Acquisition Geometry Research Articles

Evaluation of a wheel-based seismic acquisition system for a planetary rover

Prior to using in-situ planetary resources, efficient mapping of geochemical and physical characteristics of the near surface will be required. As part of an integrated geophysical instrument suite on exploration and prospecting vehicles, we investigated the suitability of seismic piezo-sensors rigidly mounted on the interior of a generic rover wheel. Factors that can compromise proper data acquisition for this system include the natural mechanical resonance of the wheel and wheel-to-ground coupling. We characterized the natural resonance frequency bands of a generic wheel with an electromagnetic shaker. We also collected seismic shot gathers for subsequent seismic surface-wave analysis using a wheel in both a dry, laboratory sand tank (1.8 × 1.8 × 0.6 m) and in frozen loess soils within the United States Army Corps of Engineers Permafrost Tunnel in Alaska. For our wheel, self-weighted coupling to the ground was found to be adequate, although suitable wheelbase dimensions can constrain field acquisition geometries. In unconsolidated sediments, represented by medium sand (0.25–0.5 mm), wheel resonance of 1 kHz does not affect the fundamental mode used in shear-wave-velocity-to-depth inversion. When analyzed, shot-gather data collected from both wheel-mounted sensors and sand-planted sensors, in loose dry sand, effectively captured similar fundamental surface-wave modes. This is evident in frequency-versus-phase velocity dispersion images. Because narrow frequency bands of wheel resonance exhibit a high signal-to-noise ratio, they also readily detect lateral attenuation changes. Thus, wheel resonance can also be used to capture soil attenuation changes, including those produced when pore H2O ice acts to cement the regolith or loose-grained soils.

Deep Learning for Mapping Tropical Forests with TanDEM-X Bistatic InSAR Data

The TanDEM-X synthetic aperture radar (SAR) system allows for the recording of bistatic interferometric SAR (InSAR) acquisitions, which provide additional information to the common amplitude images acquired by monostatic SAR systems. More concretely, the volume decorrelation factor, which can be derived from the bistatic interferometric coherence, is a reliable indicator of the presence of vegetation and it was used as main input feature for the generation of the global TanDEM-X forest/non-forest map, by means of a clustering algorithm. In this work, we investigate the capabilities of deep Convolutional Neural Networks (CNNs) for mapping tropical forests at large-scale using TanDEM-X InSAR data. For this purpose, we rely on a U-Net architecture, which takes as input a set of feature maps selected on the basis of previous preparatory works. Moreover, we design an ad hoc training strategy, aimed at developing a robust model for global mapping purposes, which has to properly manage the large variety of different acquisition geometries characterizing the TanDEM-X global data set. In addition to detecting forest/non-forest areas, the CNN has also been trained to detect water surfaces, which are typically characterized by low values of coherence. By applying the proposed method on single TanDEM-X images, we achieved a significant performance improvement with respect to the baseline clustering approach, with an average F-score increase of 0.13. We then applied such a model for mapping the entire Amazon rainforest, as well as the other tropical forests in Central Africa and South-East Asia, in order to test its robustness and generalization capabilities, and we observed that forests are typically well detected as contour closed regions and that water classification is reliable, too. Finally, the generated maps show a great potential for mapping temporal changes occurring over forested areas and can be used for generating large-scale maps of deforestation.

Correcting laser scanning intensity recorded in a cave environment for high-resolution lithological mapping: A case study of the Gouffre Georges, France

