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Y. Nishitsuji
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1
We examine the deep geothermal potential beneath Malargüe, Argentina, using global-phase seismic interferometry (GloPSI) and ghost reflections, retrieved from it, to analyze the intrinsic attenuation in the crust and upper mantle. By processing data from the MalARRgüe seismic array and distant earthquakes, we identify distinct patterns of seismic-wave attenuation: the crust displays moderate attenuation, while the upper mantle near the Moho discontinuity shows much higher attenuation. This sharp contrast suggests the presence of elevated temperatures or partial melt, possibly linked to a magma chamber, which could enhance geothermal potential in the region. The method provides higher spatial resolution and depth-specific information than traditional models, allowing non-invasive identification of zones with increased heat flow—potential geothermal “sweet spots.” This approach also avoids the need for expensive drilling, making it valuable for large-scale geothermal assessment.
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We examine the deep geothermal potential beneath Malargüe, Argentina, using global-phase seismic interferometry (GloPSI) and ghost reflections, retrieved from it, to analyze the intrinsic attenuation in the crust and upper mantle. By processing data from the MalARRgüe seismic array and distant earthquakes, we identify distinct patterns of seismic-wave attenuation: the crust displays moderate attenuation, while the upper mantle near the Moho discontinuity shows much higher attenuation. This sharp contrast suggests the presence of elevated temperatures or partial melt, possibly linked to a magma chamber, which could enhance geothermal potential in the region. The method provides higher spatial resolution and depth-specific information than traditional models, allowing non-invasive identification of zones with increased heat flow—potential geothermal “sweet spots.” This approach also avoids the need for expensive drilling, making it valuable for large-scale geothermal assessment.
Carbon dioxide capture, utilization, and storage (CCUS) are widely expected to play a significant role in decarbonization efforts. In discussing the commercialization of CCUS, it is essential to consider various factors, including the revenue and cost associated with CO2 utilization, carbon dioxide capture and storage (CCS), governmental subsidies, and carbon pricing. Among these, carbon pricing is particularly crucial for offsetting the costs of CCUS and enabling its commercialization. Carbon prices vary depending on factors such as the method of carbon credit issuance (e.g., forestry, solar, and wind energy), transaction mechanisms (e.g., cap-and-trade systems), carbon markets (e.g., voluntary markets), and country-specific regulations. Previous studies have investigated the impact of carbon pricing on carbon emissions; however, the pricing mechanisms are largely contingent on the method of carbon credit issuance. For instance, carbon credits generated through CCUS differ from those issued via forestry regarding associated costs, technologies, and subsidies. Therefore, carbon price models focusing on CCUS would be useful for the stakeholders, such as governments, firms, and investors. A significant challenge in developing an optimal carbon pricing model for CCUS lies in the complexity arising from uncertain parameters (e.g., carbon prices in voluntary markets, and revenue from CO2 utilization) across these stakeholders. This study presents a game theory-based model to provide an index of carbon credit revenue, i.e., the carbon price, to consider entry into the CCUS business. Our model aims to analytically simplify this complex problem with uncertain multi-parameters among several stakeholders and provide an index of the carbon price for CCUS in the voluntary carbon markets.
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Carbon dioxide capture, utilization, and storage (CCUS) are widely expected to play a significant role in decarbonization efforts. In discussing the commercialization of CCUS, it is essential to consider various factors, including the revenue and cost associated with CO2 utilization, carbon dioxide capture and storage (CCS), governmental subsidies, and carbon pricing. Among these, carbon pricing is particularly crucial for offsetting the costs of CCUS and enabling its commercialization. Carbon prices vary depending on factors such as the method of carbon credit issuance (e.g., forestry, solar, and wind energy), transaction mechanisms (e.g., cap-and-trade systems), carbon markets (e.g., voluntary markets), and country-specific regulations. Previous studies have investigated the impact of carbon pricing on carbon emissions; however, the pricing mechanisms are largely contingent on the method of carbon credit issuance. For instance, carbon credits generated through CCUS differ from those issued via forestry regarding associated costs, technologies, and subsidies. Therefore, carbon price models focusing on CCUS would be useful for the stakeholders, such as governments, firms, and investors. A significant challenge in developing an optimal carbon pricing model for CCUS lies in the complexity arising from uncertain parameters (e.g., carbon prices in voluntary markets, and revenue from CO2 utilization) across these stakeholders. This study presents a game theory-based model to provide an index of carbon credit revenue, i.e., the carbon price, to consider entry into the CCUS business. Our model aims to analytically simplify this complex problem with uncertain multi-parameters among several stakeholders and provide an index of the carbon price for CCUS in the voluntary carbon markets.
This study presents a game theory-based model to analyze the decision-making processes of key stakeholders in the exploration of natural hydrogen. The model incorporates uncertain parameters such as revenue from hydrogen businesses, government subsidies, and technological risks to provide a comprehensive framework for understanding the stakeholder decisions between governments and energy firms. Numerical experiments reveal that the probability of different Nash equilibria is significantly influenced by the levels of risk compensation and subsidies. Higher subsidies increase the likelihood of passive supervision and market entry by energy firms, while lower subsidies favor active supervision and market entry. These findings highlight key economic and regulatory considerations.
