonic tool (USIT), both extensively used in the dataset. In addition to identifying what the most fundamental pieces of data represent, we also show some simple examples of how this data can be reprocessed to find new results not provided in the well log file. We provide the code underlying this article in an accompanying Jupyter Notebook.},
keywords = {acoustics, well logging},
pubstate = {published},
tppubtype = {conference}
}

2019

Viggen, Erlend Magnus; Hoff, Lars (Ed.)

Proceedings of the 42nd Scandinavian Symposium on Physical Acoustics Book

Norwegian Physical Society, 2019, ISBN: 978-82-8123-019-4.

BibTeX | Tags: acoustics | Links:

2018

Gelderblom, Femke B; Tronstad, Tron V; Viggen, Erlend Magnus

Subjective evaluation of a noise-reduced training target for deep neural network-based speech enhancement Journal Article

IEEE/ACM Transactions on Audio, Speech, and Language Processing, 27 (3), pp. 583–594, 2018, ISSN: 2329-9290.

Abstract | BibTeX | Tags: machine learning, speech enhancement | Links:

@article{gelderblom_subjective_2018,
title = {Subjective evaluation of a noise-reduced training target for deep neural network-based speech enhancement},
author = {Femke B. Gelderblom and Tron V. Tronstad and Erlend Magnus Viggen},
url = {https://erlend-viggen.no/wp-content/uploads/2018/11/gelderblom_subjective_2018_post-print.pdf, Full-text},
doi = {10.1109/TASLP.2018.2882738},
issn = {2329-9290},
year = {2018},
date = {2018-11-21},
journal = {IEEE/ACM Transactions on Audio, Speech, and Language Processing},
volume = {27},
number = {3},
pages = {583–594},
abstract = {Speech enhancement systems aim to improve the quality and intelligibility of noisy speech. In this study, we compare two speech enhancement systems based on deep neural networks. The speech intelligibility and quality of both systems was evaluated subjectively, by a Speech Recognition Test based on Hagerman sentences and a translation of the ITU-T P.835 recommendation, respectively. Results were compared with the objective measures STOI and POLQA. Neither STOI nor POLQA reliably predicted subjective results. While STOI anticipated improvement, subjective results for both models showed degradation of speech intelligibility. POLQA results were overall hardly affected, while the subjective results showed significant changes in overall quality, both positive and negative, in many of the tests. One of the systems was trained to remove all noise; a strategy that is common in speech enhancement systems found in the literature. The other system was trained to only reduce the noise such that the signal-to-noise ratio increased with 10 dB. The latter system subjectively outperformed the system that attempted to remove noise completely. From this, we conclude that objective evaluation cannot replace subjective evaluation until a measure that reliably predicts intelligibility and quality for deep neural network based systems has been identified. Results further indicate that it may be beneficial to move away from more aggressive noise removal strategies towards noise reduction strategies that cause less speech distortion.},
keywords = {machine learning, speech enhancement},
pubstate = {published},
tppubtype = {article}
}

2017

Krüger, Timm; Kusumaatmaja, Halim; Kuzmin, Alexandr; Shardt, Orest; Silva, Goncalo; Viggen, Erlend Magnus

The lattice Boltzmann method: Principles and practice Book

Springer International Publishing, 2017, ISBN: 978-3-319-44647-9 / 978-3-319-44649-3.

BibTeX | Tags: lattice boltzmann | Links:

Gelderblom, Femke B; Tronstad, Tron V; Viggen, Erlend Magnus

Subjective Intelligibility of Deep Neural Network-Based Speech Enhancement Conference

INTERSPEECH 2017, ISCA, 2017.

Abstract | BibTeX | Tags: machine learning, speech enhancement | Links:

Viggen, Erlend Magnus; Johansen, Tonni Franke; Merciu, Ioan-Alexandru

Simulation and inversion of ultrasonic pitch-catch through-tubing well logging with an array of receivers Journal Article

NDT & E International, 85 , pp. 72–75, 2017, ISSN: 09638695.

Abstract | BibTeX | Tags: acoustics, well logging | Links:

2016

Viggen, Erlend Magnus; Johansen, Tonni Franke; Merciu, Ioan-Alexandru

Simulation and modeling of ultrasonic pitch-catch through-tubing logging Journal Article

Geophysics, 81 (4), pp. D383-D393, 2016.

