Application of vertical seismic profiling for the characterisation of hard rock

Seismic imaging in hard rock environments is gaining wider acceptance as a mineral exploration technique and as a mine-planning tool. However, the seismic images generated from hard rock targets are complex due to high rock velocities, low contrasts in elastic rock properties, fractionated geology, complicated steep dipping structures and mineralogical alterations. In order to comprehend the complexity and utilise seismic images for structural mapping and rock characterisation, it is essential to correlate these images to known geology. An ideal tool for this purpose is Vertical Seismic Profiling or VSP. The VSP method can provide not only a means to correlate seismic images to geology but also to study the properties of the transmitted seismic field as it is modified by different rock formations, the origin of the reflected events and the corresponding reflector geometry. However, the VSP technique is rarely used in hard rock environments because of the cost and operational issues related to using clamping geophones in exploration boreholes, which are 96 mm or less in diameter. Consequently the main objective of this research is to produce an efficient VSP methodology that can be readily deployed for mineral exploration.An alternative to the clamping geophone is the hydrophone. Hydrophones are suspended in, and acoustically coupled to the borehole wall through, the borehole fluid. Borehole acoustic modes known as "tube-waves" are generated by seismic body waves passing the water column and are guided in the borehole due to the high acoustic impedance contrast between the rock and fluid. Tube-waves are 1-2 orders in magnitude higher in amplitude than seismic signal and mask reflected energy in hydrophone VSP profiles. As such the use of borehole hydrophone arrays to date has been restricted to direct body wave measurements only. I have effectively mitigated tube-waves in hydrophone VSP surveys with specific acquisition methodologies and refined signal processing techniques. The success of wavefield separation of tubewaves from hydrophone data depends critically upon; having high signal to noise ratio, well sampled data, pre-conditioning of the field data and processing in the field record (FFID) domain. Improvements in data quality through the use of high viscosity drilling fluids and baffle systems have been tested and developed. The increased signal to noise ratio and suppression of tube-wave energy through these technologies greatly enhances the performance of hydrophone VSP imaging.Non-standard wavefield separation techniques successfully removed strong coherent tube-wave noise. The additional wavefield separation steps required to remove high amplitude tube-waves does degrade the overall result with some fidelity and coherency being lost. However, a direct comparison of hydrophone and borehole clamping geophone VSP surveys has been conducted in the Kambalda nickel district and the two methodologies produced comparable results. The difference was that the hydrophone data were collected in a fraction of the time compared to clamping geophone equipment with significantly less risk of equipment loss and with reduced cost.The results of these field experiments and the data processing methodology used, demonstrate the potential of hydrophone VSP surveys in the small diameter boreholes typical of hard rock exploration. Thus, these results show that hydrophone VSP is a viable, cost effective and efficient solution that should be employed more routinely in hard rock environments in order to enhance the value of the surface seismic datasets being acquired

ORCA-SPY enables killer whale sound source simulation, detection, classification and localization using an integrated deep learning-based segmentation

Acoustic identification of vocalizing individuals opens up new and deeper insights into animal communications, such as individual-/group-specific dialects, turn-taking events, and dialogs. However, establishing an association between an individual animal and its emitted signal is usually non-trivial, especially for animals underwater. Consequently, a collection of marine species-, array-, and position-specific ground truth localization data is extremely challenging, which strongly limits possibilities to evaluate localization methods beforehand or at all. This study presents ORCA-SPY, a fully-automated sound source simulation, classification and localization framework for passive killer whale (Orcinus orca) acoustic monitoring that is embedded into PAMGuard, a widely used bioacoustic software toolkit. ORCA-SPY enables array- and position-specific multichannel audio stream generation to simulate real-world ground truth killer whale localization data and provides a hybrid sound source identification approach integrating ANIMAL-SPOT, a state-of-the-art deep learning-based orca detection network, followed by downstream Time-Difference-Of-Arrival localization. ORCA-SPY was evaluated on simulated multichannel underwater audio streams including various killer whale vocalization events within a large-scale experimental setup benefiting from previous real-world fieldwork experience. Across all 58,320 embedded vocalizing killer whale events, subject to various hydrophone array geometries, call types, distances, and noise conditions responsible for a signal-to-noise ratio varying from −14.2 dB to 3 dB, a detection rate of 94.0 % was achieved with an average localization error of 7.01∘. ORCA-SPY was field-tested on Lake Stechlin in Brandenburg Germany under laboratory conditions with a focus on localization. During the field test, 3889 localization events were observed with an average error of 29.19∘ and a median error of 17.54∘. ORCA-SPY was deployed successfully during the DeepAL fieldwork 2022 expedition (DLFW22) in Northern British Columbia, with a mean average error of 20.01∘ and a median error of 11.01∘ across 503 localization events. ORCA-SPY is an open-source and publicly available software framework, which can be adapted to various recording conditions as well as animal species