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This study presents a game theory-based model to analyze the decision-making processes of key stakeholders in the exploration of natural hydrogen. The model incorporates uncertain parameters such as revenue from hydrogen businesses, government subsidies, and technological risks to provide a comprehensive framework for understanding the stakeholder decisions between governments and energy firms. Numerical experiments reveal that the probability of different Nash equilibria is significantly influenced by the levels of risk compensation and subsidies. Higher subsidies increase the likelihood of passive supervision and market entry by energy firms, while lower subsidies favor active supervision and market entry. These findings highlight key economic and regulatory considerations.
A citation network analysis on diffusion of technologies to other fields
A case study about FWI
In recent years, the rapid changes in social trends and technologies, such as digital transformation and energy transition, have had a large impact on many industries. Future forecasts and exploration of potential values become indispensable for dealing with such changes and achieving the success of novel research and/or business. In this paper, we discuss an approach to evaluate the diffusion of innovative technologies to other fields using the network data in academic articles citing a review paper. This study provides a case study of full waveform inversion as an example in exploration geophysics to demonstrate the effectiveness of the approach by using the Web of Science database. This analysis enables us to forecast the trend of technologies by analyzing the diffusion of the other technologies as well as full waveform inversion.
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In recent years, the rapid changes in social trends and technologies, such as digital transformation and energy transition, have had a large impact on many industries. Future forecasts and exploration of potential values become indispensable for dealing with such changes and achieving the success of novel research and/or business. In this paper, we discuss an approach to evaluate the diffusion of innovative technologies to other fields using the network data in academic articles citing a review paper. This study provides a case study of full waveform inversion as an example in exploration geophysics to demonstrate the effectiveness of the approach by using the Web of Science database. This analysis enables us to forecast the trend of technologies by analyzing the diffusion of the other technologies as well as full waveform inversion.
We investigate an applicability of Bayesian-optimization (BO) to optimize hyperparameters associated with support-vector-machine (SVM) in order to classify facies using elastic properties derived from well data in the East Central Graben, UKCS. The cross-plot products of the field dataset appear to be successfully classified with non-linear boundaries. Although there are a few factors to be predetermined in the BO scheme such as an iteration number to deal with a trade-off between the prediction accuracy and the computational cost, this approach effectively reduces possible human subjectivity connected to the architecture of the SVM. Our proposed workflow might be beneficial in resource-exploration and development in terms of subsurface objective technical evaluations.
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We investigate an applicability of Bayesian-optimization (BO) to optimize hyperparameters associated with support-vector-machine (SVM) in order to classify facies using elastic properties derived from well data in the East Central Graben, UKCS. The cross-plot products of the field dataset appear to be successfully classified with non-linear boundaries. Although there are a few factors to be predetermined in the BO scheme such as an iteration number to deal with a trade-off between the prediction accuracy and the computational cost, this approach effectively reduces possible human subjectivity connected to the architecture of the SVM. Our proposed workflow might be beneficial in resource-exploration and development in terms of subsurface objective technical evaluations.
One of major technical competitions in energy industry relates to how optimally deep-learning architectures we can design. Optimization of hyperparameters is treated as labor-intensive. However, it is important to tune the parameters especially when we deal with relatively small targets, yet high-impact consequences can be resulted. In this study, we adapt Optuna, the global optimizer, for tuning the hyperparameter of the deep-learning scheme of the extended long-short term memory with forget gates. We apply this framework for predicting lithological facies. Although the macro difference with and without Optuna is not significant in this study, our results indicate that Optuna could make large commercial impacts when targets are small yet difficult to be captured.
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One of major technical competitions in energy industry relates to how optimally deep-learning architectures we can design. Optimization of hyperparameters is treated as labor-intensive. However, it is important to tune the parameters especially when we deal with relatively small targets, yet high-impact consequences can be resulted. In this study, we adapt Optuna, the global optimizer, for tuning the hyperparameter of the deep-learning scheme of the extended long-short term memory with forget gates. We apply this framework for predicting lithological facies. Although the macro difference with and without Optuna is not significant in this study, our results indicate that Optuna could make large commercial impacts when targets are small yet difficult to be captured.
