Pure and Applied Geophysics

Kalinowski, Martin B.; Sarid, Eli; Mialle, Pierrick; Zampolli, Mario; Haralabus, Georgios

doi: 10.1007/s00024-023-03292-0pmid: N/A

Begnaud, Michael L.; Davenport, Kathy; Conley, Andrea; Ballard, Sanford; Hipp, James; Porritt, Robert W.

doi: 10.1007/s00024-022-03155-0pmid: N/A

Location algorithms have historically relied on simple, one-dimensional (1D) velocity models for fast seismic event locations. 1D models are generally used as travel-time lookup tables, one for each seismic phase, with travel-times pre-calculated for event distance and depth. These travel-time lookup tables are extremely fast to use and this fast computational speed makes them the preferred type of velocity model for operational needs. Higher-dimensional (i.e., three-dimensional—3D) seismic velocity models are becoming readily available and provide more accurate event locations over 1D models. The computational requirements of these 3D models tend to make their operational use prohibitive. Additionally, comparing location accuracy for 3D seismic velocity models tends to be problematic, as each model is determined using different ray-tracing algorithms. Attempting to use a different algorithm than the one used to develop a model usually results in poor travel-time prediction. We demonstrate and test a framework to create first-P and first-S 3D travel-time correction surfaces using an open-source framework (PCalc + GeoTess, https://www.sandia.gov/salsa3d/software/geotess) that easily stores 3D travel-time and uncertainty data. This framework produces fast travel-time and uncertainty predictions and overcomes the ray-tracing algorithm hurdle because the lookup tables can be generated using the exact ray-tracing algorithm that is preferred for a model.

Arora, Geeta; Arora, Nimar; Le Bras, Ronan; Kushida, Noriyuki

doi: 10.1007/s00024-022-03201-xpmid: N/A

We introduce a Monte Carlo procedure for estimating the posterior location uncertainty of events produced by NET-VISA, a Physics-Based Generative Model of global scale seismology. The procedure produces a parametric estimate (confidence ellipse) of the uncertainty in location as well as the joint uncertainty in depth and time. This takes into account the uncertainty in the measurements of all of the seismic, hydroacoustic, and infrasound phases that are detected as well as those that are not detected at operational stations including the possibility that the detections were, in fact, noise. The resulting parameteric estimates are shown to be more accurate than some of the existing deployed algorithms in evaluations on a ground truth seismic dataset. An improvement is also proposed to the NET-VISA model training to take into account the inaccuracy in the current human-labeled training data. This extra uncertainty that is injected into the model leads to even better uncertainty quantification. We also demonstrate on a number of illustrative examples that NET-VISA’s generative model leads to posterior uncertainty contours that are not accurately captured by confidence ellipses.

Bregman, Y.; Radzyner, Y.; Ben Horin, Y.; Kahlon, M.; Rabin, N.

doi: 10.1007/s00024-022-03129-2pmid: N/A

Discrimination between earthquakes and explosions is an essential component of nuclear test monitoring. However, the discrimination methods currently employed by the Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO) are sometimes less effective for regional events. For instance, five seismic events whose epicenters lie near the Sea of Galilee were reported by the CTBTO in July of 2018. Those were relatively strong regional events, observed by primary stations hundreds of kilometers from the epicenter. Notably, three out of those five events were not screened out by the CTBTO as natural events. In this work, a diffusion maps-based discrimination method is configured and applied for the July 2018 Sea of Galilee seismic events. New features are introduced to the method, in order to enhance automation and computational efficiency and facilitate its use in operational settings. In the first of which, waveform segments are selected by relying on calculated arrivals rather than observed arrivals, alleviating the need for detection by a human analyst. In a further extension of the method, the low-dimensional diffusion maps representation from the training set is extended to a test set by means of geometric harmonics, relieving the need for the re-calculation of the diffusion maps coordinates for the entire data set as each new event comes in. Utilizing a network of three stations, we show that this machine learning method classifies as earthquakes all the July 2018 Sea of Galilee seismic events with durational magnitude Md > 2.3. In the context of the CTBT, the method can be used as part of an Expert Technical Analysis in order to aid the State Party concerned to identify the source of specific events.