Robust detection of North Atlantic right whales using deep learning methods

This thesis begins by assessing the current state of marine mammal detection, specifically investigating currently used detection platforms and approaches of detection. The recent development of autonomous platforms provides a necessity for automated processing of hydrophone recordings and suitable methods to detect marine mammals from their acoustic vocalisations. Although passive acoustic monitoring is not a novel topic, the detection of marine mammals from their vocalisations using machine learning is still in its infancy. Specifically, detection of the highly endangered North Atlantic right whale (Eubalaena glacialis) is investigated. A large variety of machine learning algorithms are developed and applied to the detection of North Atlantic right whale (NARW) vocalisations with a comparison of methods presented to discover which provides the highest detection accuracy. Convolutional neural networks are found to outperform other machine learning methods and provide the highest detection accuracy when given spectrograms of acoustic recordings for detection. Next, tests investigate the use of both audio and image based enhancements method for improving detection accuracy in noisy conditions. Log spectrogram features and log histogram equalisation features both achieve comparable detection accuracy when tested in clean (noise-free), and noisy conditions. Further work provides an investigation into deep learning denoising approaches, applying both denoising autoencoders and denoising convolutional neural networks to noisy NARW vocalisations. After initial parameter and architecture testing, a full evaluation of tests is presented to compare the denoising autoencoder and denoising convolutional neural network. Additional tests also provide a range of simulated real-world noise conditions with a variety of signal-to-noise ratios (SNRs) for evaluating denoising performance in multiple scenarios. Analysis of results found the denoising autoencoder (DAE) to outperform other methods and had increased accuracy in all conditions when testing on an underlying classifier that has been retrained on the vestigial denoised signal. Tests to evaluate the benefit of augmenting training data were carried out and discovered that augmenting training data for both the denoising autoencoder and convolutional neural network, improved performance and increased detection accuracy for a range of noise types. Furthermore, evaluation using a naturally noisy condition saw an increase in detection accuracy when using a denoising autoencoder, with augmented training and convolutional neural network classifier. This configuration was also timed and deemed capable of running multiple times faster than real-time and likely suitable for deployment on-board an autonomous system

Analysis of Multi-Component Seismic Data in the Shallow Water Environment of the Arabian Gulf

The quality of the seismic data is essential to quantitative reservoir characterization of rock properties and geological structure interpretation. Although marine multi-component seismic data hold a wealth of information about both compressional and shear velocities, the acquisition suffers from high levels of noise, which make the processing a challenging task and drastically decreases optimal value extraction. This dissertation employs a four component (4C) processing workflow via advanced time-frequency-wavenumber filtering and polarization methods to improve data quality for further interpretation and reservoir characterization. The proposed workflow is used to enhance seismic reflected energy and explore the shear wave information in the horizontal components. This study makes use of one 2D seismic line and well log dataset from one offshore in the southern Arabian Gulf (Abu Dhabi, United Arab Emirates). A combination of strong lateral seafloor heterogeneities, shallow water depths (~10m), and hard sea bottom results in highly interfering and complex wave-fields and seemingly noisy seismic data acquired in this shallow water environment. Meanwhile, we expect converted wave modes (PS-S waves) due to the strong reflector at hard sea bottom. In this work, we first propose sophisticated filtering algorithms to attenuate surface waves, and then designed advanced processing sequence combined with existing techniques for converted waves detection. Compared to body waves, surface waves are characterized by low velocity, low frequency and high polarization. First, we utilize the variable factor S transform to transform the seismic data from time domain to time-frequency-wavenumber(TFK) domain. This designed transform provides better resolution control on both time and frequency by adjusting the shape of Gaussian window function through additional parameters. Second, we estimate the impacts of residual surface waves on rotation and suppress those waves using TFK dependent polarization analysis. Polarization attributes, ellipticity and rise angle, are calculated through a developed 3D covariance matrix analysis that exploits the joint relationship of wavenumber, time, frequency and polarization. Those computed attributes are used for attenuating the surface waves and determining radial and transverse components. Third, we introduce the new 4C ocean bottom cable (OBC) processing strategy using both compressional and shear waves to recover the image of the subsurface from noisy seismic data. Comparing the time slices and gathers before and after using the strategy, it is observed that the method, described here, attenuates surface waves and remnant surface waves effectively and improves the signal to noise ratio without weakening the desired reflected signals. The results from this dissertation will find application in reservoir characterization from shear wave and converted wave analysis