Energy exploration for low-carbon resources
A case study of deep geothermal and lithium extraction by passive seismic interferometry with curvelet denoising
Conference paper
(2021)
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Yohei Nishitsuji, Shogo Masaya
We have investigated technical feasibility of a first exploration tool for deep geothermal and lithium extraction that are seen to be promising future energy resources with low-carbon emission. The method we adapted is called seismic interferometry which can redatum source positions to their receiver ones and then estimate an image of the subsurface from seismic data. We applied seismic interferometry to a passive seismic dataset acquired in the Neuquen basin, Argentina where relatively vast amount of heat resources are expected to be present. This region is highly active in terms of active volcanoes that are being surrounded by a complex geological structure of Andes Mountains associated with Nazca slab subduction. Since there are a number of interferometric techniques available to these days, we compared crosscorrelation, crosscoherence and multi-dimensional deconvolution in order to evaluate individual performance. From the comparison, we found that the virtual reflection profiles given by curvelet denoising applied to multi-dimensional deconvolution, which is stabilized using a singular-value decomposition, showed a better fault image whose existence was previously interpreted by active seismic and exploration wells in this region. Although initial stages of these low-carbon businesses tend to cost a lot especially in frontier regions, our approach has a capability to provide at least a scale of major faults which are considered to be potentially crucial for reservoir performance with a relatively small footprint on the surface.
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We have investigated technical feasibility of a first exploration tool for deep geothermal and lithium extraction that are seen to be promising future energy resources with low-carbon emission. The method we adapted is called seismic interferometry which can redatum source positions to their receiver ones and then estimate an image of the subsurface from seismic data. We applied seismic interferometry to a passive seismic dataset acquired in the Neuquen basin, Argentina where relatively vast amount of heat resources are expected to be present. This region is highly active in terms of active volcanoes that are being surrounded by a complex geological structure of Andes Mountains associated with Nazca slab subduction. Since there are a number of interferometric techniques available to these days, we compared crosscorrelation, crosscoherence and multi-dimensional deconvolution in order to evaluate individual performance. From the comparison, we found that the virtual reflection profiles given by curvelet denoising applied to multi-dimensional deconvolution, which is stabilized using a singular-value decomposition, showed a better fault image whose existence was previously interpreted by active seismic and exploration wells in this region. Although initial stages of these low-carbon businesses tend to cost a lot especially in frontier regions, our approach has a capability to provide at least a scale of major faults which are considered to be potentially crucial for reservoir performance with a relatively small footprint on the surface.
The characterization of the megaregolith on the Moon has been investigated in various ways including analysis of lunar meteorites, remote sensing of mineralogy and gravity, and deriving a seismic velocity profile. In this study, we propose a method for analyzing azimuthal anisotropy of the megaregolith. We call this method deep-moonquake seismic interferometry applied to S-wave coda (DMSI-S). DMSI-S can turn the records of deep moonquakes into recordings from virtual active sources. The retrieved virtual sources coincide with the station positions, and thus, we obtain virtual zero-offset (pulse-echo) measurements. DMSI-S is applied to seven clusters of deep moonquakes recorded at the Apollo 14 landing site, resulting in virtual zero-offset measurements at the Apollo station 14. We use the S-wave recordings retrieved from DMSI-S to analyze azimuthal anisotropy. We find weak anisotropy at the layer where the megaregolith is assumed to be present. We interpret our result to show that the megaregolith at this site is characterized by a layer (or layers) of impact material, following the Imbrium impact, with internal alignment of the crushed material.
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The characterization of the megaregolith on the Moon has been investigated in various ways including analysis of lunar meteorites, remote sensing of mineralogy and gravity, and deriving a seismic velocity profile. In this study, we propose a method for analyzing azimuthal anisotropy of the megaregolith. We call this method deep-moonquake seismic interferometry applied to S-wave coda (DMSI-S). DMSI-S can turn the records of deep moonquakes into recordings from virtual active sources. The retrieved virtual sources coincide with the station positions, and thus, we obtain virtual zero-offset (pulse-echo) measurements. DMSI-S is applied to seven clusters of deep moonquakes recorded at the Apollo 14 landing site, resulting in virtual zero-offset measurements at the Apollo station 14. We use the S-wave recordings retrieved from DMSI-S to analyze azimuthal anisotropy. We find weak anisotropy at the layer where the megaregolith is assumed to be present. We interpret our result to show that the megaregolith at this site is characterized by a layer (or layers) of impact material, following the Imbrium impact, with internal alignment of the crushed material.
Oil and gas companies evaluate the possibility of finding oil and gas fields carefully more than ever because it has been difficult to find gigantic discoveries which directly leads to their capital. Since a conventional evaluation contains human interpretation, luck and uncertainties, a variety of ranges of the reserves are often inferred from different interpreters given even identical dataset and conditions. As a consequence, there are differences between actual reserves and evaluated reserves. In this paper, using certain cases of how much actual reserves are deviated from interpreted reserves, deep learning is applied to mitigate such differences for unknown data which do not have actual reserves information. We find that our approach stably predicts the actual model by decreasing the misfit between the human and actual in comparison with the validation data on our workflow. The approach could be used to de-risk reserves estimation without changing traditional way of interpretations.
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Oil and gas companies evaluate the possibility of finding oil and gas fields carefully more than ever because it has been difficult to find gigantic discoveries which directly leads to their capital. Since a conventional evaluation contains human interpretation, luck and uncertainties, a variety of ranges of the reserves are often inferred from different interpreters given even identical dataset and conditions. As a consequence, there are differences between actual reserves and evaluated reserves. In this paper, using certain cases of how much actual reserves are deviated from interpreted reserves, deep learning is applied to mitigate such differences for unknown data which do not have actual reserves information. We find that our approach stably predicts the actual model by decreasing the misfit between the human and actual in comparison with the validation data on our workflow. The approach could be used to de-risk reserves estimation without changing traditional way of interpretations.