Active remote sensing by laser scanning (LiDAR) has markedly improved the mapping of a cave environment with an unprecedented level of accuracy and spatial detail. However, the use of laser intensity simultaneously recorded during the scanning of caves remains unexplored despite it having promising potential for lithological mapping as it has been demonstrated by many applications in open-sky conditions. The appropriate use of laser intensity requires calibration and corrections for influencing factors, which are different in caves as opposed to the above-ground environments. Our study presents an efficient and complex workflow to correct the recorded intensity, which takes into consideration the acquisition geometry, micromorphology of the cave surface, and the specific atmospheric influence previously neglected in terrestrial laser scanning. The applicability of the approach is demonstrated on terrestrial LiDAR data acquired in the Gouffre Georges, a cave located in the northern Pyrenees in France. The cave is unique for its geology and lithology allowing for observation, with a spectacular continuity without any vegetal cover, of the contact between marble and lherzolite rocks and tectonic structures that characterize such contact. The overall accuracy of rock surface classification based on the corrected laser intensity was over 84%. The presence of water or a wet surface introduced bias of the intensity values towards lower values complicating the material discrimination. Such conditions have to be considered in applications of the recorded laser intensity in mapping underground spaces. The presented method allows for putting geological observations in an absolute spatial reference frame, which is often very difficult in a cave environment. Thus, laser scanning of the cave geometry assigned with the corrected laser intensity is an invaluable tool to unravel the complexity of such a lithological environment.

COMPUTING GLOBAL HARMONIC PARAMETERS FOR FLOOD MAPPING USING TU WIEN’S SAR DATACUBE SOFTWARE STACK

Abstract. TU Wien’s flood mapping algorithm, used for global operations, utilizes harmonic functions to model the seasonal behavior of backscatter to improve flood classification. In earth observation (EO), temporal harmonic models have been used in various scenarios for vegetation and water mapping in the optical and, recently, synthetic aperture radar (SAR) domains. These models condense EO time series stacks to a few Fourier coefficient images that capture seasonal variability, allowing for variable estimation for each day of the year. TU Wien’s harmonic parameters consist of these coefficients plus the regression standard deviation and number of observations. However, generating harmonic models at large scales and high resolution presents significant logistical and technical challenges. Particularly for SAR, which requires special handling due to acquisition geometry considerations, implementation on a datacube infrastructure is necessary for agile filtering in metadata, temporal and spatial dimensions. In this work, we highlight our harmonic parameter dataset and our software stack of loosely coupled Python packages, which were deployed in a high-performance computing environment to generate these parameters globally.

Remote Sensing Application for Landslide Detection, Monitoring along Eastern Lake Michigan (Miami Park, MI)

We assessed the nature and spatial and temporal patterns of deformation over the Miami Park bluffs on the eastern margin of Lake Michigan and investigated the factors controlling its observed deformation. Our approach involved the following steps: (1) extracting bluff deformation rates (velocities along the line of sight of the satellite) using a stack of Sentinel-1A radar imagery in ascending acquisition geometry acquired between 2017 and 2021 and applying the Intermittent Small Baseline Subset (ISBAS) InSAR time series analysis method; (2) generating high-resolution (5 cm) elevation models and orthophotos from temporal unmanned aerial vehicle (UAV) surveys acquired in 2017, 2019, and 2021; and (3) comparing the temporal variations in mass wasting events to other relevant datasets including the ISBAS-based bluff deformation time series, lake level (LL) variations, and local glacial stratigraphy. We identified areas witnessing high line-of-sight (LOS) deformation rates (up to −21 mm/year) along the bluff from the ISBAS analysis and seasonal deformation patterns associated with freeze-thaw cycles, suggesting a causal effect. The acceleration of slope failures detected from field and UAV acquisitions correlated with high LLs and intensified onshore wave energy in 2020. The adopted methodology successfully predicts landslides caused by freezes and thaws of the slope face by identifying prolonged slow deformation preceding slope failures, but it does not predict the catastrophic landslides preceded by short-lived LOS deformation related to LL rise.