Listening to Rivers: Using sound to monitor rivers

From a babbling brook to a thunderous torrent, a rivers' soundscape can be described by many onomatopoeic words. Using sound produced sub-aerially by a river to calculate its stage is an entirely novel idea, designed to be used in an environment that is seldom monitored, headwater catchments. In these environments it is difficult to use traditional methods of automatic stage gauging, such as pressure transducers and ultrasonic depth monitors. I propose a cost-effective, simple to install sound monitor which can be simply placed beside a river that is making a noise. I develop a method of how to take the tempest that is river sound and filter it to a usable component using data collected from around the North East of England during Storm Ciara and Dennis, 2020. Understanding where river sound is generated from and the mechanisms behind it are key to developing sound monitoring which is why I use an experiment at a white water course to investigate the link between sound and river topography. Using an artificial channel and obstacles I investigate the link between obstacle height and configuration on the production of sound. To use river sound as a proxy for river stage, there has to be a process of how to setup and calibrate sound. I present a method of how one may go about setting up a sound monitor and the usage it may have in water resource management. Finally, I apply the method of sound filtering, river placement, and calibration at a catchment scale to determine its validity in river monitoring. Although novel, using sound to monitor a rivers' stage is practical and deployable

Photonic Crystal Hydrogels: Simulation, Fabrication & Biomedical Application

Photonic crystal (PhC) hydrogels are a unique class of material that has tremendous promise as biomedical sensors. The underlying crystal structure allows for simple analysis of microstructural properties by assessing the diffraction pattern generated following laser illumination. The hydrogel medium provides elasticity, regenerability, and potential functionalization. Combining these two properties, photonic crystal hydrogels have the potential for sensing physical forces and chemical reagents using a low-cost, reusable platform. The development of biomedical sensors using this material is limited due to the lack of a method to accurately predict the diffraction pattern generated. To overcome this, a computational model was developed specifically for PhCs and validated against existing analytical models and an existing electromagnetic scattering model in the literature. Assessment of its accuracy in comparison to existing analytical equations and a more generalized multiparticle scattering model in the literature, CELES, found clear alignment. Another challenge is the lack of a technique to assess the specific positions of each particle in the crystal structure non-destructively. To overcome this, a novel fabrication approach was created using fluorescent particles, allowing subsequent confocal fluorescence microscopy and analyses to extract per-particle position information. This technique was used to directly compare experimental, computational, and analytical results within a single sample. To demonstrate a novel biomedical application of this material, ultrasound detection was chosen since it would be able to leverage the elastomeric structure of the PhC hydrogel as well as the ability to optically measure small changes in crystal microstructure. The sensitivity, frequency bandwidth, and limit of detection of fabricated PhC hydrogels were assessed using three ultrasound transducers. All transducers created a measurable optical response, with the limit of detection growing steadily with transducer frequency. These results provide evidence that the platform can be utilized across a variety of biomedical disciplines. For biomedical imaging, this platform can be used for all-optical non-contact ultrasound sensing. For cell and tissue engineering, this platform can provide a novel approach for characterizing and monitoring contractile cells, such as cardiomyocytes. Finally, for environmental engineering, this platform can be used as a continuous monitoring solution for dangerous toxins in environmental waterways

Electrophysiologic assessment of (central) auditory processing disorder in children with non-syndromic cleft lip and/or palate

Session 5aPP - Psychological and Physiological Acoustics: Auditory Function, Mechanisms, and Models (Poster Session)Cleft of the lip and/or palate is a common congenital craniofacial malformation worldwide, particularly non-syndromic cleft lip and/or palate (NSCL/P). Though middle ear deficits in this population have been universally noted in numerous studies, other auditory problems including inner ear deficits or cortical dysfunction are rarely reported. A higher prevalence of educational problems has been noted in children with NSCL/P compared to craniofacially normal children. These high level cognitive difficulties cannot be entirely attributed to peripheral hearing loss. Recently it has been suggested that children with NSCLP may be more prone to abnormalities in the auditory cortex. The aim of the present study was to investigate whether school age children with (NSCL/P) have a higher prevalence of indications of (central) auditory processing disorder [(C)APD] compared to normal age matched controls when assessed using auditory event-related potential (ERP) techniques. School children (6 to 15 years) with NSCL/P and normal controls with matched age and gender were recruited. Auditory ERP recordings included auditory brainstem response and late event-related potentials, including the P1-N1-P2 complex and P300 waveforms. Initial findings from the present study are presented and their implications for further research in this area —and clinical intervention—are outlined. © 2012 Acoustical Society of Americapublished_or_final_versio

Linearity tests of a multibeam echosounder

The backscatter information available from many modern multibeam echosounder systems (MBES) has been shown to be useful for a number of purposes such as habitat classification and bottom type classification. Linearity of the system response is posited to be an important requirement for many backscatter processing techniques. A procedure to measure the system linearity is developed for the Reson 7125. These measurements are performed both in a controlled test tank environment and with systems installed on operational platforms. The linearity of the system with respect to power, gain, and the returned signal level is evaluated. It is possible to drive the Reson 7125 to nonlinear behavior. The consequences of nonlinearity on both bathymetric measurements and backscatter intensity values are developed theoretically and tested against experimental observations. Nonlinear performance generally complicates and degrades both backscatter and bathymetric data products

Novel DEMON Spectra Analysis Techniques and Empirical Knowledge Based Reference Criterion for Acoustic Signal Classification

This paper presents some novel methods to estimate a vessel’s number of shafts, course, speed and classify it using the underwater acoustic noise it generates. A classification framework as well as a set of reference parameters for comparison are put forth. Identifying marine traffic in surroundings is an important task for vessels in an open sea. Vessels in vicinity can be identified using their signatures. One of the typical signatures emitted by a vessel is its acoustic measurements. The raw sonar data consisting of the acoustic signatures is generally observed manually by sonar operators for suggesting class of query vessel. The valuable information that can be extracted from the recorded acoustic signature includes shaft revolutions per minute (SRPM), number of blades (NOB), number of shafts, course and speed etc. Expert sonar operators use their empirical knowledge to estimate a vessel’s SRPM and NOB. Based on this information vessel classification is performed. Empirical knowledge comes with experience, and the manual process is prone to human error. To make the process systematic, calculation of the parameters of the received acoustic samples can be visually analyzed using Detection of Envelope Modulation on Noise (DEMON) spectra. Reported research mostly focuses on SRPM and NOB. Parameters such as number of shafts and vessel course and speed can effectively aid the vessel classification process. This paper makes three novel contributions in this area. Firstly, some novel DEMON spectra analysis techniques are proposed to estimate a water vessel’s number of shafts, speed, and relative course. Secondly, this paper presents a classification framework that uses the features extracted from DEMON spectra and compares them with a reference set. Thirdly, a novel set of reference parameters are provided that aid classification into categories of large merchant ship type 1, large merchant ship type 2, large merchant ship type 3, medium merchant ship, oiler, car carrier, cruise ship, fishing boat and fishing trawler. The proposed analysis and classification techniques were assessed through trials with 877 real acoustic signatures recorded under varying conditions of ship’s speed and sea state. The classification trials revealed a high accuracy of 94.7%