Journal article
(2019)
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M. Chamarczuk, M. Malinowski, Yohei Nishitsuji, Jan Willem Thorbecke, E. Koivisto, S. Heinonen, S. Juurela, M. Mężyk, Deyan Draganov
The main issues related to passive-source reflection imaging with seismic interferometry (SI) are inadequate acquisition parameters for sufficient spatial wavefield sampling and vulnerability of surface arrays to the dominant influence of the omnipresent surface-wave sources. Additionally, long recordings provide large data volumes that require robust and efficient processing methods. We address these problems by developing a two-step wavefield evaluation and event detection (TWEED) method of body waves in recorded ambient noise. TWEED evaluates the spatiotemporal characteristics of noise recordings by simultaneous analysis of adjacent receiver lines. We test our method on synthetic data representing transient ambient-noise sources at the surface and in the deeper subsurface. We discriminate between basic types of seismic events by using three adjacent receiver lines. Subsequently, we apply TWEED to 600 h of ambient noise acquired with an approximately 1000-receiver array deployed over an active underground mine in Eastern Finland. We develop the detection of body-wave events related to mine blasts and other routine mining activities using a representative 1 h noise panel. Using TWEED, we successfully detect 1093 body-wave events in the full data set. To increase the computational efficiency, we use slowness parameters derived from the first step of TWEED as input to a support vector machine (SVM) algorithm. Using this approach, we detect 94% of the TWEED-evaluated body-wave events indicating the possibility to limit the illumination analysis to only one step, and therefore increase the time efficiency at the price of lower detection rate. However, TWEED on a small volume of the recorded data followed by SVM on the rest of the data could be efficiently used for a quick and robust (real-time) scanning for body-wave energy in large data volumes for subsequent application of SI for retrieval of reflections.
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The main issues related to passive-source reflection imaging with seismic interferometry (SI) are inadequate acquisition parameters for sufficient spatial wavefield sampling and vulnerability of surface arrays to the dominant influence of the omnipresent surface-wave sources. Additionally, long recordings provide large data volumes that require robust and efficient processing methods. We address these problems by developing a two-step wavefield evaluation and event detection (TWEED) method of body waves in recorded ambient noise. TWEED evaluates the spatiotemporal characteristics of noise recordings by simultaneous analysis of adjacent receiver lines. We test our method on synthetic data representing transient ambient-noise sources at the surface and in the deeper subsurface. We discriminate between basic types of seismic events by using three adjacent receiver lines. Subsequently, we apply TWEED to 600 h of ambient noise acquired with an approximately 1000-receiver array deployed over an active underground mine in Eastern Finland. We develop the detection of body-wave events related to mine blasts and other routine mining activities using a representative 1 h noise panel. Using TWEED, we successfully detect 1093 body-wave events in the full data set. To increase the computational efficiency, we use slowness parameters derived from the first step of TWEED as input to a support vector machine (SVM) algorithm. Using this approach, we detect 94% of the TWEED-evaluated body-wave events indicating the possibility to limit the illumination analysis to only one step, and therefore increase the time efficiency at the price of lower detection rate. However, TWEED on a small volume of the recorded data followed by SVM on the rest of the data could be efficiently used for a quick and robust (real-time) scanning for body-wave energy in large data volumes for subsequent application of SI for retrieval of reflections.
We present a method for automatic detection and classification of seismic events from continuous ambient-noise (AN) recordings using an unsupervised machine-learning (ML) approach. We combine classic and recently developed array-processing techniques with ML enabling the use of unsupervised techniques in the routine processing of continuous data. We test our method on a dataset from a large-number (large-N) array, which was deployed over the Kylylahti underground mine (Finland), and show the potential to automatically process and cluster the volumes of AN data. Automatic sorting of detected events into different classes allows faster data analysis and facilitates the selection of desired parts of the wavefield for imaging (e.g., using seismic interferometry) and monitoring. First, using array-processing techniques, we obtain directivity, location, velocity, and frequency representations of AN data. Next, we transform these representations into vector-shaped matrices. The transformed data are input into a clustering algorithm (called k-means) to define groups of similar events, and optimization methods are used to obtain the optimal number of clusters (called elbow and silhouette tests). We use these techniques to obtain the optimal number of classes that characterize the AN recordings and consequently assign the proper class membership (cluster) to each data sample. For the Kylylahti AN, the unsupervised clustering produced 40 clusters. After visual inspection of events belonging to different clusters that were quality controlled by the silhouette method, we confirm the reliability of 10 clusters with a prediction accuracy higher than 90%. The obtained division into separate seismic-event classes proves the feasibility of the unsupervised ML approach to advance the automation of processing and the utilization of array AN data. Our workflow is very flexible and can be easily adapted for other input features and classification algorithms.