4D seismic co-processing pre-stack depth migration for scarce acquisition repeatability

Time-lapse (4D) seismic processing is routinely used to monitor hydrocarbon reservoir production. Seismic reflections are sensitive to formation pressure and fluid content. This means that repeated seismic surveys can theoretically detect pressure changes and fluid changes associated with field production. These measurements can help optimize production strategy and identify areas where hydrocarbons have been bypassed. However, the seismic signal associated with such changes can be negligible, especially in heterogeneous carbonate reservoirs. To measure this 4D signal, the seismic acquisition must be repeated. Data vintages should be processed together to minimize differences unrelated to production. Repeatability of data acquisition is sometimes impossible to achieve in the Middle East due to environmental changes (e.g., dunes, currents, field facilities). Due to high cost and inadequate sampling, attempts to permanently bury seismic sources and receivers have failed.In this study, least-square pre-stack depth migration (LSM) co-processing was applied to remove the influence of survey design on the final image and pinching mark compared to conventional Kirchhoff pre-stack depth migration (KPSDM). The 4D physical, geometric, and seismic attributes are analyzed in the field as key diagnostic tools to evaluate the probability of a sighted 4D difference independent of two different acquisition geometries. The 4D analysis was performed on two case studies offshore Abu Dhabi, to determine which workflow and algorithm are most likely to ensure that the complete and optimal 4D processing sequence relaxes the need for seismic acquisition repeatability.

World-wide InSAR sensitivity index for landslide deformation tracking

• Sensitivity index: a priori assessment of InSAR geometry at any landslide. • 91% of the world’s slopes allow for displacement monitoring by InSAR. • Detection potential: assess the ability to extract InSAR deformation time series. • A Google Earth Engine app for world-wide semi-automated assessment of InSAR potential. Landslides are a major geohazard in hilly and mountainous environments. In-situ inspection of downslope motion is costly, sometimes dangerous and, requires prior knowledge of the existence of a landslide. Remote sensing from space is a way to detect and characterize landslides systematically at large scale. Interferometric Synthetic Aperture Radar (InSAR) has shown to be a valuable resource of deformation information, but it requires expert knowledge and considerable computational efforts. Moreover, the successful application of InSAR for landslides requires a favorable acquisition geometry relative to the landslide deformation pattern. Consequently, there is a need for a widely applicable tool to assess the potential of InSAR at a particular location a priori. Here we present a novel, generic approach to assess the potential of InSAR-based deformation tracking, providing a standardised and automated method applicable on any slope. We define the detection potential as the sensitivity of InSAR to detect downslope displacement combined with the presence of coherently scattering surfaces. We show that deformation can be detected on at least 91% of the global landslide-prone slopes, and provide an open source Google Earth Engine tool for the quick assessment of the availability of potential coherent scatterers. This tool enables any person interested in applying InSAR to routinely assess the potential for monitoring landslide deformation in their region of interest.

Simulation of Tomographic Acquisition and Reconstruction Slice for Industrial Machine Rotor with Fixed-Angle Scanner

Rotating machines such as electric motors and pumps are the heart of any industrial production chain. Rotors are usually the most worn parts, and the preventive maintenance of these vital organs, although necessary, remains penalizing for the production line because the machines are stopped and disassembled for a lengthy period during the inspection phase. This paper suggests a simulation of optimized tomographic reconstruction by cone beam acquisition geometry to scan a functioning machine rotor with high X-ray energy. In this work we propose a simple design of a fixed-angle projection scanner. Rotating projections are replaced by synchro-rotation to the nominal angular velocity of the rotor. The simulation results demonstrate that it is possible to adapt the filtered back-projection algorithm to compute a tomographic image of the rotor by a single projection device.

Deep learning-based point-spread function deconvolution for migration image deblurring

High-resolution seismic imaging is crucial for deriving an accurate geologic section and ensuring successful petroleum exploration. However, traditional high-resolution imaging methods, such as least-squares reverse time migration (LSRTM) and migration deconvolution, usually involve high computational costs for approximating the inverse Hessian matrix with limited improvements in resolution. To obtain high-resolution migration images and reduce computing costs, we present a deep learning-based point-spread function (PSF) deconvolution method. We decompose the large Hessian matrix into small dispersed PSFs and design a convolutional neural network (CNN) to automatically predict the deconvolution operator of every single PSF. The deconvolution operator eliminates the PSF effect and balances the insufficient illumination of the acquisition geometry. To obtain an efficient CNN model for predicting deconvolution operators, we trained 2500 pairs of PSFs and their corresponding deconvolution operators collected from part of a model using the regular PSF deconvolution method. After training the network, we could predict all deconvolution operators in a few seconds for the following processing of migration images. The results on synthetic and field-data applications indicate that our method could provide a deblurred migration image comparable to that of the LSRTM approaches with significantly reduced computational and memory costs.

