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This presentation applies open set recognition to classify unknown waveforms, enabling systems to not only identify known types but also reliably detect when waveforms fall outside the training distribution. This approach enhances robustness by avoiding forced misclassification of novel or anomalous signals.
We present a new waveform inversion technique to estimate the energy of near-surface explosions using atmospheric acoustic waves. Conventional methods often employ air blast models based on a homogeneous atmosphere, where the acoustic wave propagation effects (e.g., refraction and diffraction) are not taken into account, and therefore, their accuracy decreases with increasing source-receiver distance. In this study, three-dimensional acoustic simulations are performed with a finite difference method in realistic atmospheres and topography, and the modeled acoustic Green's functions are incorporated into the waveform inversion for the acoustic source time functions. The strength of the acoustic source is related to explosion yield based on a standard air blast model. The technique was applied to local explosions (<10 km) and provided reasonable yield estimates (<~30% error) in the presence of realistic topography and atmospheric structure. In conclusion, the presented method can be extended to explosions recorded at far distance provided proper meteorological specifications.
A photonic technique based on the Mach–Zehnder Modulator and the dual parallel Mach–Zehnder modulator is proposed for the simultaneous generation of multiple chirp and unchirp microwave waveform with frequency multiplying capability for efficient beam steering applications. There is provision to obtain a single chirp signal of a particular center frequency and chirp rate from a multiple chirp signal without any alternation in the experimental setup. In the proposed methodology, the PIN photodetector is used for the detection of 12 GHz unchirp microwave waveform showing the frequency doubling characteristics and the balance photodetector is used for the detection of multiple chirp signal with frequency components 6, 12, and 18 GHz. The same experimental setup is used to generate dual linear chirp microwave signal of 12 GHz center frequency. This can be achieved by adjusting the bias voltage of the phase modulator. Time bandwidth product and bandwidth of the signal are 18.33 and 410 MHz, respectively. This technique is tunable in frequency, but the generation of higher frequency microwave signals is limited by the speed and bandwidth of the Mach–Zehnder Modulator and photodetector. The novelty of our proposed scheme is that we are going to drive MZM by chirp RF signal to multiply the chirp rate in manifold. To the best of our knowledge the effect of chirp microwave signal on photonic highly steerable beam forming system has not yet been studied experimentally in past, which we are targeting under this paper. It is expected that the range of beam steering performance should be improved substantially as compared to previously reported results.
Two different types of data acquisition methods, “averaging mode” and “ion-counting mode”, have been used in a time-of-flight (TOF) mass spectrometry. The most common method is an averaging mode that sums waveform signals obtained from each flight cycle. While it is possible to process many ions arriving at the same TOF in one flight cycle, low-abundance ions are difficult to measure because ion signals are overwhelmed by noises from the detection system. An ion-counting mode is suitable for the detection of such low-concentration ions, but counting loss occurs when two or more ions arrive at the detector within the dead time of the acquisition system. In this study, we introduce a technique that combines two methods to measure target ions with a high concentration difference, i.e., averaging mode and ion-counting mode are used simultaneously for high abundant and trace ions, respectively. By processing waveforms concurrently during data acquisition, one can choose to analyze either or both types of data to achieve a highly quantitative mass spectrum over a wide range of sample concentrations. The result of the argon isotope analysis shows that this method provides a more accurate determination of the isotope ratio compared to averaging mode alone at one-twentieth of the analysis time required by ion-counting alone. .
Quadrupole ion traps (QITs) are versatile platforms for performing experiments with gas-phase ions due to their abilities to store ions of both polarities and to conduct MS{sup n} experiments. The QIT is particularly useful as a reaction cell for ion/ion reactions. In the case of an ion/ion reaction experiment in a QIT, multiply charged reactant ions may initially be of relatively low m/z (e.g., m/z < 1000) whereas the product ions can be one or more orders of magnitude higher in m/z (e.g., m/z > 100,000). Several factors can limit the m/z range over which an ion/ion reaction experiment can be conducted. These include (1) the efficiency of the detector, (2) the m/z range over which oppositely charged ions can be mutually stored, and (3) the m/z range over which ions can be mass selectively ejected into an external detector. High-frequency waveforms provide larger m/z trapping ranges for mutual storage of oppositely charged ions whereas low-frequency waveforms provide better trapping for very high m/z product ions. Presented here is a method that switches from a high-frequency sine wave prior to and during an ion/ion reaction to a low-frequency square wave to eject low m/z reagent ions and improves confinement of the product ions before mass-selective ejection by scanning the frequency of the square wave. This approach addresses the third issue, which is the primary limiting factor with QITs operated at high RF (e.g., > 900 MHz). .