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We present a method for automatic detection and classification of seismic events from continuous ambient-noise (AN) recordings using an unsupervised machine-learning (ML) approach. We combine classic and recently developed array-processing techniques with ML enabling the use of unsupervised techniques in the routine processing of continuous data. We test our method on a dataset from a large-number (large-N) array, which was deployed over the Kylylahti underground mine (Finland), and show the potential to automatically process and cluster the volumes of AN data. Automatic sorting of detected events into different classes allows faster data analysis and facilitates the selection of desired parts of the wavefield for imaging (e.g., using seismic interferometry) and monitoring. First, using array-processing techniques, we obtain directivity, location, velocity, and frequency representations of AN data. Next, we transform these representations into vector-shaped matrices. The transformed data are input into a clustering algorithm (called k-means) to define groups of similar events, and optimization methods are used to obtain the optimal number of clusters (called elbow and silhouette tests). We use these techniques to obtain the optimal number of classes that characterize the AN recordings and consequently assign the proper class membership (cluster) to each data sample. For the Kylylahti AN, the unsupervised clustering produced 40 clusters. After visual inspection of events belonging to different clusters that were quality controlled by the silhouette method, we confirm the reliability of 10 clusters with a prediction accuracy higher than 90%. The obtained division into separate seismic-event classes proves the feasibility of the unsupervised ML approach to advance the automation of processing and the utilization of array AN data. Our workflow is very flexible and can be easily adapted for other input features and classification algorithms.
Journal article
(2019)
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Yohei Nishitsuji, Luis Franco Marín, Martín Gomez, C.A Rowe, Deyan Draganov
No conclusive evidence has been presented to date for tectonic tremor (TT) in the vicinity of central Chile, where the Nazca Plate is subducting beneath the South American Plate. Subduction in our experimental location (roughly 35.5° S, 70.5° W) is steep and fairly unobstructed compared to the flattened and more seismogenic behavior to the north. We seek to identify TT in our experimental area, whose geodynamics are comparable to tremor-rich subduction zones such as Cascadia and the Nankai Trough. Our method combines time-series visual inspection, frequency-spectrum analysis, waveform cross-correlation, and 3-component (3C) signal covariance to explore the presence of TT in this region. We have identified TT using stations in central Chile and the Malargüe region, Argentina. The TT exhibits similar features to other TT observations worldwide. Waveform characteristics for the TT in our study, particularly dimension of the 3C signal covariance, vary as a function of apparent source location. The duration of one episode of identified TT was about 10 h, which may indicate that the plate interface where tremor generates is strongly coupled. We conclude that our observations reflect features of the local propagation, rather than the tremor source itself.
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No conclusive evidence has been presented to date for tectonic tremor (TT) in the vicinity of central Chile, where the Nazca Plate is subducting beneath the South American Plate. Subduction in our experimental location (roughly 35.5° S, 70.5° W) is steep and fairly unobstructed compared to the flattened and more seismogenic behavior to the north. We seek to identify TT in our experimental area, whose geodynamics are comparable to tremor-rich subduction zones such as Cascadia and the Nankai Trough. Our method combines time-series visual inspection, frequency-spectrum analysis, waveform cross-correlation, and 3-component (3C) signal covariance to explore the presence of TT in this region. We have identified TT using stations in central Chile and the Malargüe region, Argentina. The TT exhibits similar features to other TT observations worldwide. Waveform characteristics for the TT in our study, particularly dimension of the 3C signal covariance, vary as a function of apparent source location. The duration of one episode of identified TT was about 10 h, which may indicate that the plate interface where tremor generates is strongly coupled. We conclude that our observations reflect features of the local propagation, rather than the tremor source itself.
Machine learning methods including support-vector-machine and deep learning are applied to facies classification problems using elastic impedances acquired from a Paleocene oil discovery in the UK Central North Sea. Both of the supervised learning approaches showed similar accuracy when predicting facies after the optimization of hyperparameters derived from well data. However, the results obtained by deep learning provided better correlation with available wells and more precise decision boundaries in cross-plot space when compared to the support-vector-machine approach. Results from the support-vector-machine and deep learning classifications are compared against a simplified linear projection based classification and a Bayes-based approach. Differences between the various facies classification methods are connected by not only their methodological differences but also human interactions connected to the selection of machine learning parameters. Despite the observed differences, machine learning applications, such as deep learning, have the potential to become standardized in the industry for the interpretation of amplitude versus offset cross-plot problems, thus providing an automated facies classification approach.
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Machine learning methods including support-vector-machine and deep learning are applied to facies classification problems using elastic impedances acquired from a Paleocene oil discovery in the UK Central North Sea. Both of the supervised learning approaches showed similar accuracy when predicting facies after the optimization of hyperparameters derived from well data. However, the results obtained by deep learning provided better correlation with available wells and more precise decision boundaries in cross-plot space when compared to the support-vector-machine approach. Results from the support-vector-machine and deep learning classifications are compared against a simplified linear projection based classification and a Bayes-based approach. Differences between the various facies classification methods are connected by not only their methodological differences but also human interactions connected to the selection of machine learning parameters. Despite the observed differences, machine learning applications, such as deep learning, have the potential to become standardized in the industry for the interpretation of amplitude versus offset cross-plot problems, thus providing an automated facies classification approach.