Abstract | BibTeX | Tags: acoustics, well logging | Links:

@article{viggen_simulation_2016,
title = {Simulation and modeling of ultrasonic pitch-catch through-tubing logging},
author = {Erlend Magnus Viggen and Tonni Franke Johansen and Ioan-Alexandru Merciu},
url = {https://erlend-viggen.no/wp-content/uploads/2018/04/viggen_simulation_2016.pdf, Full-text},
doi = {10.1190/geo2015-0251.1},
year = {2016},
date = {2016-07-01},
journal = {Geophysics},
volume = {81},
number = {4},
pages = {D383-D393},
abstract = {Cased petroleum wells must be logged to determine the bonding and hydraulic isolation properties of the sealing material and to determine the structural integrity status. Although ultrasonic pitch-catch logging in single-casing geometries has been widely studied and is commercially available, this is not the case for logging in double-casing geometries despite its increasing importance in plug and abandonment operations. It is therefore important to investigate whether existing logging tools can be used in such geometries. Using a finite-element model of a double-casing geometry with a two-receiver pitch-catch setup, we have simulated through-tubing logging, with fluid between the two casings. We found that there appears a cascade of leaky Lamb wave packets on both casings, linked by leaked wavefronts. By varying the geometry and materials in the model, we have examined the effect on the pulse received from the second wave packet on the inner casing, sometimes known as the third interface echo. The amplitude of this pulse was found to contain information on the bonded material in the outer annulus. Much stronger amplitude variations were found with two equally thick casings than with a significant thickness difference; relative thickness differences of up to one-third were simulated. Finally, we have developed a simple mathematical model of the wave packets’ time evolution to encapsulate and validate our understanding of the wave packet cascade. This model shows a more complex time evolution in the later wave packets than the exponentially attenuated primary packet, which is currently used for single-casing logging. This indicates that tools with more than two receivers, which could measure wave packets’ amplitude at more than two points along their time evolution, would be able to draw more information from these later packets. The model was validated against simulations, finding good agreement when the underlying assumptions of the model were satisfied.},
keywords = {acoustics, well logging},
pubstate = {published},
tppubtype = {article}
}

Viggen, Erlend Magnus; Kristiansen, Ulf R (Ed.)

Proceedings of the 39th Scandinavian Symposium on Physical Acoustics Book

2016, ISBN: 978-82-8123-016-3.

BibTeX | Tags: acoustics | Links:

Viggen, Erlend Magnus; Johansen, Tonni Franke; Merciu, Ioan-Alexandru

Analysis of outer-casing echoes in simulations of ultrasonic pulse-echo through-tubing logging Journal Article

Geophysics, 81 (6), pp. D679–D685, 2016, ISSN: 0016-8033, 1942-2156.

Abstract | BibTeX | Tags: acoustics, well logging | Links:

@article{viggen_analysis_2016,
title = {Analysis of outer-casing echoes in simulations of ultrasonic pulse-echo through-tubing logging},
author = {Erlend Magnus Viggen and Tonni Franke Johansen and Ioan-Alexandru Merciu},
url = {https://erlend-viggen.no/wp-content/uploads/2018/04/viggen_analysis_2016.pdf, Full-text},
doi = {10.1190/geo2015-0376.1},
issn = {0016-8033, 1942-2156},
year = {2016},
date = {2016-01-01},
urldate = {2017-10-11},
journal = {Geophysics},
volume = {81},
number = {6},
pages = {D679--D685},
abstract = {Cased petroleum wells must be logged to determine the bonding and hydraulic isolation properties of the cement. Ultrasonic logging of single casings has been widely studied and is commercially available. However, ultrasonic logging in multiple-casing geometries is an unexplored topic despite its importance in plug and abandonment operations. Therefore, current logging technologies should be studied to evaluate whether they indicate the potential for multiple-casing logging. In this study, we used two finite-element models of pulse-echo logging. The first model represents logging in the transverse cross section of a double-casing well. The second model is a copy of the first, but with the outer casing and formation removed so that the pulse-echo transducer receives only a resonant first interface echo from the inner casing. By subtracting the received signals of the second model from those of the first, we can recover the third interface echo (TIE) signal representing the resonant reflection from the outer casing. This signal is used to study what information can, in principle, be drawn from TIEs in double-casing geometries, with the caveat that TIEs can only approximately be recovered in practical cases. We simulated variations of the material in the annulus beyond the outer casing, of the thickness of the outer casing, and of the eccentering of the outer casing. We have determined that the first two of these variations have only weak effects on the TIE, but that the eccentering of the outer casing can, in principle, be found using the TIE arrival time.},
keywords = {acoustics, well logging},
pubstate = {published},
tppubtype = {article}
}

2015

Viggen, Erlend Magnus; Solvang, Audun; Vennerød, Jakob; Olsen, Herold

Development of an outdoor auralisation prototype with 3D sound reproduction Conference

Proceedings of the 18th International Conference on Digital Audio Effects (DAFx-15), Trondheim, 2015.

Abstract | BibTeX | Tags: acoustics, auralisation | Links:

2014

Viggen, Erlend Magnus

The lattice Boltzmann method: Fundamentals and acoustics PhD Thesis

Norwegian University of Science and Technology, 2014.