Full-3D seismic waveform tomography (F3DT) is the latest seismic tomography technique that can assimilate broadband, multi-component seismic waveform observations into high-resolution 3D subsurface seismic structure models. The main drawback in the current F3DT implementation, in particular the scattering-integral implementation (F3DT-SI), is the high disk storage cost and the associated I/O overhead of archiving the 4D space-time wavefields of the receiver- or source-side strain tensors. The strain tensor fields are needed for computing the data sensitivity kernels, which are used for constructing the Jacobian matrix in the Gauss-Newton optimization algorithm. In this study, we have successfully integrated a lossy compression algorithm into our F3DT SI workflow to significantly reduce the disk space for storing the strain tensor fields. The compressor supports a user-specified tolerance for bounding the error, and can be integrated into our finite-difference wave-propagation simulation code used for computing the strain fields. The decompressor can be integrated into the kernel calculation code that reads the strain fields from the disk and compute the data sensitivity kernels. During the wave-propagation simulations, we compress the strain fields before writing them to the disk. To compute the data sensitivity kernels, we read the compressed strain fields from the disk and decompress them before using them in kernel calculations. Experiments using a realistic dataset in our California statewide F3DT project have shown that we can reduce the strain-field disk storage by at least an order of magnitude with acceptable loss, and also improve the overall I/O performance of the entire F3DT-SI workflow significantly. The integration of the lossy online compressor may potentially open up the possibilities of the wide adoption of F3DT-SI in routine seismic tomography practices in the near future.
The EGS Collab project acquired continuous active-source seismic monitoring (CASSM) data before, during, and after hydraulic stimulations at the first testbed at the depth of 4850 ft (1478 m) at the Sanford Underground Research Facility in Lead, South Dakota, for monitoring fracture creation and evolution. CASSM acquisition was conducted using 24 hydrophones, 18 accelerometers, and 17 piezoelectric sources within four fracture-parallel wells and two orthogonal wells. 3D anisotropic traveltime tomography and anisotropic elastic-waveform inversion of the campaign cross-borehole seismic data show that the rock within the stimulation region is a heterogeneous horizontal transverse isotropic medium. Here we use these inversion results as the initial models and apply 3D anisotropic first-arrival traveltime tomography and 3D anisotropic elastic-waveform inversion to the CASSM data acquired after each stimulation in May, 2018 and December, 2018. We observe the spatiotemporal evolution of seismic velocities and anisotropic parameters caused by hydraulic fracture stimulations, showing the regions of rock alternation caused by hydraulic fracture stimulation.
The proton light yield of organic scintillators has been measured extensively in recent years using fast waveform digitizers and large discrepancies exist in the values reported by different authors. In this letter, we address principles of digital signal processing that must be considered when conducting scintillator light yield measurements. Digitized waveform pulse height values are only proportional to the amount of scintillation light if the temporal shape of the scintillation pulse is independent of the amount of energy deposited. Finally, this is not the case for scintillation pulses resulting from fast neutron interactions in organic scintillators. Authors measuring proton light yield should therefore report pulse integral values and ensure that the integration length is long enough to capture most of the scintillation light.
Here, we investigate the feasibility of using deep learning techniques, in the form of a one-dimensional convolutional neural network (1D-CNN), for the extraction of signals from the raw waveforms produced by the individual channels of liquid argon time projection chamber (LArTPC) detectors. A minimal generic LArTPC detector model is developed to generate realistic noise and signal waveforms used to train and test the 1D-CNN, and evaluate its performance on low-level signals. We demonstrate that our approach overcomes the inherent shortcomings of traditional cut-based methods by extending sensitivity to signals with ADC values below their imposed thresholds. This approach exhibits great promise in enhancing the capabilities of future generation neutrino experiments like DUNE to carry out their low-energy neutrino physics programs.