Semi-supervised deep-learning architectures provide a multi-layer, pattern recognition, approach that is powerful and ideally suited to the data rich environment that exists at the heart of the oil and gas industry. In this study we apply this technology in order to classify facies using elastic impedances from UK North Sea well and seismic data. The semi-supervised deep-learning method in this study uses a self-training strategy that combines both labelled and unlabelled data during the training phase so that classified data subsequently becomes part of the training dataset in the next iteration. This approach is ideal when the availability of labelled data is limited by practical constraints, which is often the case in subsurface geoscience. The resulting outputs of classified facies were visualised using elastic impedance cross-plots after application to a single training well from a North Sea oil discovery. To validate the result we upscaled the classification model to equivalent seismic data in order to compare the learning from the training well with two blind wells. The results indicate that semi-supervised deep-learning has the potential to accurately determine facies, including hydrocarbon distributions, in subsurface data at a field scale.
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Semi-supervised deep-learning architectures provide a multi-layer, pattern recognition, approach that is powerful and ideally suited to the data rich environment that exists at the heart of the oil and gas industry. In this study we apply this technology in order to classify facies using elastic impedances from UK North Sea well and seismic data. The semi-supervised deep-learning method in this study uses a self-training strategy that combines both labelled and unlabelled data during the training phase so that classified data subsequently becomes part of the training dataset in the next iteration. This approach is ideal when the availability of labelled data is limited by practical constraints, which is often the case in subsurface geoscience. The resulting outputs of classified facies were visualised using elastic impedance cross-plots after application to a single training well from a North Sea oil discovery. To validate the result we upscaled the classification model to equivalent seismic data in order to compare the learning from the training well with two blind wells. The results indicate that semi-supervised deep-learning has the potential to accurately determine facies, including hydrocarbon distributions, in subsurface data at a field scale.
Journal article
(2017)
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Deyan Draganov, Yohei Nishitsuji, Martin Gomez, Boris Boullenger, Shohei Minato, Kees Wapenaar, Jan Willem Thorbecke, Elmer Ruigrok, Charlotte Rowe, Bob Paap, Arie Verdel
The reflection seismic method is the most frequently used exploration method for imaging and monitoring subsurface structures with high resolution. It has proven its qualities from the scale of regional seismology to the scale of near-surface applications that look just a few meters below the surface. The reflection method uses controlled active sources at known positions to give rise to reflections recorded at known receiver positions. The reflections’ two-wave travel time is used to extract desired information about and image the subsurface structures. When active sources are unavailable or undesired, one can retrieve body-wave reflections from application of seismic interferometry (SI) to sources of opportunity—quakes, tremors, ambient noise, or even man-made sources not connected to the exploration campaign. We show examples of imaging of subsurface structures using reflections retrieved from quakes and ambient noise. We apply SI by autocorrelation to global earthquake to image seismic and aseismic parts of the Nazca plate and the Moho at these places, SI by multidimensional deconvolution to P-wave coda from local earthquakes to image the Moho and the crust at the same places, and SI by autocorrelation to deep moonquakes to image the lunar Moho and to ambient noise to monitor CO2 sequestration.
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The reflection seismic method is the most frequently used exploration method for imaging and monitoring subsurface structures with high resolution. It has proven its qualities from the scale of regional seismology to the scale of near-surface applications that look just a few meters below the surface. The reflection method uses controlled active sources at known positions to give rise to reflections recorded at known receiver positions. The reflections’ two-wave travel time is used to extract desired information about and image the subsurface structures. When active sources are unavailable or undesired, one can retrieve body-wave reflections from application of seismic interferometry (SI) to sources of opportunity—quakes, tremors, ambient noise, or even man-made sources not connected to the exploration campaign. We show examples of imaging of subsurface structures using reflections retrieved from quakes and ambient noise. We apply SI by autocorrelation to global earthquake to image seismic and aseismic parts of the Nazca plate and the Moho at these places, SI by multidimensional deconvolution to P-wave coda from local earthquakes to image the Moho and the crust at the same places, and SI by autocorrelation to deep moonquakes to image the lunar Moho and to ambient noise to monitor CO2 sequestration.
Geophysics publishes abstracts of dissertations and titles of master’s theses both in print and online. Recent graduates are invited to submit their abstracts or titles by completing and submitting the appropriate form found at http://seg.org/dissertationabstracts. Abstracts and titles will be reviewed and accepted or rejected based on their relevance to the readers of Geophysics. Abstracts must be written in English and defended in 2009 or later.