Spatial Variability of In Situ Above-Water Reflectance in Coastal Dynamic Waters: Implications for Satellite Match-Up Analysis

The validation of ocean color satellite retrievals generally relies on analyzing match-ups between in situ measurements and satellite retrievals. These analyses focus on the quality of the satellite data, however, of the same importance is the quality of the in situ data. Here, we present the spatial variability of in situ above-water reflectance (Rrs(0+)) within the spatial resolution of different ocean color satellites—300, 900, 1500, and 3000 m spatial resolutions, mimicking Sentinel 3 OLCI and MODIS-Aqua satellites, and possible 3 × 3 and 5 × 5 windows. Radiometric data was acquired with autonomous radiometric sensors installed in the British Columbia Ferry Services Inc. vessel “Queen of Alberni” from May to September 2019, crossing the optically dynamic waters of the Strait of Georgia, Canada. The dataset followed optimal geometry of acquisition and processing, including corrections for skylight radiance signals, ship superstructure, the non-isotropic distribution of the water-leaving radiances, and quality control. A total of 33,073 spectra at full resolution, corresponding to 10 days, were considered for the analysis presented here. The results showed that, overall, the subpixel variability increased as the spatial resolution of the sensor or the window size increased, mainly in a linear fashion. Specifically, spatial variability of Rrs(0+) was the largest (∼18% and 68% for 900 and 3000 m pixel resolution, respectively) in Near Field Plume Interface waters, followed by in the Ocean Water Interface (∼28% and 35%, respectively), thus indicating spatial heterogeneity of interface waters. Further, we found that the estuarine waters showed higher subpixel Rrs(0+) variability (∼8% and 16% for 900 and 3000 m, respectively) compared with plume and oceanic waters. We showed that the high spatial variability in Rrs(0+) was primarily associated with the spatial dynamics of the optical water constituents, thus limiting the use of these datasets as Fiducial Reference Measurements and for validation of satellite-derived atmospherically corrected reflectance. We suggest that spatial variability of the in situ Rrs(0+) should also be considered in the selection criteria for good match-up data, especially for data acquired in coastal dynamic systems. As a result, it will advocate for the exclusion of interface or transition water pixel grids in order to avoid compromising the statistical result of satellite validation.

Suppressing migration noise in reverse time migration of vertical seismic profiles by multiple stacking estimation

Reverse time migration (RTM) has advantages of imaging steep dip structures but may inevitably produce migration swings when applied to vertical seismic profiles (VSPs). These artifacts often are not suppressed by stacking over shots. The noise is mainly generated from the limited coverage of VSP acquisition geometry, which leads to uneven superposition. The noise is processed by the imaging condition and then overlaps with the real layers, generating artificial images with different dip angles that are difficult to distinguish. The difference is that the real layers can be enhanced by the redundancy of multifold data, whereas the noise is usually generated by a smaller subset of the data. A VSP RTM processing flow is developed to suppress this type of noise. This method constructs the dip angle gather and estimates the redundancy of the coverage in the dip domain. In the stratigraphic dip domain, the false structure and the real structure are located at different dip angles. The real dip is determined by identifying the center of the stacking number. Then, the noise is suppressed by attenuating the imaging information of the other dip angles. The efficient wavefield decomposition method based on Hilbert transform-based instantaneous wavenumber method is adopted to determine the wave propagation direction. Compared with the Poynting vector method and the instantaneous wavenumber method, this approach alleviates the influence of multiwave arrivals and is suitable for complex media, ensuring the accuracy of image decomposition. The whole process is data-driven and does not require prior information. Theoretical and field examples indicate that this method can significantly reduce migration noise and improve the image quality of VSP RTM.

