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Seismic experiments on future Mars exploration missions, and the instrumentation required for their performances are reported. Advances in microcomputer techniques of which have made feasible the processing of data prior to transmission to earth are discussed along with experience with the Viking hardware and software as related to future experiments. Sensor techniques are briefly mentioned.
To improve the microearthquake detection capability during the 2012 Newberry enhanced geothermal system (EGS) demonstration, we applied two advanced earthquake detection algorithms to the continuous seismic data. These techniques have the power to detect microearthquakes within the seismic noise that would otherwise be missed using traditional techniques. We compare the efficacy of a matched field processing algorithm with a multi-channel waveform correlation algorithm. Both methods identified between 60%–80% more events, with each slightly outperforming the other in different seismic noise contexts. Incorporating either template-matching technique into a seismic processing routine could therefore effectively increase the sensitivity of the seismic network without changes to the existing hardware. Additionally, lowering the magnitude of completeness would significantly add to the number of events that could subsequently inform future decision making at the reservoir level.
Probabilistic Seismic Hazard Assessments (PSHA) greatly benefit from reliable earthquake catalogs. Central Asia, while rich in seismic monitoring history, reports a fraction of their earthquake data to international data collection centres. Furthermore, since the collapse of Soviet Union until now, monitoring and data collection efforts in Central Asia have not been centralized, making monitoring and reporting uneven and disconnected in this time period. Lawrence Livermore National Laboratory (LLNL) has undertaken a multi-year effort to systematically work with national seismic monitoring and data centres and seismic networks in Kazakhstan, Kyrgyzstan, and Tajikistan to compile a unified seismological bulletin for Central Asia. In tandem with this effort, an earthquake catalog that is processed to be PSHA ready was also generated. The processing for this effort included further duplicate identification and removal, magnitude harmonization and catalog completeness analyses. This report describes the “complete” PSHA-ready earthquake catalog, including the completeness intervals that were applied.
Combined inferences from seismology, high-pressure experiment and theory, geomagnetism, fluid dynamics, and current views of terrestrial planetary evolution lead to models of the earth's core with five basic properties. These are that core formation was contemporaneous with earth accretion; the core is not in chemical equilibrium with the mantle; the outer core is a fluid iron alloy containing significant quantities of lighter elements and is probably almost adiabatic and compositionally uniform; the more iron-rich inner solid core is a consequence of partial freezing of the outer core, and the energy release from this process sustains the earth's magnetic field; and the thermodynamic properties of the core are well constrained by the application of liquid-state theory to seismic and labroatory data.
The Apollo Passive Seismic Experiment (APSE) consisted of four 3-component seismometers deployed between 1969 and 1972, that continuously recorded lunar ground motion until late 1977. The APSE data provide a unique opportunity for investigating the interior of a planet other than Earth, generating the most direct constraints on the elastic structure, and hence the thermal and compositional evolution of the Moon. Owing to the lack of far side moonquakes, past seismic models of the lunar interior were unable to constrain the lowermost 500 km of the interior. Recently, array methodologies aimed at detecting deep lunar seismic reflections found evidence for a lunar core, providing an elastic model of the deepest lunar interior consistent with geodetic parameters. Here we study the uncertainties in these models associated with the double array stacking of deep moonquakes for imaging deep reflectors in the Moon. We investigate the dependency of the array stacking results on a suite of parameters, including amplitude normalization assumptions, polarization filters, assumed velocity structure, and seismic phases that interfere with our desired target phases. These efforts are facilitated by the generation of synthetic seismograms at high frequencies (approx. 1Hz), allowing us to directly study the trade-offs between different parameters. We also investigate expected amplitudes of deep reflections relative to direct P and S arrivals, including predictions from arbitrarily oriented focal mechanisms in our synthetics. Results from separate versus combined station stacking help to establish the robustness of stacks. Synthetics for every path geometry of data were processed identically to that done with data. Different experiments were aimed at examining various processing assumptions, such as adding random noise to synthetics and mixing 3 components to some degree. The principal stacked energy peaks put forth in recent work persist, but their amplitude (which maps into reflector impedance contrast) and timing (which maps into reflector depth) depend on factors that are not well constrained -- most notably, the velocity structure of the overlying lunar interior. Thus, while evidence for the lunar core remains strong, the depths of imaged reflectors have associated uncertainties that will require new seismic data and observations to constrain. These results strongly advocate further investigations on the Moon to better resolve the interior (e.g., Selene missions), for the Moon apparently has a rich history of construction and evolution that is inextricably tied to that of Earth.