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Geophysics publishes abstracts of dissertations and titles of master’s theses both in print and online. Recent graduates are invited to submit their abstracts or titles by completing and submitting the appropriate form found at http://seg.org/dissertationabstracts. Abstracts and titles will be reviewed and accepted or rejected based on their relevance to the readers of Geophysics. Abstracts must be written in English and defended in 2009 or later.
This thesis investigates the potential of passive seismic methods that make use of body waves, and especially the passive reflection method, as cost-effective applications for multiscale subsurface imaging and characterization. For this purpose, we develop several seismic techniques for different scales: basin, crustal, and lithospheric. For the basin scale, we developed horizontal- and vertical-components spectral ratio of global earthquake phases to estimate the basin depth. We also used the Sp-wave method and analysis of the frequency-dependent quality factor to characterize the basin’s heterogeneities. The results show good agreement with active-seismic profiles. At the crustal scale, we investigated the application of seismic interferometry (SI). Comparison among different SI methodologies suggests that multidimensional deconvolution based on the truncated singular-value decomposition gives better structural imaging than do the conventional crosscorrelation or crosscoherence approaches, but also better than multidimensional deconvolution based on the damped least-squares scheme. This crustal-scale SI could be useful, for example, as a prescreening-exploration tool for deep geothermal reservoirs whose targets can be as deep as 10 km. At the lithospheric scale we studied not only the Earth, but also the Moon. For the Earth, we applied SI with global phases to obtain detailed images of aseismic parts of a subduction slab. Although the interpretation of the imaging results of the aseismic parts is not sufficiently decisive, the results suggest that the applied method is helpful for imaging aseismic parts of slabs. Furthermore, the radiation efficiency of intermediate-depth earthquakes is estimated to understand the source mechanism as a function of focal depth. The results indicate that there is a larger amount of non-radiated energy for intermediate-depth earthquakes. This implies one of the mechanisms for the slabs to be aseismic at certain depths. For the Moon, we applied SI to deep moonquakes to obtain reflection imaging of the lunar subsurface. With this application, the lunar Moho is interpreted to be around 50 km depth, indicating the potential usefulness of SI for other celestial bodies. Following the results obtained in this thesis, we conclude that the passive seismic methods with natural quakes have excellent potential usage in both the resource industry and academia.
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This thesis investigates the potential of passive seismic methods that make use of body waves, and especially the passive reflection method, as cost-effective applications for multiscale subsurface imaging and characterization. For this purpose, we develop several seismic techniques for different scales: basin, crustal, and lithospheric. For the basin scale, we developed horizontal- and vertical-components spectral ratio of global earthquake phases to estimate the basin depth. We also used the Sp-wave method and analysis of the frequency-dependent quality factor to characterize the basin’s heterogeneities. The results show good agreement with active-seismic profiles. At the crustal scale, we investigated the application of seismic interferometry (SI). Comparison among different SI methodologies suggests that multidimensional deconvolution based on the truncated singular-value decomposition gives better structural imaging than do the conventional crosscorrelation or crosscoherence approaches, but also better than multidimensional deconvolution based on the damped least-squares scheme. This crustal-scale SI could be useful, for example, as a prescreening-exploration tool for deep geothermal reservoirs whose targets can be as deep as 10 km. At the lithospheric scale we studied not only the Earth, but also the Moon. For the Earth, we applied SI with global phases to obtain detailed images of aseismic parts of a subduction slab. Although the interpretation of the imaging results of the aseismic parts is not sufficiently decisive, the results suggest that the applied method is helpful for imaging aseismic parts of slabs. Furthermore, the radiation efficiency of intermediate-depth earthquakes is estimated to understand the source mechanism as a function of focal depth. The results indicate that there is a larger amount of non-radiated energy for intermediate-depth earthquakes. This implies one of the mechanisms for the slabs to be aseismic at certain depths. For the Moon, we applied SI to deep moonquakes to obtain reflection imaging of the lunar subsurface. With this application, the lunar Moho is interpreted to be around 50 km depth, indicating the potential usefulness of SI for other celestial bodies. Following the results obtained in this thesis, we conclude that the passive seismic methods with natural quakes have excellent potential usage in both the resource industry and academia.
Journal article
(2016)
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Yohei Nishitsuji, Shohei Minato, Boris Boullenger, M Gomez, Kees Wapenaar, Deyan Draganov
We have developed an application of passive seismic interferometry (SI) using P-wave coda of local earthquakes for the purpose of crustal-scale reflection imaging. We processed the reflection gathers retrieved from SI following a standard seismic processing in exploration seismology. We applied SI to the P-wave coda using crosscorrelation, crosscoherence, and multidimensional deconvolution (MDD) approaches for data recorded in the Malargüe region, Argentina. Comparing the results from the three approaches, we found that MDD based on the truncated singular-value decomposition scheme gave us substantially better structural imaging. Although our results provided higher resolution images of the subsurface, they showed less clear images for the Moho in comparison with previous seismic images in the region obtained by the receiver function and global-phase SI. Above the Moho, we interpreted a deep thrust fault and the possible melting zones, which were previously indicated by active-seismic and magnetotelluric methods in this region, respectively. The method we developed could be an alternative option not only for crustal-scale imaging, e.g., in enhanced geothermal systems, but also for lithospheric-scale as well as basin-scale imaging, depending on the availability of local earthquakes and the frequency bandwidth of their P-wave coda.