Near‐surface three‐dimensional multicomponent source and receiver S‐wave survey in the Tannwald Basin, Germany: Acquisition and data processing

ABSTRACTShallow 3‐D reflection seismic surveys using S‐waves have rarely been carried out, even though S‐waves can provide higher resolution subsurface images than P‐waves. We conducted a 3‐D near‐surface multicomponent source and receiver survey in Quaternary sediments. We employed a small electrodynamic seismic source with a horizontal shaking unit operated in two orientations. Three‐component geophones in an orthogonal layout covering an area of 117×99 m2 were used for recording. Changes in weather and ground conditions, including freezing and thawing during acquisition, directly influenced the data quality and resulted in discernible relative time shifts in the data. Our seismic processing flow included a four‐component rotation of the data from the Cartesian acquisition geometry into the ‘natural’ coordinate frame to orient sources and receivers in radial or transverse orientation to separate different S‐wave polarizations. The rotation increased the signal strength and helped, for example, to improve the quality of the images of the basin base. The irregular offset distribution in the common midpoint gathers impedes filtering to suppress surface waves in the f–k domain. We, therefore, applied a common‐reflection surface processing flow. After regularization, we could better remove the energy of the surface waves. Both stacked 3‐D S‐wave volumes of vertical and horizontal polarizations provide images of the Quaternary overdeepened Tannwald Basin that was partly known from previous P‐ and S‐wave 2‐D surveys. Compared to a P‐wave profile adjacent to the volume, however, the S‐wave volumes provide higher resolution images of the basin base and internal structure. The basin base is well mapped in three dimensions and shows undulations that were not obvious from the P‐wave data. Comparing the S‐wave volumes of different polarizations, we find only minor differences in the stacks and interpretations.

Downhole microseismic data interpretation for media anisotropy evaluation with limited acquisition geometry in Western Siberia

We have evaluated the results of processing and interpretation for one of the first projects in Russia for downhole microseismic monitoring of hydraulic fracturing in Western Siberia. The data are characterized by large distance to the acquisition array and noisy recordings. We illustrate that preliminary quality control of microseismic array installation is a useful tool that may predict some of the issues and help to promote better acquisition quality. Therefore, it is important to reiterate the significance of the supervision for microseismic field measurements, especially when the technology is tested in new regions. Despite limited acquisition aperture and a comparatively small number of observed microseismic events, it is possible to derive valuable interpretation results from the data set. The geometry of the event locations suggests significant unplanned development of the hydraulic fractures, including a significant shift of the fracture from the initiation point. Records with clear S-wave splitting allowed us to determine arrival times of P waves and two S waves for several microseismic events. Arrival times were then used for simultaneous kinematic inversion for microseismic-event location and anisotropic velocity-model update (layered transversely isotropic with the vertical axis of symmetry model). We estimate significant anisotropy from microseismic data. Thomsen parameters [Formula: see text] and [Formula: see text] are well constrained, whereas parameter [Formula: see text] is constrained only for one of the layers.

Solar illumination effects on the dry-season variability of spectral and spatial attributes calculated from PlanetScope data over tropical forests of the Amazon

