Sensor Orientation Research Articles

Orientation – invariant limb action recognition method for human-robot interaction

PurposeThis study aims to address the issue that existing methods for limb action recognition typically assume a fixed wearing orientation of inertial sensors, which is not the case in real-world human-robot interaction due to variations in how operators wear it, installation errors, and sensor movement during operation.Design/methodology/approachTo address the resulting decrease in recognition accuracy, this paper introduced a data transformation algorithm that integrated the Euclidean norm with singular value decomposition. This algorithm effectively mitigates the impact of orientation errors on data collected by inertial sensors. To further enhance recognition accuracy, this paper proposed a method for extracting features that incorporate both time-domain and time-frequency domain features, markedly improving the algorithm’s robustness. This paper used five classifiers to conduct comparative experiments on action recognition. Finally, this paper built an experimental human-robot interaction platform.FindingsThe experimental results demonstrate that the proposed method achieved an average action recognition accuracy of 96.4%, conclusively proving its effectiveness. This approach allows for the recognition of data from sensors placed in any orientation, using only training samples conducted at an orientation.Originality/valueThis study addresses the challenge of reduced accuracy in limb action recognition caused by sensor misorientation. The human-robot interaction system developed in this paper was experimentally verified to effectively and efficiently guide the industrial robot to perform tasks based on the operator’s limb actions.

JointTracker: Real-time inertial kinematic chain tracking with joint position estimation

In-field motion capture is drawing increasing attention due to the multitude of application areas, in particular for human motion capture (HMC). Plenty of research is currently invested in camera-based markerless HMC, however, with the inherent drawbacks of limited field of view and occlusions. In contrast, inertial motion capture does not suffer from occlusions, thus being a promising approach for capturing motion outside the laboratory. However, one major challenge of such methods is the necessity of spatial registration. Typically, during a predefined calibration sequence, the orientation and location of each inertial sensor are registered with respect to an underlying skeleton model. This work contributes to calibration-free inertial motion capture, as it proposes a recursive estimator for the simultaneous online estimation of all sensor poses and joint positions of a kinematic chain model like the human skeleton. The full derivation from an optimization objective is provided. The approach can directly be applied to a synchronized data stream from a body-mounted inertial sensor network. Successful evaluations are demonstrated on noisy simulated data from a three-link chain, real lower-body walking data from 25 young, healthy persons, and walking data captured from a humanoid robot.

A Robust Method for Validating Orientation Sensors Using a Robot Arm as a High-Precision Reference.

This paper presents a robust and efficient method for validating the accuracy of orientation sensors commonly used in practical applications, leveraging measurements from a commercial robotic manipulator as a high-precision reference. The key concept lies in determining the rotational transformations between the robot's base frame and the sensor's reference, as well as between the TCP (Tool Center Point) frame and the sensor frame, without requiring precise alignment. Key advantages of the proposed method include its independence from the exact measurement of rotations between the reference instrumentation and the sensor, systematic testing capabilities, and the ability to produce repeatable excitation patterns under controlled conditions. This approach enables automated, high-precision, and comparative evaluation of various orientation sensing devices in a reproducible manner. Moreover, it facilitates efficient calibration and analysis of sensor errors, such as drift, noise, and response delays under various motion conditions. The method's effectiveness is demonstrated through experimental validation of an Inertial Navigation System module and the SLAM-IMU fusion capabilities of the HTC VIVE VR headset, highlighting its versatility and reliability in addressing the challenges associated with orientation sensor validation.