Acoustic waveform inversions can provide estimates of volume flow rate and erupted mass, enhancing the ability to estimate volcanic emissions. Previous studies have generally assumed a simple acoustic source (monopole); however, more complex and accurate source reconstructions are possible with a combination of equivalent sources (multipole). We deployed a high–density acoustic network around Yasur volcano, Vanuatu, including acoustic sensors on a tethered aerostat that was moved every ~15–60 min. Using this unique data set we invert for the acoustic multipole source mechanism using a grid search approach for 80 events to examine volume flow rates and dipole strengths. Our method utilizes finite–difference time–domain modeling to obtain the full 3–D Green's functions that account for topography. Inversion results are compared using a monopole–only, multipole (monopole and dipole), simulations that do not include topography, and those that use a subset of sensors. We find that the monopole source is a good approximation when topography is considered. However, initial compression amplitude is not fully captured by a monopole source so source directionality cannot be ruled out. The monopole solution is stable regardless of whether a monopole–only or multipole inversion is performed. Inversions for the dipole components produce estimates consistent with observed source directionality, though these inversions are somewhat unstable given station configurations of typical deployments. Furthermore, our results suggest that infrasound waveform inversion shows promise for realistic quantitative source estimates, but additional work is necessary to fully explore inversion stability, uncertainty, and robustness.
Acoustic source inversions estimate the mass flow rate of volcanic explosions or yield of chemical explosions and provide insight into potential source directionality. However, the limitations of applying these methods to complex sources and their ability to resolve a stable solution have not been investigated in detail. We perform synthetic infrasound waveform inversions that use 3-D Green’s functions for a variety of idealized and realistic deployment scenarios using both a flat plane and Yasur volcano, Vanuatu as examples. We investigate the ability of various scenarios to retrieve the input source functions and relative amplitudes for monopole and multipole (monopole and dipole) inversions. Infrasound waveform inversions appear to be a robust method to quantify mass flow rates from simple sources (monopole) using deployments of infrasound sensors placed around a source, but care should be taken when analyzing and interpreting results from more complex acoustic sources (multipole) that have significant directional components. In the examples we consider the solution is stable for monopole inversions with a signal-to-noise ratio greater than five and the dipole component is small. For most scenarios investigated, the vertical dipole component of the multipole explosion source is poorly constrained and can impact the ability to recover the other source term components. Because multipole inversions are ill-posed for many deployments, a low residual does not necessarily mean the proper source vector has been recovered. Synthetic studies are able to help investigate the limitations and place bounds on information that may be missing using monopole and multipole inversions for potentially directional sources.
Mapping fracture networks in Enhanced Geothermal Systems (EGS) is essential for optimizing reservoir performance, yet complex fracture evolution during stimulation remains difficult to resolve. This study examines the evolution of microseismicity and fracture networks during stage 3 of the 2022 EGS stimulation at the Utah Frontier Observatory for Research in Geothermal Energy site. We map the fracture network represented by 20 clusters of seismic events identified by waveform similarities with cross-correlation. We characterize their geometric properties such as strike, dip, length, and width, and analyze the time evolution of activated fractures. The results reveal a systematic fracture evolution: early activation of pre-existing natural fractures, complex network development during peak injection, and continued activation of less favorably oriented fractures post-injection. Magnitude calibration using the Principal Component Analysis of cross-correlated waveforms improves relative amplitude measurements, refining estimations of the Gutenberg-Richter b-values with spatial variations in b-values suggesting stress re-distribution across the stimulated area. Analysis of the stress state of selected fractures further shows that fractures requiring higher excess pore pressure primarily activate at the end of injection and post-injection, highlighting stress transfer due to pore pressure as a dominant triggering mechanism. These findings provide insights into fracture propagation, stress evolution, and seismic hazard assessment in EGS reservoirs.
Abstract Three-dimensional models of Earth’s seismic structure can be used to identify temperature-dependent phenomena, including mineralogical phase and spin transformations, that are obscured in 1-D spherical averages. Full-waveform tomography maps seismic wave-speeds inside the Earth in three dimensions, at a higher resolution than classical methods. By providing absolute wave speeds (rather than perturbations) and simultaneously constraining bulk and shear wave speeds over the same frequency range, it becomes feasible to distinguish variations in temperature from changes in composition or spin state. We present a quantitative joint interpretation of bulk and shear wave speeds in the lower mantle, using a recently published full-waveform tomography model. At all depths the diversity of wave speeds cannot be explained by an isochemical mantle. Between 1000 and 2500 km depth, hypothetical mantle models containing an electronic spin crossover in ferropericlase provide a significantly better fit to the wave-speed distributions, as well as more realistic temperatures and silica contents, than models without a spin crossover. Below 2500 km, wave speed distributions are explained by an enrichment in silica towards the core-mantle boundary. This silica enrichment may represent the fractionated remains of an ancient basal magma ocean.