ABSTRACT The spectral variability of tropical forests during the Amazonian dry season is not entirely understood because of the divergent responses in Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices (VIs) measured under-increased water deficit and high insolation. Here, we used a dataset composed of 493 cloud-free PlanetScope (PS) images to investigate possible effects of solar illumination on the dry-season variability of spectral and spatial attributes. The attributes were calculated from June to September over dense tropical forests of the Amazon. The dry-season images were obtained at nadir viewing between 2017 and 2019 over 12 selected sites representing different climatic and environmental conditions. To detect dry-season patterns of vegetation brightness with changes in the geometry of image acquisition, we applied principal component analysis (PCA) over the PS surface reflectance. We plotted the average surface reflectance (2017–2019) for each of the four PS bands and inspected the variability of two VIs with distinct levels of anisotropy to bidirectional effects: the Enhanced Vegetation Index (EVI) and the Normalized Difference Vegetation Index (NDVI). We also investigated the signal of textural metrics from Grey Level Co-occurrence Matrix (GLCM) obtained from the near-infrared (NIR) band of PS. Finally, we generated shade fractions from Spectral Mixture Analysis (SMA), correlated the spectral and spatial attributes of vegetation with solar angles, and observed the dry-season variability in reflectance and VIs over pseudo-invariant soil surfaces. The results showed the existence of solar illumination effects on PS image acquisition during the dry season of the Amazon, which affected differently the NDVI and EVI. From the beginning (June) to the end (September) of the dry season, the solar zenith angle (SZA) decreased and the solar azimuth angle (SAA) increased during the period of acquisition of the PS images. The amplitude of SZA between June and September increased towards south of the Amazon, while the amplitude of SAA increased towards north of this region. Changes in vegetation brightness from June to September were captured by PCA over some sites. Because of the overall increase in both red and NIR band reflectance, solar illumination effects were compensated during the NDVI calculation. In contrast, because the EVI is largely driven by changes in NIR reflectance, these effects contributed to increase the EVI signal at the end of the dry season. For most sites, GLCM texture mean increased towards the end of the dry season, while texture variance decreased in the opposite direction. Shade fractions decreased towards September when reduced amounts of canopy shadows were sensed by PS. EVI was more anisotropic than NDVI and presented higher negative correlations with SZA and shade fractions and higher positive correlations with SAA and texture mean. The dry-season increase in EVI with solar illumination effects was also observed over pseudo-invariant soil surfaces. From this unprecedent scale of observations at high spatial and temporal resolutions, we recommend caution when using anisotropic VIs for large-scale phenological studies over the Amazon because biophysical and non-biophysical signals may be coupled together.

Data-driven reflection imaging based on seismic interferometry

In conventional land seismic exploration, shallow seismic reflections with low fold are usually contaminated by noise and most conventional acquisition geometries struggle to produce sufficient near-offset fold to process shallow seismic reflections based on the velocity model. Deep seismic reflections tend to occur as weak events due to seismic wave absorption and attenuation. To enhance the shallow and deep seismic reflections, we have developed a data-driven reflection imaging method based on seismic interferometry of cross-coherence type, which first retrieves coherent reflections by cross-coherence of full wavefields with zero-offset reflections and then achieves imaging results with high quality by summing all of the crosscorrelations of full wavefields with coherent reflections in a common-midpoint gather. The novel purely data-driven method does not require any velocity information and can flatten noisy interfering reflection events with no waveform distortions. The focusing effects, which represent the stack of all the seismic reflection events, increase the fold of weak seismic reflections and further significantly improve the signal-to-noise ratio of imaging results. In comparison with data-driven reflection imaging methods based on seismic interferometry of crosscorrelation type, our results illustrate that the data-driven reflection imaging method based on seismic interferometry of cross-coherence type can automatically compress the band-limited source signature and make full use of background noise to broaden the bandwidth of reflection signals for achieving imaging results with a higher resolution.

Reflection residual statics estimated using a high-resolution neural network

Performing reflection statics corrections for improving seismic imaging is an important step in land and shallow marine data processing. The reflection residual statics solution is commonly derived from stack power maximization with hyperbolic assumptions that are valid for normal moveout (NMO) correction and stacking. We have developed a deep learning method for deriving surface-consistent reflection residual statics directly from common-shot, common-receiver, or common-midpoint gathers without velocity analysis and NMO correction. To mitigate overfitting and improve generalization ability, we augment training data by using only a few gathers but massive synthetic statics values. Because the magnitude of residual statics is often small, we apply a high-resolution neural network as a backbone to learn detailed features. We also design the head of the neural network to allow multiscale training and multiscale testing, which enables different acquisition geometries for prediction. Training with labeled samples from synthetic or real data has been tested with new real data as prediction inputs. In both cases, residual statics help improve stacked sections. If the NMO assumption is valid, our method produces results comparable to stack power maximization with much less computation time. If the NMO assumption is invalid, our method produces better results than stack power maximization.