Quantifying the Impact of Sustained Acceleration on Critical Care Transport Medical Equipment

ABSTRACT Introduction Military and commercial stakeholders are investing to explore the use of hypersonic aircraft and orbital spacecraft to transport cargo, medical supplies, passengers, and casualties. These vehicle platforms require periods of sustained acceleration, but to date, these dynamic forces have not been comprehensively considered in the environment of critical care patient movement because injured patients and advanced aeromedical evacuation (AE) equipment are rarely subjected to these conditions. While military AE equipment does undergo crash hazard acceleration testing, equipment functionality during or after sustained acceleration remains to be evaluated. This study was performed to fill that knowledge gap. Materials and Methods AE equipment currently used by the U.S. Air Force and Critical Care Air Transport Teams (ZOLL EMV+ 731 ventilator and ZOLL Propaq MD cardiac monitor) was subjected to low (2.5 g), moderate (4.5 g), and variable acceleration (1.5–4.5 g) for 3-minute periods at the KBR Brooks Centrifuge. AE equipment was tested for functionality in 3 different orientations (gX, gY, and gZ). Predetermined variations were made in equipment input settings to ensure each equipment item would function across mission-relevant conditions (differing ventilator tidal volumes, differing cardiac monitor arterial pressure inputs, etc.). AE was evaluated for accuracy compared to controlled inputs, alarm conditions, and equipment failure. Results The EMV+ 731 ventilator and Propaq MD cardiac monitor had no equipment failures during testing. The ventilator had clinically negligible variations in tidal volume, peak pressure, and fraction of inspired oxygen during acceleration. At the highest tidal volume tested (480 mL), the ventilator had elevated peak pressure results. However, we believe this was due to limitations in test-lung resistance and was not related to a ventilator fault. Mild effects of sensor orientation were recorded in the Propaq MD blood pressure results; for example, gX and gY differed by 5.9 ± 1.21 mmHg at 75 mmHg input (P < .01) and 6.45 ± 1.229 mmHg with 150 mmHg input (P < .001). Conclusions The EMV+ 731 ventilator and Propaq MD cardiac monitor had reliable clinical performance despite sustained acceleration. This knowledge will facilitate immediate follow-on experimentation with advanced models of combat injury during simulated medical evacuation in sustained acceleration environments.

Remote assessment of ‘Type C’ implantable satellite tag extrusion using light sensors

Advances in engineering and long‐term monitoring projects have greatly increased the sophistication of cetacean biologging methods and technology. While implantable cetacean tags naturally extrude and eventually fall off, satellite tag duration for large whales is still highly variable and often below expected longevity based on battery life alone. Causes of tag failure are difficult to determine and may include natural extrusion of the device, transmitter failure during deployment, or post‐deployment damage. Tags deployed during a study designed to assess tag performance and impacts in humpback whales (Megaptera novaeangliae) in the Gulf of Maine between 2011 and 2015 were equipped with a sensor designed to detect light exposure. Light sensor readings were evaluated based on follow‐up photos of 30 whales in order to investigate whether recorded light levels could serve as a remote indicator of tag extrusion distance. There was a direct correlation between the amount of extrusion and daytime light levels; fully embedded tags recorded no light, while tags that had extruded enough to fully expose the light sensor recorded full light levels. Partially extruded tags recorded variable light levels throughout daylight hours, likely due to irregular or partial exposure of the light sensor. While the single‐sensor design cannot describe the fine‐scale rate of tag extrusion, additional light sensors placed along the length of the tag and a visible indicator of sensor orientation would greatly improve remote diagnostics. These results show that light levels may be used as an indicator of extrusion and highlight their potential value for understanding tag performance.

Software and hardware solution for stream processing of data for сompensation of temperature drifts of LWD orientation sensor «Looch»

The Scientific-production enterprise of geophysical equipment “Looch” develops and manufactures LWD telemetry systems used in the process of drilling oil and gas wells. They include an orientation sensor (inclinometer) that estimates the position of the device in the well based on signals from three accelerometers and three magnetometers. System modules operate at high temperatures (up to 120 °C), and temperature drift compensation is required to ensure the specified orientation measurement error. This paper provides estimates of allowable drifts of sensor readings, a temperature polynomial model of accelerometers and magnetometers, and a compensation technique. An experiment was carried out, the results of which determined the suitability of the model and methodology used.