The use of Tailored Voltage Waveforms is a technique wherein one uses non-sinusoidal waveforms with a period equivalent to RF frequencies to excite a plasma. It has been shown to be an effective technique to decouple maximum Ion Bombardment Energy (IBE) from the ion flux at the surface of the electrodes. In this paper, we use it for the first time as a way to scan through the IBE in order to study the growth mechanism of hydrogenated microcrystalline silicon using a SiH{sub 4}/H{sub 2} chemistry. We find that at critical energies, a stepwise increase in the amorphous to microcrystalline transition thickness is observed, as detected by Real Time Spectroscopic Ellipsometry. The same energy thresholds (30 eV and 70 eV) are found to be very influential on the final surface morphology of the samples, as observed by Atomic Force Microscopy. These thresholds correspond to SiH{sub x}{sup +} bulk displacement (30 eV) and H{sub x}{sup +} (70 eV) surface displacement energies. A model is therefore proposed to account for the impact of these ions on the morphology of μc-Si:H growth.
We have demonstrated the laser ablation of PMMA using dual-color waveform synthesis of the fundamental (ω) and its second-harmonic (2ω) of a femtosecond Ti:Sapphire laser. A modest and yet clear modulation (∼22%) in ablated area versus relative phase between the 2ω and ω beams with a power-ratio of 15% (28/183 mW) is revealed. This is explained qualitatively by the dependence of ablation on multiphoton ionization of which the rate is related to the relative phase of the synthesized waveform. At higher peak power ratios, the modulation decreases rapidly, as the two-photon-ionization rate of the 2ω dominates over that of the three- to four- photon ionization of the ω beam. This technique demonstrates the feasibility of phase-controlled laser processing of materials.
We present a 17 GHz (Ku band) arbitrary waveform pulsed electron paramagnetic resonance spectrometer for experiments down to millikelvin temperatures. The spectrometer is located at room temperature, while the resonator is placed either in a room temperature magnet or inside a cryogen-free dilution refrigerator; the operating temperature range of the dilution unit is from ca. 10 mK to 8 K. This combination provides the opportunity to perform quantum control experiments on electron spins in the pure-state regime. At 0.6 T, spin echo experiments were carried out using γ-irradiated quartz glass from 1 K to 12.3 mK. With decreasing temperatures, we observed an increase in spin echo signal intensities due to increasing spin polarizations, in accordance with theoretical predictions. Through experimental data fitting, thermal spin polarization at 100 mK was estimated to be at least 99%, which was almost pure state. Next, to demonstrate the ability to create arbitrary waveform pulses, we generate a shaped pulse by superposing three Gaussian pulses of different frequencies. The resulting pulse was able to selectively and coherently excite three different spin packets simultaneously—a useful ability for analyzing multi-spin system and for controlling a multi-qubit quantum computer. By applying this pulse to the inhomogeneously broadened sample, we obtain three well-resolved excitations at 8 K, 1 K, and 14 mK.
Electric field waveforms of light carry rich information about dynamical events on a broad range of timescales. The insight that can be reached from their analysis, however, depends on the accuracy of retrieval from noisy data. In this article, we present a novel approach for waveform retrieval based on supervised deep learning. We demonstrate the performance of our model by comparison with conventional denoising approaches, including wavelet transform and Wiener filtering. The model leverages the enhanced precision obtained from the nonlinearity of deep learning. The results open a path toward an improved understanding of physical and chemical phenomena in field-resolved spectroscopy.
Field-resolved measurements of few-cycle laser waveforms allow access to ultrafast electron dynamics in light–matter interactions and are key to future lightwave electronics. Recently, sub-cycle gating based on nonlinear excitation in active pixel sensors has allowed the first single-shot measurements of mid-infrared optical fields. Extending the techniques to shorter wavelengths, however, is not feasible using silicon-based detectors with bandgaps in the near-infrared. Here, we demonstrate an all-optical sampling technique for near-infrared laser fields, wherein an intense fundamental field generates a sub-cycle gate through nonlinear excitation of a wide-bandgap crystal, in this case, ZnO, which can sample the electric field of a weak perturbing pulse. By using a crossed-beam geometry, the temporal evolution of the perturbing field is mapped onto a transverse spatial axis of the nonlinear medium, and the waveform is captured in a single measurement of the spatially resolved fluorescence emission from the crystal. The technique is demonstrated through field-resolved measurements of the field reshaping during nonlinear propagation in the ZnO detection crystal.