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We have developed an application of passive seismic interferometry (SI) using P-wave coda of local earthquakes for the purpose of crustal-scale reflection imaging. We processed the reflection gathers retrieved from SI following a standard seismic processing in exploration seismology. We applied SI to the P-wave coda using crosscorrelation, crosscoherence, and multidimensional deconvolution (MDD) approaches for data recorded in the Malargüe region, Argentina. Comparing the results from the three approaches, we found that MDD based on the truncated singular-value decomposition scheme gave us substantially better structural imaging. Although our results provided higher resolution images of the subsurface, they showed less clear images for the Moho in comparison with previous seismic images in the region obtained by the receiver function and global-phase SI. Above the Moho, we interpreted a deep thrust fault and the possible melting zones, which were previously indicated by active-seismic and magnetotelluric methods in this region, respectively. The method we developed could be an alternative option not only for crustal-scale imaging, e.g., in enhanced geothermal systems, but also for lithospheric-scale as well as basin-scale imaging, depending on the availability of local earthquakes and the frequency bandwidth of their P-wave coda.
Obtaining detailed images of aseismic parts of subducting slabs remains a large challenge for understanding slab dynamics. Hypocenter mapping cannot be used for the purpose due to the absence of seismicity, whereas the use of receiver functions might be compromised by the presence of melt. Global tomography can be used to identify the presence of the slab, but it does not reveal the structure in detail. We have determined how detailed images can be obtained using global-phase seismic interferometry. The method provides high-resolution (<15km in depth) pseudo zero-offset (i.e., colocated source and receiver) reflection information. We have applied the method to aseismic zones of the Nazca slab in which initiation of possible slab tearing and plume decapitation was identified by global tomography and electrical conductivity, respectively. We have obtained an image of the Moho and the mantle and found an attenuated area in the image consistent with the presence of an aseismic dipping subducting slab. However, our interpretation was not unambiguous. The results confirmed that the method is useful for imaging aseismic transects of slabs.
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Obtaining detailed images of aseismic parts of subducting slabs remains a large challenge for understanding slab dynamics. Hypocenter mapping cannot be used for the purpose due to the absence of seismicity, whereas the use of receiver functions might be compromised by the presence of melt. Global tomography can be used to identify the presence of the slab, but it does not reveal the structure in detail. We have determined how detailed images can be obtained using global-phase seismic interferometry. The method provides high-resolution (<15km in depth) pseudo zero-offset (i.e., colocated source and receiver) reflection information. We have applied the method to aseismic zones of the Nazca slab in which initiation of possible slab tearing and plume decapitation was identified by global tomography and electrical conductivity, respectively. We have obtained an image of the Moho and the mantle and found an attenuated area in the image consistent with the presence of an aseismic dipping subducting slab. However, our interpretation was not unambiguous. The results confirmed that the method is useful for imaging aseismic transects of slabs.
The internal structure of the Moon has been investigated over many years using a variety of seismic methods, such as travel time analysis, receiver functions, and tomography. Here we propose to apply body-wave seismic interferometry to deep moonquakes in order to retrieve zero-offset reflection responses (and thus images) beneath the Apollo stations on the nearside of the Moon from virtual sources colocated with the stations. This method is called deep-moonquake seismic interferometry (DMSI). Our results show a laterally coherent acoustic boundary around 50 km depth beneath all four Apollo stations. We interpret this boundary as the lunar seismic Moho. This depth agrees with Japan Aerospace Exploration Agency's (JAXA) SELenological and Engineering Explorer (SELENE) result and previous travel time analysis at the Apollo 12/14 sites. The deeper part of the image we obtain from DMSI shows laterally incoherent structures. Such lateral inhomogeneity we interpret as representing a zone characterized by strong scattering and constant apparent seismic velocity at our resolution scale (0.2–2.0 Hz).
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The internal structure of the Moon has been investigated over many years using a variety of seismic methods, such as travel time analysis, receiver functions, and tomography. Here we propose to apply body-wave seismic interferometry to deep moonquakes in order to retrieve zero-offset reflection responses (and thus images) beneath the Apollo stations on the nearside of the Moon from virtual sources colocated with the stations. This method is called deep-moonquake seismic interferometry (DMSI). Our results show a laterally coherent acoustic boundary around 50 km depth beneath all four Apollo stations. We interpret this boundary as the lunar seismic Moho. This depth agrees with Japan Aerospace Exploration Agency's (JAXA) SELenological and Engineering Explorer (SELENE) result and previous travel time analysis at the Apollo 12/14 sites. The deeper part of the image we obtain from DMSI shows laterally incoherent structures. Such lateral inhomogeneity we interpret as representing a zone characterized by strong scattering and constant apparent seismic velocity at our resolution scale (0.2–2.0 Hz).