Abstract | BibTeX | Tags: acoustics, lattice boltzmann | Links:

@phdthesis{viggen_lattice_2014,
title = {The lattice Boltzmann method: Fundamentals and acoustics},
author = {Erlend Magnus Viggen},
url = {https://www.researchgate.net/publication/263714289_The_lattice_Boltzmann_method_Fundamentals_and_acoustics, Full-text on ResearchGate},
year = {2014},
date = {2014-02-21},
address = {Trondheim},
school = {Norwegian University of Science and Technology},
abstract = {The lattice Boltzmann method has been widely used as a solver for incompressible flow, though it is not restricted to this application. More generally, it can be used as a compressible Navier-Stokes solver, albeit with a restriction that the Mach number is low. While that restriction may seem strict, it does not hinder the application of the method to the simulation of sound waves, for which the Mach numbers are generally very low. Even sound waves with strong nonlinear effects can be captured well. Despite this, the method has not been as widely used for problems where acoustic phenomena are involved as it has been for incompressible problems.

The research presented this thesis goes into three different aspects of lattice Boltzmann acoustics. Firstly, linearisation analyses are used to derive and compare the sound propagation properties of the lattice Boltzmann equation and comparable fluid models for both free and forced waves. The propagation properties of the fully discrete lattice Boltzmann equation are shown to converge at second order towards those of the discrete-velocity Boltzmann equation, which itself predicts the same lowest-order absorption but different dispersion to the other fluid models.

Secondly, it is shown how multipole sound sources can be created mesoscopically by adding a particle source term to the Boltzmann equation. This method is straightforwardly extended to the lattice Boltzmann method by discretisation. The results of lattice Boltzmann simulations of monopole, dipole, and quadrupole point sources are shown to agree very well with the combined predictions of this multipole method and the linearisation analysis. The exception to this agreement is the immediate vicinity of the point source, where the singularity in the analytical solution cannot be reproduced numerically.

Thirdly, an extended lattice Boltzmann model is described. This model alters the equilibrium distribution to reproduce variable equations of state while remaining simple to implement and efficient to run. To compensate for an unphysical bulk viscosity, the extended model contains a bulk viscosity correction term. It is shown that all equilibrium distributions that allow variable equations of state must be identical for the one-dimensional D1Q3 velocity set. Using such a D1Q3 velocity set and an isentropic equation of state, both mechanisms of nonlinear acoustics are captured successfully in a simulation, improving on previous isothermal simulations where only one mechanism could be captured. In addition, the effect of molecular relaxation on sound propagation is simulated using a model equation of state. Though the particular implementation used is not completely stable, the results agree well with theory.},
keywords = {acoustics, lattice boltzmann},
pubstate = {published},
tppubtype = {phdthesis}
}

The lattice Boltzmann method has been widely used as a solver for incompressible flow, though it is not restricted to this application. More generally, it can be used as a compressible Navier-Stokes solver, albeit with a restriction that the Mach number is low. While that restriction may seem strict, it does not hinder the application of the method to the simulation of sound waves, for which the Mach numbers are generally very low. Even sound waves with strong nonlinear effects can be captured well. Despite this, the method has not been as widely used for problems where acoustic phenomena are involved as it has been for incompressible problems.

The research presented this thesis goes into three different aspects of lattice Boltzmann acoustics. Firstly, linearisation analyses are used to derive and compare the sound propagation properties of the lattice Boltzmann equation and comparable fluid models for both free and forced waves. The propagation properties of the fully discrete lattice Boltzmann equation are shown to converge at second order towards those of the discrete-velocity Boltzmann equation, which itself predicts the same lowest-order absorption but different dispersion to the other fluid models.

Secondly, it is shown how multipole sound sources can be created mesoscopically by adding a particle source term to the Boltzmann equation. This method is straightforwardly extended to the lattice Boltzmann method by discretisation. The results of lattice Boltzmann simulations of monopole, dipole, and quadrupole point sources are shown to agree very well with the combined predictions of this multipole method and the linearisation analysis. The exception to this agreement is the immediate vicinity of the point source, where the singularity in the analytical solution cannot be reproduced numerically.

Thirdly, an extended lattice Boltzmann model is described. This model alters the equilibrium distribution to reproduce variable equations of state while remaining simple to implement and efficient to run. To compensate for an unphysical bulk viscosity, the extended model contains a bulk viscosity correction term. It is shown that all equilibrium distributions that allow variable equations of state must be identical for the one-dimensional D1Q3 velocity set. Using such a D1Q3 velocity set and an isentropic equation of state, both mechanisms of nonlinear acoustics are captured successfully in a simulation, improving on previous isothermal simulations where only one mechanism could be captured. In addition, the effect of molecular relaxation on sound propagation is simulated using a model equation of state. Though the particular implementation used is not completely stable, the results agree well with theory.

Viggen, Erlend Magnus

Acoustic equations of state for simple lattice Boltzmann velocity sets Journal Article

Physical Review E, 90 , pp. 013310, 2014, ISSN: 1539-3755, 1550-2376.

Abstract | BibTeX | Tags: acoustics, lattice boltzmann | Links: