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.@en

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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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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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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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.@en

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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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.@en

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. @en

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.@en

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.@en

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.@en

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).@en

Several seismic investigations - using receiver-function methods as well as tomographic approaches - have been carried out in the Malargüe region (Argentina) for various purposes over a few decades. We use a body-wave seismic interferometry (SI) approach to retrieve reflections later used for the consecutive imaging of the subsurface. We investigate the applicability of the body-wave SI using P-wave coda from local earthquakes with the aim to retrieve reflection responses from a part of the Andean crust below the seismic array we use. We called our technique local-earthquake P-wave coda (LEPC) SI. In this presentation, we show three different LEPC SI results based on three different SI theories: crosscorrelation, crosscoherence, and multidimensional deconvolution.We find that, from a structural-interpretation point of view, multidimensional deconvolution based on the truncated singularvalue decomposition scheme provides us with a better structural imaging than the other SI approaches.We interpret deep thrust faults in the imaging results from LEPC SI, whose presence in this region has previously been indicated from interpretation of active seismic-survey data and exploration-well data. We also interpret dimmed-amplitude parts in the reflection image as possible melting zones that have been previously indicated by magnetotelluric methods. The LEPC SI method we propose could be used as a low-cost alternative to active-source seismic surveys for imaging and monitoring purposes of deeper geothermal reservoirs, e.g. in enhanced geothermal systems where the target structures are down to 10 km depth. @en

We investigate the applicability of passive seismic interferometry using P-wave coda from local earthquakes for the purpose of retrieving reflections for imaging enhanced geothermal systems. For this, we use ambient-noise data recorded in the Neuquén basin, Argentina, where the Peteroa and Los Molles geothermal fields are present nearby. After retrieving reflections, we proceed to process them following a standard processing sequence to obtain images of the crustal structures. Examining crosscorrelation, crosscoherence, and multidimensional deconvolution approaches, we find that multidimensional deconvolution, based on the truncated singular-value decomposition scheme, gives us slightly better structural imaging than the other two approaches. Our results provide higher-resolution imaging of the crustal structures down to the lower boundary of the Moho in comparison with previous passive seismic imaging by receiver function and global-phase seismic interferometry in this region. We also interpret the deep basement thrust fault that has been indicated by active-seismic reflection profile and nearby exploration well. The method we propose could be used as a low-cost alternative to active-source acquisition for imaging and monitoring purposes of deeper geothermal reservoirs, e.g., in enhanced geothermal systems, where the target structures are down to 10 km depth. @en

In 30 years following NASA’s Apollo missions, numerous geophysical methods have been applied to determine the depth of the Lunar Moho. These methods, such as travel-time analysis and gravity inversion, have yielded inconsistent estimates. Here, we apply a seismic interferometry technique using body waves. We use deep moonquakes recorded by the Apollo stations to retrieve zero-offset reflection responses beneath each seismic station on the Nearside of the Moon. We call this application deep-moonquake seismic interferometry (DMSI). We present here the first pseudo-reflection imaging of the Lunar Moho, which we interpret to reside at around 50 km depth. Our interpretation agrees with JAXA’s SELENE result, and with earlier travel-time studies. Our DMSI results also show lateral inhomogeneity beneath the Moho, suggesting strong scattering within a zone characterized by seismic velocity that exhibits little variation at our resolution scale (0.2-2.0 Hz). This zone is where most of the shallow moonquakes are presumed to be occurring. @en

We investigated the applicability of global phases (epicentral distances of ≥ 120° and ≥ 150°) for the H/V spectral ratio to identify the fundamental resonance frequency. We applied the method to delineate a part of Neuquén basin in Argentina without the need for active seismic sources. We obtained fairly identifiable fundamental resonance frequencies in the range 0.15 Hz to 2.5 Hz. Receiver-side resonances were pronounced by stacking the H/V spectral ratio over many earthquake recordings. By doing so, the same fundamental resonances were found by using different window (P and S-phases) as well as different epicentral distances (≥ 120° and ≥ 150°). Our result, assuming average velocity, shows identical features in comparison with both the Bouguer anomaly and the active seismic profile nearby, indicating that our method is reliable. The method we demonstrate here can be applied in the fields particularly when the seismic array of long-term purposes is available.

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