Developing a Guideline of Unmanned Aerial Vehicle’s Acquisition Geometry for Landslide Mapping and Monitoring

Remote sensing data and techniques are widely used for monitoring and managing natural or man-made disasters, due to their timeliness and their satisfactory accuracy. A key stage in disaster research is the detailed and precise mapping of an affected area. The current work examines the relationship that may exist between the acquisition geometry of Unmanned Aerial Vehicle (UAV) campaigns and the topographic characteristics of an investigated area, toward landslide mapping and monitoring that is as accurate as possible. In fact, this work, concerning the systematic research of the acquisition geometry of UAV flights over multiple active landslides, is conducted for the first time and is focused on creating a guideline for any researcher trying to follow the UAV photogrammetric survey during landslide mapping and monitoring. In particular, UAV flights were executed over landslide areas with different characteristics (land cover, slope, etc.) and the collected data from each area were classified into three groups depending on UAV acquisition geometry, i.e., nadir imagery, oblique imagery, and an integration of nadir and oblique imagery. High-resolution orthophotos and Digital Surface Models (DSMs) emerged from the processing of the UAV imagery of each group through structure-from-motion photogrammetry (SfM). Accuracy assessment was carried out using quantitative and qualitative comparative approaches, such as root mean square error calculation, length comparison, and mean center estimation. The evaluation of the results revealed that there is a strong relationship between UAV acquisition geometry and landslide characteristics, which is evident in the accuracy of the generated photogrammetric products (orthophotos, DSMs). In addition, it was proved that the synergistic processing of nadir and oblique imagery increased overall centimeter accuracy.

A Multi-Temporal Small Baseline Interferometry Procedure Applied to Mining-Induced Deformation Monitoring

This work addresses a methodology based on the interferometric synthetic aperture radar (InSAR) applied to analyze and monitor ground-motion phenomena induced by underground mining activities in the Legnica-Glogow copper district, south-western Poland. The adopted technique employs an InSAR processing chain that exploits a stack of Sentinel-1 synthetic aperture radar (SAR) images using a small baseline multitemporal approach. Interferograms with small temporal baselines are first selected, then their network is optimized and reduced to eliminate noisy data, in order to mitigate the effect of decorrelation sources related to seasonal phenomena, i.e., snow and vegetation growth, and to the radar acquisition geometry. The atmospheric disturbance is mitigated using a spatio-temporal filter based on the nonequispaced fast Fourier transform. The estimated displacement maps and time series show the effect of both linear and impulsive ground motion and are validated against global navigation satellite system (GNSS) measurements. In this context, a significant threat to the built environment is represented by seismic tremors triggered by underground mining activities, which are analyzed using the proposed method to integrate the information gathered by in situ seismometer devices.

Quest CCS facility: Time-lapse seismic campaigns

In August 2015, CO2 injection commenced at the Quest Carbon Capture and Storage (CCS) Facility located near Fort Saskatchewan, Alberta, Canada. Quest is a fully integrated CCS operation with a capture target of just over one million metric tonnes of CO2 per year. CO2 is injected into a deep saline aquifer, the Basal Cambrian Sandstone (BCS), at a depth of about two kilometers below ground.In order to demonstrate containment and conformance of the injected CO2, a Measurement, Monitoring and Verification (MMV) plan has been implemented. Multiple domains are monitored as part of the plan, including the atmosphere, biosphere, hydrosphere, geosphere, and wells. Time-lapse seismic methods are utilized primarily for conformance monitoring and secondarily for containment monitoring of the CO2 plume within the Geosphere domain. These methods currently include 3D surface seismic (SEIS3D), 2D surface seismic (SEIS2D) and 2D multi-azimuth walk-away borehole vertical seismic profiles with distributed acoustic sensing (VSP2D).The first goal of this paper is to summarize the role of seismic, subsurface modeling efforts and the different baseline and monitor datasets that have been acquired at the Quest CCS Facility. The second goal is to describe the current Quest time-lapse strategy. Five key seismic acquisition campaigns have occurred at Quest through the pre- and post-start of injection periods, including an initial SEIS3D acquisition, a pre-injection baseline VSP2D acquisition, and three monitor VSP2D acquisitions. SEIS2D acquisitions have also been acquired as part of two of the most recent monitor campaigns. All campaigns have occurred over the winter season, to facilitate surface access, minimize impact on the land and farming/harvest activities, and to ensure maximum repeatability. Time-lapse seismic methods yield an image of the CO2 that is injected into the BCS pore space, displacing some of the brine in the saline aquifer. Since the injected CO2 is more compressible and less dense than brine, the velocity of seismic primary-waves traveling through the BCS is reduced in those places containing CO2 and remain unchanged elsewhere.Petrophysical data from the injection wells (IWs) were used to calculate the acoustic properties of the BCS. Gassmann fluid substitution was used to model the effect of CO2 saturation on the expected seismic response. Modeling showed that differences in the seismic images of the BCS obtained before and during CO2 injection should be seen in two characteristics ways:1) Travel-times across the BCS increase due to the slower primary-wave velocity inside the BCS.2) Reflections from the base of the BCS become stronger as the impedance contrast with the underlying granite basement increases. The contrast between the BCS and underlying basement is much larger than between the BCS and the overlying sediments. The large contrast between the BCS and basement produces a prominent reflection which is easily mapped during baseline and monitor surveys and is more practical for amplitude extractions.Modeling also indicated that, while the acoustic changes are sufficient to define the CO2 front qualitatively, it is unlikely that they can be used to quantify the distribution of CO2 saturations within the plume.The initial time-lapse seismic strategy called for SEIS3D to be employed after a few years of injection. Borehole distributed acoustic sensing (DAS) VSPs were proposed as a cost-effective and flexible alternative to SEIS3D while injection volumes are small. With the optical fiber cemented in place behind the casing, data can be acquired at any time without suspending injection, something not achievable with conventional VSPs that use retrievable downhole geophone arrays. The effectiveness of VSPs is inherently dependent upon the injection volume, plume offset from the IWs and acquisition geometry. After evaluating the concentric plumes predicted from dynamic modeling results and considering the acquisition logistics at the Quest CCS well sites, a VSP2D program was executed.In 2017 and 2019, SEIS2D was recorded in addition to the VSP2D surveys. Surface geophones were deployed along the VSP2D shot locations, recording a pseudo-3D or cross-spread survey. SEIS2D will be evaluated as another seismic method to potentially monitor CO2 plume development in the BCS.

Two Improved Acquisition Systems for Deep Subsurface Exploration

Present land seismic surveys mainly focus on acquiring reflection data. The maximum offset is usually 1–1.5 times the depth of targets. Limited offset results in that the acquired diving waves only penetrate the shallow parts of the Earth model, far from targets. Thus, the reflection data are used to build the deep part of the velocity model with migration velocity analysis. However, two issues challenge the success of velocity model building. First, incomplete information. Limited offsets lead to a narrow aperture of observation, which results in an under-determined inversion system. One manifestation is the trade-off between the depth of interfaces/reflectors and the average velocity above them. Second, low signal-to-noise (S/N) ratios. Complex near-surface conditions and geologic structures lead to low S/N ratios for reflection data, which fails to build velocity with reflection data. The fundamental solution to these two issues is to acquire better data with an improved acquisition system. In this work, we propose two types of modified geometries to enhance the penetration depth of the diving waves, especially the first arrivals, which can be used to build a deeper velocity model effectively. Type-I geometry adds extra sparse sources on the extension line of the normal acquisition geometry, whereas Type-II geometry deploys extra sparse receivers on the extension line. Consequently, the new acquisition system includes ultra-large offsets, which acquire diving waves from the deep subsurface. These diving waves, including waveform and first-break time, are particularly useful for recovering deeper velocity, which has paramount significance for the exploration of deep and ultra-deep hydrocarbon reservoirs. Synthetic and field data examples preliminarily demonstrate the feasibility of this improved acquisition system.