This submission contains raster and vector data for the entire state of Washington, with specific emphasis on the three geothermal play fairway sites: Mount St. Helens seismic zone (MSHSZ), Wind River valley (WRV), and Mount Baker (MB). Data are provided for 3 major geothermal models: heat, permeability, and fluid-filled fractures, and an additional infrastructure model. Both of the permeability and fluid-filled-fracture models are produced at 200 m and at 2 km depths; the heat model is only produced at the 200 m depth. Values are provided for both model favorability and model confidence. A combined model at 200m and 2 km depths is provided for favorability, confidence, and exploration risk. Raster data are provided in GeoTiff format and have a statewide coverage. Cell size is 104.355 ft; file type is unsigned 8-bit integer (0-255); 0 represents no favorability or confidence; 255 represents maximum favorability or confidence. The NAD83(HARN)/Washington South (ftUS) projection is used (EPSG:2927). Vector data are provided in shapefile or comma-delimited text file formats. Geographic coordinates, where provided, are in WGS84. A readme file accompanies each folder and provides an overview and description of the enclosed data. The heat model combines 5 intermediate raster layers (which are included in the download package): temperature gradient wells, young volcanic vents, hot springs, young intrusive volcanic rocks, and geothermometry. The permeability model combines 8 intermediate raster layers: density of mapped faults, 2D dilation tendency of mapped faults, 2D slip tendency of mapped faults, seismicity, 3D dilation tendency, 3D slip tendency, 3D maximum coulomb shear stress, and 3D slip gradients. The fluid-filled fracture model combines up to 4 intermediate rasters: resistivity from magneto-telluric 3D inversions, seismicity, Vp/Vs anomalies from passive seismic tomography, and Vs anomalies from ambient-noise tomography. A statewide infrastructure model is also provided that formalizes land-use constraints and restrictions relevant for geothermal prospecting and development. This model combines 10 intermediate rasters: areas off limits to drilling, existing or proposed geothermal leases, DNR-owned land, land-use restrictions along the Columbia River Gorge, areas inundated by water, availability of potential process water, proximity to existing roads, proximity to transmission lines, distance from urban areas, and snow-related elevation restrictions. Supporting vector data for the development of each raster layer is provided. For details on the areas of interest and modeling process please see the 'WA_State_Play_Fairway_Phase_2_Technical_Report' in the download package.
The Ecuadorian portion of the South American subduction zone presents an interesting case study in the structure and complex evolution of an upper plate. There are outstanding questions about its tectonic history, composition, and magmatic processes. While previous studies have employed ambient noise tomography to image the Ecuadorian upper plate, surface wave inversions alone often lack sensitivity at relevant shallow depths. This limitation can be overcome with an independent, complementary data set, such as gravity. We have jointly inverted Rayleigh wave phase velocities and Bouguer gravity anomalies to provide a more detailed seismic velocity model of the Ecuadorian upper plate. Our joint inversion has yielded several key improvements from previous models. First, we observe much shallower slow velocities beneath major basins (the Manabí, Progreso, and Gulf of Guayaquil), better aligning with expected basin structure. Second, we identify a high-velocity block beneath the entire forearc, corresponding to the Piñon Terrane, with velocities suggesting the presence of ultramafic material. Third, we highlight a new narrow swath of slow velocities beneath the Ecuadorian Andes, which closely follows the active volcanoes along the Eastern Cordillera. The extent of these slow velocities coincides with the termination of active arc volcanism and the predicted location of the subducted Carnegie Ridge. The predicted compositions for the mid to lower crust in the region preclude a purely compositional explanation for these velocities, suggesting that some level of partial melt is necessary.
Microseismic monitoring plays an important role in many energy-related and environmental industries.The microseismic event catalog and seismic structure of the subsurface are two of the primary outputsof the microseismic monitoring system. Though rough locations of microseismic events can be estimated automatically, obtaining high-resolution microseismic event locations requires a significant amount of human laborespecially on seismic phase picking. Unlike traditional automatic pickers that are usually less precise than human analysts, a fewrecently proposed algorithms based on deepneural networks(DNN)were able to match or surpass human performance for earthquake signals. Due to differences in the spatial scale of the study area, sensor sampling rate, and geometry of the monitoring system, it is not clear whether these deepneural networkmodels can be used to speed up microseismic data processing. In this paper, we adapted the DNN based technique for automatic phase picking of microseismic signals. We usedmicroseismic data recorded at the experiment 1 site of the enhancedgeothermal system (EGS) Collab project anddesigneda workflowthat we call transfer-learning aided double-difference tomography (TADT),thatcombines transfer learning and seismic tomography. We re-train an existing DNN with our data to obtain a newmodel using around 3500 seismogramsand associated manual phase picks. Thistransfer learnedmodel is able to reach human performance but muchfaster than human analysts. The transfer-learning-derived phase pickswereused to improve microseismic event locations and imagethesubsurface. The results are similar to or slightly better than those obtained with manual phase picks.
Seismic refraction tomography provides images of the elastic properties of subsurface materials in landslide settings. Seismic velocities are sensitive to changes in moisture content, which is a triggering factor in the initiation of many landslides. However, the application of the method to long-term monitoring of landslides is rarely used, given the challenges in undertaking repeat surveys and in handling and minimizing the errors arising from processing time-lapse surveys. This work presents a simple method and workflow for producing a reliable time-series of inverted seismic velocity models. This method is tested using data acquired during a recent, novel, long-term seismic refraction monitoring campaign at an active landslide in the UK. Potential sources of error include those arising from inaccurate and inconsistent determination of first-arrival times, inaccurate receiver positioning, and selection of inappropriate inversion starting models. At our site, a comparative analysis of variations in seismic velocity to real-world variations in topography over time shows that topographic error alone can account for changes in seismic velocity of greater than ±10% in a significant proportion (23%) of the data acquired. The seismic velocity variations arising from real material property changes at the near-surface of the landslide, linked to other sources of environmental data, are demonstrated to be of a similar magnitude. Over the monitoring period we observe subtle variations in the bulk seismic velocity of the sliding layer that are demonstrably related to variations in moisture content. This highlights the need to incorporate accurate topographic information for each time-step in the monitoring time-series. The goal of the proposed workflow is to minimize the sources of potential errors, and to preserve the changes observed by real variations in the subsurface. Following the workflow produces spatially comparable, time-lapse velocity cross-sections formulated from disparate, discretely-acquired datasets. These practical steps aim to aid the use of the seismic refraction tomography method for the long-term monitoring of landslides prone to hydrological destabilization.
We present a kinetic model based upon pore water data collected from eight sites drilled during the second Ulleung Basin gas hydrate drilling expedition (UBGH2) in 2010. Three sites were drilled at locations where acoustic chimneys were identified in seismic data, and the rest were drilled on non-chimney (i.e. background) environments. Our model, coupled a comprehensive compositional and isotopic data set, is used to illustrate the different biogeochemical processes at play in those two environments, in terms of reactions around the sulfate-methane-transition-zone (SMTZ). Organic matter decomposition is an important process for production of methane, dissolved inorganic carbon (DIC) and consumption of sulfate in the non-chimney sites, whereas anaerobic oxidation of methane (AOM) dominates both carbon and sulfur cycles in the chimney environment. Different sources of methane mediate AOM in the two settings. Internally produced methane through CO₂ reduction (CR) and methanogenesis fuels AOM in the non-chimney sites, whereas AOM is sustained by methane from external sources in the chimney sites. We also simulate the system evolution from non-chimney to chimney conditions by increasing the bottom methane supply to a non-chimney setting. We show that the higher CH₄ flux leads to a higher microbial activity of AOM, and more organic matter decomposition through methanogenesis. A higher methanogenesis rate and a smaller CR contribution relative to AOM in the chimney sites is responsible for the isotopically light DIC and heavy methane in this environment, relative to the non-chimney sites.
Enhanced geothermal systems (EGS) can enrich the Nation’s energy portfolio by increasing the amount of renewable baseload power available to the electrical grid. Microseismic activity, due to fluid resource injection and production, provides critical insight into the processes that occur within EGS subsurface fracture networks. To more precisely characterize these fracture networks, we leverage recent advances in deep neural network (DNN) image analysis techniques. Using seismic spectrogram data collected during a meso scale EGS field experiment as image inputs, we developed a new approach to correlate microseismic events. Our results indicate that our chosen DNN is able to estimate the correlation value between highly correlated events (i.e., cross-correlation coefficients greater than approximately 0.7), but has difficulty estimating correlation values between poorly correlated events. In physical systems however, the highly correlated microseismic events are the events of interest. Therefore, this data-driven method has shown potential for improving domestic Energy and Resource Security, advancing the current methods available to image subsurface EGS thermal reservoirs to enhance the availability of clean and reliable energy to the national electric grid.
Long-term seismic monitoring of carbon capture and storage projects (CCS) is needed to verify that the injected gas is safely stored in the subsurface until permanence can be assured. Conventional surface seismic monitoring techniques are usually expensive, require highly invasive surface operations, and need significant time investments on the part of personnel for both the field effort and processing the acquired data. For these reasons, permanent reservoir monitoring (PRM) technologies are preferred, as they can offer a cost-effective solution for long-term monitoring. In this context, we have developed and trialed the use of distributed acoustic sensing (DAS) coupled to permanent rotary sources, called surface orbital vibrators (SOV), with the objective to build a continuous monitoring array for CCS projects.
Image-based geological interpretation has been a labor-intensive and time-consuming process because it requires well-trained geoscientists to identify geological structures, features, and textures from various types of images. These images include scanning electron microscopic images, optical microscopic images, optical photos, resistivity images, seismic volumes, remote-sensing images, etc. With fast-evolving machine learning (ML) technology and computing power in recent decades, computers can achieve nearhuman-level to super-human-level performance with scalable high efficiency in the computer vision field. These technological revolutions facilitated image-based geological interpretation in petroleum exploration and production. For example, a fault picking method applied to 3-D seismic volume data using deep learning can achieve superior performance in comparison to conventional auto-picking methods. In addition, under the new normal of low oil prices, the petroleum industry seeks cost-effective strategies such as automating traditionally labor-intensive processes. Nevertheless, the potential of applying ML to geological image interpretation is still facing a few key challenges including data scarcity, data distribution, poor data and/or label quality, data leakage, learning algorithms, model architecture, training methodologies, testing and evaluation metrics, hyper-parameters optimization, model drift, production deployment, and the like.
Infrasound data from arrays can be used to detect, locate, and quantify a variety of natural and anthropogenic sources from local to remote distances. However, many array processing methods use a single broad frequency range to process the data, which can lead to signals of interest being missed due to the choice of frequency limits or simultaneous clutter sources. In this work, we introduce a new open-source Python code that processes infrasound array data in multiple sequential narrow frequency bands using the least-squares approach. We test our algorithm on a few examples of natural sources (volcanic eruptions, mass movements, and bolides) for a variety of array configurations. Our method reduces the need to choose frequency limits for processing, which may result in missed signals, and it is parallelized to decrease the computational burden. Improvements of our narrow-band least-squares algorithm over broad-band least-squares processing include the ability to distinguish between multiple simultaneous sources if distinct in their frequency content (e.g., microbarom or surf vs. volcanic eruption), the ability to track changes in frequency content of a signal through time, and a decreased need to fine-tune frequency limits for processing. We incorporate a measure of planarity of the wavefield across the array (sigma tau, στ) as well as the ability to utilize the robust least trimmed squares algorithm to improve signal processing and insight into array performance. Our implementation allows for more detailed characterization of infrasound signals recorded at arrays that can improve monitoring and enhance research capabilities.
This project is based on designing a small lunar probe which will conduct research relating to future manned missions to the moon. The basic design calls for two experiments to be run. The first of these experiments is an enclosed environment section which will be exposed to solar radiation while on the moon. The purpose of this experiment is to determine the effect of radiation on an enclosed environment and how different shielding materials can be used to moderate this effect. The eight compartments will have the following covering materials: glass, polarized glass, plexiglass, polyurethane, and boron impregnated versions of the polyurethane and plexiglass. The enclosed atmosphere will be sampled by a mass spectrometer to determine elemental breakdown of its primary constituents. This is needed so that an accurate atmospheric processing system can be designed for a manned mission. The second experiment is a seismic study of the moon. A small penetrating probe will be shot into the lunar surface and data will be collected onboard the lander by an electronic seismograph which will store the data in the data storage unit for retrieval and transmission once every twenty-three hours. The project is designed to last ten years with possible extended life for an additional nine years at which point power requirements prevent proper functioning of the various systems.
This slide presentation reviews the analysis of Apollo-era seismic data and indirect geophysical measurements (i.e., moment of inertia, lunar laser ranging and electromagnetic induction) and concludes that significant questions still remain. The Apollo deep moonquake seismograms using terrestrial array processing methods is analyzed to infer the structure of the lunar core. The results indicate the presence of a solid inner and fluid outer core.
The nature and dynamics of time-dependent deformation along major seismic zones including the influence of irregularities in fault geometry on the earthquake cycle, and the processes contributing to the state of stress and rates of strain in plate interior regions were studied. The principle findings of the research are discussed.
The 2011 great Tohoku-Oki earthquake, apart from shaking the ground, perturbed the motions of satellites orbiting some hundreds km away above the ground, such as GRACE, due to coseismic change in the gravity field. Significant changes in inter-satellite distance were observed after the earthquake. These unconventional satellite measurements were inverted to examine the earthquake source processes from a radically different perspective that complements the analyses of seismic and geodetic ground recordings. We found the average slip located up-dip of the hypocenter but within the lower crust, as characterized by a limited range of bulk and shear moduli. The GRACE data constrained a group of earthquake source parameters that yield increasing dip (7-16 degrees plus or minus 2 degrees) and, simultaneously, decreasing moment magnitude (9.17-9.02 plus or minus 0.04) with increasing source depth (15-24 kilometers). The GRACE solution includes the cumulative moment released over a month and demonstrates a unique view of the long-wavelength gravimetric response to all mass redistribution processes associated with the dynamic rupture and short-term postseismic mechanisms to improve our understanding of the physics of megathrusts.