Mathew, Shaji; MacBeth, Colin; Stevanovic, Jenny; Mangriotis, Maria-Daphne

doi: 10.1007/s00024-022-03145-2pmid: N/A

The application of an active seismic method for detecting the source location of an underground nuclear explosion (UNE) is an ongoing field of research. The objective of active seismic in On-Site Inspection (OSI) is to detect the static signatures such as the cavity created by the UNE. Along with characteristic static signatures, UNEs produce dynamic phenomena such as groundwater mounding, which gradually revert to pre-test conditions. These dynamic phenomena are observable for an extended period, even up to several decades. The magnitude of these phenomena is prominent near the source origin and results from the redistribution of residual energy, such as pressure, temperature, and saturation. These dynamic changes in sub-surface rock and fluid properties will affect the seismic property of the rock, resulting in changes of P-wave velocity. These changes can be detected by using an active seismic survey. This study highlights the potential of using time-lapse seismic to identify ground zero by monitoring post-explosion variation in the seismic signature. Time-lapse seismic, also known as 4D seismic, is a well-known technology, used in the oil and gas industry for several decades for petroleum production monitoring and management. It involves taking more than one 2D/3D survey at different calendar times over the same reservoir and studying the difference in seismic attributes. This study investigates the characteristic dynamic phenomena associated with the UNE and their impact on the emplacement rock’s seismic property. Groundwater mounding (GWM) is one of the phenomena with a high gradient of dissipation during the initial days immediately after the explosion. We look at the impact of GWM variation on seismic P-wave velocity and discuss the potential of using time-lapse seismic for its detection. The challenges of implementing time-lapse seismic, such as non-repeatability, seasonal variations and time constraints, are discussed. A frequent seismic monitoring survey method (time-lapse seismic) is proposed to monitor rock and fluid properties changes due to the post-UNE dynamic phenomena. Due to the time constraint for the OSI activity, conventional time-lapse seismic processing would not be suitable. Therefore, a machine learning-based 4D detection workflow is presented. The near-real-time 4D detection workflow using machine learning can be implemented during the OSI to identify the source location or ground zero.

Bittner, Paulina; Le Bras, Ronan; Mialle, Pierrick; Nielsen, Peter

doi: 10.1007/s00024-022-03146-1pmid: N/A

This paper focuses on events linked to controlled underwater explosions of World War 2 (WW2) ordnances which were included in the Reviewed Event Bulletin (REB). Data used for the study were provided by seismic stations of the International Monitoring System (IMS) in 2020. Examined events were triggered by devices of different charge size and took place in several locations in Europe. There were also other, previously detected WW2 ordnance underwater explosions which could be compared to events in 2020. It is shown that these relatively small underwater explosions listed in the REB, with good coupling to the ground, are located by the IMS network within 20 km from the ground truth. Charge size of explosive material was related to event magnitude. Results were compared to magnitudes published for underwater explosions of larger sizes. The conclusion is that an in-water explosion will result in seismic waves with amplitudes equivalent to the amplitudes of seismic waves from an in-ground explosion with 17.2 times the yield in kT.

Prario, Igor; Cinquini, Mariano; Marques Rojo, Rui; Gonzalez, Juan D.; Lavia, Edmundo; Bos, Patricio; Blanc, Silvia

doi: 10.1007/s00024-022-03090-0pmid: N/A

This paper presents an overview of the results obtained with different analysis techniques applied to long-range signal recordings of the acoustic event associated with the loss of the Argentinian submarine ARA San Juan that occurred on 15 November 2017 around 500 km off the coast of Chubut, Argentina. The published works including analyses of those signals have mainly considered the source geolocation. In contrast, this work is focused on the nature of the source that generated the registered signals. An inverse problem approach is followed for estimating some features of impulse-like sources through the application of spectral and cepstral analyses to a set of acoustic data associated with an unknown source. The International Monitoring System (IMS) of the Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO) detected an unusual hydroacoustic signal originating in the vicinity of the last known location of the ARA San Juan, reported as a hydroacoustic anomaly, at two of its hydrophone stations located at Ascension Island and Crozet Island, in the central Atlantic Ocean and the southern Indian Ocean, respectively. The analyses performed on the raw acoustic data provided by the CTBTO are also applied to the signals produced by an underwater explosion of a depth charge during a controlled experiment conducted by the Argentinian Navy in a nearby area on 1 December. The propagation effects along these very long-range paths (approximately 7000 km) are calculated using a range-dependent 2D underwater acoustic propagation model based on the resolution of the wide-angle parabolic equation. Results of exhaustive comparative analyses of the signals generated on 15 November and 1 December, together with comparisons between the energy spectral density of recorded and modelled signal levels, reinforce the hypothesis of at least two close and successive implosions with a time lag of 329 ms and a separation of approximately 38 m, with an azimuth to the north in accordance with the expected heading of the submarine. The values obtained are compatible with the submarine dimensions and the course over ground of the submarine towards Mar del Plata harbour. Finally, an optimization process is performed for estimating the depth of occurrence of the acoustic event related to the submarine loss.