DESIGN AND FABRICATION OF SHAPE MORPHING MAGNETO-IMPEDANCE MAGNETIC SENSOR

The magnetoimpedance (MI) effect is characterized by the alteration in the impedance of soft magnetic materials when subjected to a magnetic field while a high-frequency alternating current flows through them. In this study, we engineered two distinct sensor architectures utilizing amorphous FeCSi magnetic ribbon: a single-bar structure with the dimension of 10 mm × 90 μm × 20 μm and a meander structure consisting of 13 parallel single bars. These structures were miniaturized through advanced techniques combining laser engraving and chemical etching. The magnetic analysis reveals that the meander structure exhibits a pronounced dependency on the angle θ between the magnetic field and the sensor orientation, enhancing its soft magnetic properties by up to fivefold compared to the single-bar design. This enhancement might be attributed to a reduction in the demagnetization effect and shape anisotropy energy within the meander sensor. Furthermore, the analysis of the MI effect indicates that the resonance frequency remains unaffected by external magnetic fields for both sensor types. Notably, the meander sensor demonstrates exceptional MI ratio values exceeding 82%, representing a remarkable 24-fold increase over the 3.5% observed in the single-bar sensor. Additionally, the isotropy - quantified as the MI ratio's dependence on angle θ, and magnetic field sensitivity are significantly improved in the meander configuration. These advancements in soft magnetic and physical properties are correlated to the domain structure of the sensor, particularly its transverse magnetic permeability, as evidenced by micromagnetic simulations conducted using Mumax3. With its superior MI ratio, isotropy, and heightened magnetic field sensitivity, the meander-type magnetic field sensor presents substantial potential for applications across diverse fields, ranging from biological systems to specialized practical missions.

A New Iterative Algorithm for Magnetic Motion Tracking.

Motion analysis is of great interest to a variety of applications, such as virtual and augmented reality and medical diagnostics. Hand movement tracking systems, in particular, are used as a human-machine interface. In most cases, these systems are based on optical or acceleration/angular speed sensors. These technologies are already well researched and used in commercial systems. In special applications, it can be advantageous to use magnetic sensors to supplement an existing system or even replace the existing sensors. The core of a motion tracking system is a localization unit. The relatively complex localization algorithms present a problem in magnetic systems, leading to a relatively large computational complexity. In this paper, a new approach for pose estimation of a kinematic chain is presented. The new algorithm is based on spatially rotating magnetic dipole sources. A spatial feature is extracted from the sensor signal, the dipole direction in which the maximum magnitude value is detected at the sensor. This is introduced as the "maximum vector". A relationship between this feature, the location vector (pointing from the magnetic source to the sensor position) and the sensor orientation is derived and subsequently exploited. By modelling the hand as a kinematic chain, the posture of the chain can be described in two ways: the knowledge about the magnetic correlations and the structure of the kinematic chain. Both are bundled in an iterative algorithm with very low complexity. The algorithm was implemented in a real-time framework and evaluated in a simulation and first laboratory tests. In tests without movement, it could be shown that there was no significant deviation between the simulated and estimated poses. In tests with periodic movements, an error in the range of 1° was found. Of particular interest here is the required computing power. This was evaluated in terms of the required computing operations and the required computing time. Initial analyses have shown that a computing time of 3 μs per joint is required on a personal computer. Lastly, the first laboratory tests basically prove the functionality of the proposed methodology.

Seismometer Orientation Measurements of Broadband Seismic Stations in the China Digital Seismograph Network

ABSTRACT The China Digital Seismograph Network, one of the largest national seismic networks, has been operating for over four decades which provides valuable seismic data for various scientific studies. Our investigation gathered a comprehensive dataset comprising 5,456,816 three-component waveforms from 3187 seismic events that took place over nine years (2014–2022). We assessed sensor orientations at 1056 broadband stations using the P-wave polarization method. Together with our calculation results, operation and maintenance log of regional networks, on-site checking, and manual inspection, we identified and addressed issues related to temporal changes of orientation, polarity reversal, and channel mislabeling. We found that ∼65.8% of seismometers (694) were well aligned with the absolute misorientation angle ≤3°, 20.8% of seismometers (220) were fairly well aligned with the absolute misorientation angle lying between 3° and 10°, 3.6% (38) of seismometers were misaligned exceeding 10°, and 9.8% of seismometers (104) showed a temporal variation in alignment. The fairly high consistency between our numerical results and gyrocompass measurements confirms the reliability of our investigation. We further compared the results of P-wave receiver functions analysis before and after sensor orientation correction. The findings indicate that sensor misorientation angles may lead to inaccurate and unstable seismological results. Therefore, conducting a systematic assessment, diagnosis, and correction for sensor orientation would be beneficial for advancing seismological analysis by promoting consistency, efficiency, accuracy, collaboration, reproducibility, and adaptability in data processing and interpretation.

Magnetic Railway Sleeper Detector

In an ever expanding railway network all around the world, the need for track maintenance grows steadily. Traditionally, one major part of track maintenance is ramming large vibrating steel picks into the gravel between and under railway sleepers to compress the gravel and generate a safe substructure. Even today, maintenance personnel still have to manually locate the sleepers if they cannot be detected by computer vision systems or visually by the operator. Here we developed a first of its kind magnetic sleeper detector, even able to find sleepers, buried in gravel, undetectable by vision based systems. Our approach of magnetic detection is based on a DC magnetic field excitation and a detector moving with respect to the rail system, including the sleepers and fasteners for mounting the rails. Due to railway application constraints a large air gap between the sensor and the sleeper structure is required, which significantly complicates the magnetic sensing task for robust sleeper detection. The design and optimization of the magnetic circuit was based on extensive 3D simulation studies to ensure highest possible variation in magnetic flux density at the sensor locations for absence and presence of a sleeper. Furthermore, a low noise and high sensitivity electronic circuit has been realized to cope with sensor signal offsets from unknown or changing sensor orientations with respect to the earth’s magnetic field, or magnetic interferences from other trains potentially passing by during active measurements. Since we only want to detect sleepers in close vicinity of the moving sensor system, digital signal processing of the acquired signals can easily compensate for disturbing slowly changing or static field components within real world application scenarios. We demonstrate that magnetic detection of even buried sleepers on railway tracks is possible for distances of up to 172 mm between the sensor and the sleeper. This enables an even higher level of railway maintenance automation previously impossible in certain scenarios.

Design and Development of Sensing Unit for Wire Rope Tester

In this work, a sensing unit on a Printed Circuit Board (PCB) has been designed to house hall sensors. Hall sensors are used to capture leakage flux signals from magnetized wire ropes for Non-Destructive Testing (NDT) of wire ropes. Various parameters are optimized before designing PCB, such as: Lift-off, orientation of sensor, number of hall sensors. Hall sensor signals are captured using DAQ card of NI® and program is written in LABVIEW® software. System is designed for: reducing noise in acquired signal (using RC filter), decreasing the number of channels for data acquisition system, reducing system cost and maintain signal fidelity.

Multi-Sensor Towbody: A down looking and side looking platform for Detection, Classification, & Localization of inert munitions

MuST comprises a FOCUS-3 towbody manufactured by MacArtney Underwater Technology, two sonar systems manufactured by EdgeTech, a multi-beam system from RESON, a suite of towbody orientation and motion sensors, and a shipboard handling system. The baseline Multi-Sensor Towbody (MuST) hardware and operation are described. Next, shipboard data acquisition/visualization and post mission analysis are presented. This includes examples of the data products currently being used for detection and classification. Finally, the detection/localization/classification performance is quantified using data acquired over blind test beds placed in Sequim Bay, Washington, by the Pacific Northwest National Laboratory. The test beds had 20 to 50 objects on and in the sediment within a 100 m diameter region. Both the number of objects and their positions within the region were unknown to the MuST field team. Results indicate that the hardware implementation and current software processing chain allows classification of inert munitions, with diameters at or above 81mm, with a probability of correct classification of approximately 85%. Furthermore, the difference between ground truth and MuST geolocations were 2 meters or less for over 90% of the targets detected. [Work sponsored by ESTCP.]

Signal Processing Method to Synthesize Eddy Current Sensors

Abstract This paper focuses on the development of a novel method for synthetizing an eddy current sensor using post-processing techniques based on the linear superposition of multiple parallel acquisition signals. The proposed method will be demonstrated in the context of a cross-wound sensor (CWS) made of a flexible printed circuit board (PCB). Traditionally, the configuration and detection orientation of an eddy current sensor are determined during the fabrication process, which limits the flexibility and versatility of the sensor. However, the method developed in this research enables the post-processing fabrication of the sensor, increasing the customization potential and adaptability. Furthermore, the proposed method also enables the production of a suitable lift-off sensor (LOS) for dynamic lift-off compensation (DLOC). DLOC is a crucial aspect of eddy current sensing as it helps to account for the distance between the sensor and the target surface, which can affect measurement accuracy. The ability to configure the CWS detection orientation with DLOC capabilities in post-processing offers significant advantages in terms of sensor performance and versatility. Experimental results will be presented that demonstrate the effectiveness of the proposed method in enabling post-processing fabrication of an eddy current sensor featuring configurable detection orientation and DLOC capabilities.

Sensor placement determination for a wearable device in dual-arm manipulation tasks

To enhance the quality of life for individuals in various sectors, such as industry, healthcare, and everyday activities, the development of a highly efficient and cost-effective wearable device is essential. This paper presents a feasibility study for a general estimation methodology for human activity assessment using a single sensor placed on the back. The methodology considers the minimal variability in human body movements when carrying different weights. Our approach involves optimizing the sensor's position through a simple pre-processing technique. This technique utilizes virtual sensor position and orientation data derived from a virtual mannequin based on real inertial measurement unit (IMU) measurements obtained during two-arm manipulation tasks in a warehouse setting. The primary contribution is the optimization of a single, non-invasive sensor placement on the back to accurately assess activities involving two-hand manipulation with low variability in movement. We identified 54 different activity classes with an accuracy of 91.77% using a hybrid two-dimensional Convolutional Neural Network (CNN) combined with a Bidirectional Long Short-Term Memory (BiLSTM) network (2D CNN-BiLSTM) model. This model does not require additional sensors that measure other physical quantities, such as electromyography activity. Identifying repetitive tasks involving significant bending, stretching, and twisting, which pose risks of musculoskeletal disorders and back pain, offers a solution for designing wearable devices. The portability and autonomy of these devices are crucial, and current sensor technology meets these needs with low size, cost, and consumption. Wearable medical devices can thus be effectively used for self-monitoring health, preventive medicine, and rehabilitation.

Smartphone-sensor-based human activities classification for forensics: a machine learning approach

The accurate classification of human activities in crime scenes during forensics (criminalistics) is of utmost importance in classifying suspicious and unlawful activities, easing their acceptability and interpretability by judges during legal procedures in courts or by other non-experts in the field of forensics. This paper implements machine learning (ML) algorithms: support vector machine (SVM) and decision tree (DT), to demonstrate with a high accuracy, how data emanating from smartphones’ sensors reveal and isolate relevant information about static and dynamic human activities in criminalistics. Smartphones’ data from five different sensors (accelerometer, gravity, orientation, Gyroscope and light), related to ten recurrent crime scenes activities, grouped into three classes of events (normal, felony and none-felony events) are classified by the proposed algorithms, with novelty being the classification decisions based on the entire period of the events and not instantaneous decision makings. Three independent data-subsets were made, with permutations done between them and at each time, two sets used for training and the third set used for testing. Time- and frequency-domain features were initially used separately and then combined for the model training and testing. The best average training accuracies of 100% and 97.8% were obtained for the DT and SVM, respectively, and the testing accuracies of 89.1% were obtained for both algorithms. We therefore believe that these results will serve as a solid persuasive and convincing argument to judges and non-experts of the field of forensics to accept and easily interpret computer-aided classification of suspicious activities emanating from criminalistic studies.

Adaptive Control for a Two-Axis Semi-Strapdown Stabilized Platform Based on Disturbance Transformation and LWOA-PID.

A two-axis semi-strapdown stabilized platform is a device designed to eliminate aircraft disturbances and ensure the stability of the sensor's orientation. A traditional two-axis semi-strapdown stabilization platform for aircraft can effectively control disturbance in pitch and yaw channel, but it cannot achieve ideal disturbance control in the roll channel. In order to solve this problem, an adaptive control method based on disturbance transformation and LWOA-PID is proposed. Disturbance transformation is the process of integrating the angular position disturbance of the roll from the previous moment into the combined disturbance of the pitch and yaw at the current moment. This is followed by decoupling the combined disturbance of the pitch and yaw at the current moment, thereby eliminating the disturbance caused by the roll from the previous moment. This process is repeated to achieve the goal of eliminating roll channel disturbances. To ensure the line of sight (LOS) pointing accuracy stability in the two-axis semi-strapdown stabilized platform system for aircraft, a whale optimization adaptive proportional-integral-derivative (LWOA-PID) controller based on Latin hypercube sampling is designed. It is then compared with the classical PID controller in Matlab/Simulink. The simulation results indicate that the disturbance conversion module proposed in this paper can eliminate the impact of roll axis disturbances on the LOS pointing accuracy of the two-axis semi-strapdown stabilized platform for aircraft. Compared to the classical PID controller, the LWOA-PID controller reduces tracking errors for step and sinusoidal signals by 50% and 75%, respectively. It also shortens optimization time by 37.5% compared to the WOA-PID while maintaining the same level of accuracy. Furthermore, when combined with the conversion module, the tracking error is reduced by an additional order of magnitude.

Development of an Ankle Sensor for Ground Reaction Force Measurement in Intelligent Prosthesis

In this study, a new low-cost, three-degree-of-freedom force sensor is developed to measure Ground Reaction Forces (GRFs) and to be used in the safe control of active transfemoral prosthesis. Initially, the proposed sensor was designed with the Finite Element Method (FEM). Then, the sensor's control board was developed to include an electronic circuit with its microcontroller module, four load cell amplifiers, and an orientation sensor. A test platform was also developed to conduct the sensor tests. To test the accuracy of the results obtained from the developed test platform, the same tests were also carried out with a commercial sensor and similar results were obtained, thus proving that the sensor is suitable for use in prosthetics.

Automated extrinsic calibration of solid-state frame LiDAR sensors with non-overlapping field of view for monitoring indoor stockpile storage facilities

Several industrial and commercial bulk material management applications rely on accurate, current stockpile volume estimation. Proximal imaging and LiDAR sensing modalities can be used to derive stockpile volume estimates in outdoor and indoor storage facilities. Among available imaging and LiDAR sensing modalities, the latter is more advantageous for indoor storage facilities due to its ability to capture scans under poor lighting conditions. Evaluating volumes from such sensing modalities requires the pose (i.e., position and orientation) parameters of the used sensors relative to a common reference frame. For outdoor facilities, a Global Navigation Satellite System (GNSS) combined with an Inertial Navigation System (INS) can be used to derive the sensors’ pose relative to a global reference frame. For indoor facilities, GNSS signal outages will not allow for such capability. Prior research has developed strategies for establishing the sensor position and orientation for stockpile volume estimation while relying on multi-beam spinning LiDAR units. These approaches are feasible due to the large range and Field of View (FOV) of such systems that can capture the internal surfaces of indoor storage facilities.The mechanical movement of multi-beam spinning LiDAR units together with the harsh conditions within indoor facilities (e.g., excessive humidity, wide range of temperature variation, dust, and corrosive environment in deicing salt storage facilities) limit the use of such systems. With the increasing availability of solid-state LiDAR units, there is an interest in exploring their potential for stockpile volume estimation. Despite their higher robustness to harsh conditions, solid-state LiDAR units have shorter distance measurement range and limited FOV when compared with multi-beam spinning LiDAR. This research presents a strategy for the extrinsic calibration (i.e., estimating the relative pose parameters) of installed solid-state LiDAR units inside stockpile storage facilities. The extrinsic calibration is made possible using deployed spherical targets and a complete, reference scan of the facility from another LiDAR sensing modality. The proposed research introduces strategies for: 1) automated extraction of the spherical targets; 2) automated matching of these targets in the solid-state LiDAR and reference scans using invariant relationships among them; and 3) coarse-to-fine estimation of the calibration parameters. Experimental results in several facilities have shown the feasibility of using the proposed methodology to conduct the extrinsic calibration and volume evaluation with an error percentage less than 3.5% even with occlusion percentages reaching up to 50%.

Optimization of sensor mounting position and pose in rotary kiln belt center measurement

Abstract To solve the problem that the measurement accuracy of the center of the wheel belt decreases during the adjustment of the center axis of the rotary kiln, a method of sensor installation position optimization based on dynamic simulation analysis and a simple small model is proposed and implemented in this paper. In this method, the coordinates of the laser points on the surface of the belt are obtained by using the sensor laser beam, and then the center position of the belt is obtained by fitting the coordinates of 6 laser points on the surface of the belt. Then V-rep dynamic simulation software is used to analyze the coordinates of the center position of the wheel under different sensor installation positions to find the sensor installation positions closest to the actual center of the wheel. Finally, the simulation results are verified by a simple small model. The experimental results show that this method can reduce the influence of belt deformation on the measurement of the center of the belt and find the most suitable sensor installation position. Taking the wheel with an ellipticity of 0.16% as an example, under the optimized sensor orientation, the error of the Y direction of the center of the wheel is 0.4 mm, and the error of the Z direction of the center of the wheel is 0.73 mm. By this method, the center accuracy of the existing measuring wheel is optimized from about 2 mm to less than 1 mm.

Seafloor Rayleigh ellipticity, measured from unoriented data, and its significance for passive seismic imaging in the ocean

SUMMARY Observations of the seafloor Rayleigh ellipticity contribute to seismic imaging in the ocean. To extract such observables from the arbitrarily oriented ocean–bottom seismometer (OBS) data, we develop an orthogonal-regression-based approach to measure the waveform amplitude ratios of the unoriented horizontal and vertical components. The amplitude ratios are then used to calculate the Rayleigh ellipticity (and the sensor orientation angle). The robustness of our method is verified by applications to both the unoriented OBS data and the well oriented on-land seismic data. As we propose to calculate the Rayleigh ellipticity directly from the unoriented three-component data, the measurement process avoids the complexity arising from the surface wave non-great-circle effects and uncertainties of the OBS sensor orientation angles. Overall the Rayleigh ellipticity measurements from our method are systematically higher than those by conventional analysis and show less uncertainties. Our analyses suggest that the Rayleigh ellipticity curve (14–60 s), which could be retrieved from the raw broad-band OBS data, is effective to constrain the oceanic lithosphere structure, and the accurate measurement of Rayleigh ellipticity curve is important. The potential of seafloor Rayleigh ellipticity for seismic imaging in the ocean is evidenced by a case study of the Japan Basin, the Sea of Japan. Considering the insufficient station coverage in the ocean, the single-station measurement of seafloor Rayleigh ellipticity is of significance for OBS community.