Metz, Dirk; Obana, Koichiro; Fukao, Yoshio

doi: 10.1007/s00024-022-03117-6pmid: N/A

On 9 April 2019, an F-35A fighter jet of the Japan Air Self-Defense Force was lost offshore northern Honshu, Japan. Underwater sound phases deemed to be associated with the crash of the aircraft were recorded by a nearby seafloor observatory and the International Monitoring System hydrophone station at Wake Island. A location and origin time estimate is derived by combining the two datasets and distinctly matches the last known position of the aircraft.

Matsumoto, Hiroyuki; Zampolli, Mario; Haralabus, Georgios; Stanley, Jerry; Robertson, James; Özel, Nurcan Meral

doi: 10.1007/s00024-022-03096-8pmid: N/A

Hydroacoustic signals originating from marine volcanic activity at Kadovar Island (Papua New Guinea), recorded by the Comprehensive Nuclear-Test-Ban Treaty (CTBT) International Monitoring System (IMS) hydroacoustic (HA) station HA11 Wake Island (USA), are examined herein. Episodes of high volcanic activity were identified on two occasions, separated by a period of 1 month. The events studied pertain to an initial eruption series during a period between January and February 2018. Based on local visual observations, the Kadovar volcano began to erupt at the summit and then created a new vent spot near the coast. This series of events also included the collapse of a lava dome. Direction-of-arrival estimates for the hydroacoustic signals detected at HA11 were computed using a cross-correlation technique, which allowed for the discrimination between hydroacoustic signals originating from the Kadovar volcanic activity and numerous other hydroacoustic signals attributed to seismic activity in the Pacific Ocean. The Kadovar-related seismic signals could not be identified by regional IMS seismic stations, suggesting a submarine origin of these events. On the other hand, hydroacoustic signals originating from the Kadovar volcanic activity were identified by the seismometer at Manus Island, which is located between Kadovar and HA11. The study suggests that a series of explosive bursts followed by an unusual rumble and a broadband signal plus rumble may constrain the time of the lava dome collapse event at Kadovar Island to 00:30 UTC, 00:33 UTC, and 00:46 UTC on 09 February 2018. Given the compatibility of this observation with the tsunami generation reported by eyewitnesses on the nearby island of Blup Blup, the authors interpret this particular hydroacoustic signal as being a remote observation of this tsunamigenic event. The objective of this study was to assess the potential added value of IMS hydroacoustic data for remote surveillance of geohazards in otherwise sparsely monitored areas.

Pilger, Christoph; Hupe, Patrick; Koch, Karl

doi: 10.1007/s00024-022-03055-3pmid: N/A

The stratosphere is the atmospheric layer with the strongest impact on long-range infrasound propagation. Natural and anthropogenic infrasound signals are efficiently ducted between refraction altitudes of 30 to 60 km and reflections on the ground and are thus propagated to infrasound receivers over long distances. The direction of favorable stratospheric ducting depends on the state of the atmosphere, primarily driven by the seasonal variation of stratospheric winds. This study uses a dataset of ground-truth infrasound events over two decades and all seasons to assess the station detectability and atmospheric model performance to correctly estimate according station observations and propagation conditions. From 2000 to 2019, the German Aerospace Center facility in Lampoldshausen has conducted ignition tests of the Ariane 5 main rocket engine. Out of the 159 engine tests considered in this study, 71 were observed at the infrasound array IS26 in the Bavarian forest, located eastward at 320 km distance. Observations were mostly made during wintertime, whereas reversed stratospheric wind patterns during summertime inhibited signal detections. A significant portion of wintertime non-detections however corresponded to stratospheric profiles that should enable signal observations. Using European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric model analyses and infrasound ray tracing only two-thirds of the non-detections could be explained by the existence of a near-station acoustic shadow zone. It must thus be concluded that the applied atmospheric model is more often than expected unable to correctly explain infrasound propagation and observation at regional distances.