System and method for non-invasive detection of subsurface hydrogen accumulation using integrated seismic and gravimetric measurements

Abstract

A method and apparatus for identifying and characterizing subterranean hydrogen gas accumulations including the deployment of passive seismic sensors to acquire surface and body wave data, and gravimetric instruments to measure spatial density variations. Optionally, this is combined with soil gas analysers to collect hydrogen (and associated gas) samples. The system integrates collected data through processing modules to produce one or more integrated tomographic models for identifying potential gas reservoir locations in a geographical region of interest. Survey design and sensor deployment are optimized according to geological targets. The integrated method enables reduction of exploratory drilling by correlating geophysical and geochemical indicators for qualitative and semi-quantitative evaluation of hydrogen reservoirs.

Description

SYSTEM AND METHOD FOR NON-INVASIVE DETECTION OF SUBSURFACE HYDROGEN ACCUMUEATION USING INTEGRATED SEISMIC AND GRAVIMETRIC MEASUREMENTSFIEED OF THE DISCEOSURE

[0001] The present disclosure relates to a non-invasive method for the detection of subterranean hydrogen gas accumulations in a geographical region of interest. The present disclosure also relates to a computerised apparatus for detecting subterranean hydrogen in a geographical region of interest.BACKGROUND

[0002] The detection and characterization of subsurface (also referred to as subterranean) hydrogen gas accumulations present significant technical and economic challenges due to its presumed scarcity within geological formations. Current exploration approaches often rely on exploratory drilling, which is costintensive, invasive, and subject to substantial uncertainty regarding reservoir presence and quality. Geophysical techniques such as passive seismic surveys and gravimetric methods have been applied independently to estimate subsurface properties, but these methods alone frequently suffer from limited resolution and ambiguous signal interpretation, particularly in complex geological settings. Likewise, soil gas measurements can offer direct geochemical insight but are constrained by spatial heterogeneity and the potential for confounding background signals, making isolated soil gas measurements insufficient for delineating potential hydrogen reservoirs. Existing solutions seldom integrate multiple data modalities and rarely employ optimized survey strategies tailored to specific geological targets. There is thus a need for an integrated, non-invasive exploration methodology that combines geophysical and geochemical measurements, enables robust spatial correlation, and improves reliability in direct hydrogen indicator identification, while reducing operational costs and the risks associated with conventional drilling-based subsurface evaluation.SUMMARY OF THE INVENTION

[0003] The present disclosure relates to a method of detecting subterranean hydrogen, the method comprising selecting a geographical region of interest and disposing a network of measurement systems on said geographical region of interest, each measurement system comprising at least a seismic sensor. Said network of measurement systems is used for recording subterranean seismic data at each of a plurality of locations on said geographical region of interest, each location corresponding to a respective measurement system, wherethe subterranean seismic data includes pressure wave (P-wave or Primary wave) and shear wave (S-wave or secondary wave) data relating to body-waves propagating underground and surface-waves. The method also comprises processing the recorded seismic data of the body waves to generate one or more tomographic models of P-wave velocity (Vp) and / or S-wave velocity (Vs). The step of processing the recorded seismic data may further comprise comparing the amplitude spectra of seismic signals recorded at sensors located at different distances from a seismic source, or by using coda waves, and measuring the amplitude decay over time and frequency, to calculate a value for the Seismic Quality Factor (Q Factor). The step of processing the recorded seismic data may further involve calculating a Horizontal-to-Vertical Spectral Ratio (HVSR) value from passive seismic data to determine resonance frequency shifts and changes in wave ellipticity due to subsurface impedance variations. The processing of the seismic data may further include deriving wave propagation attributes such as phase velocity, group velocity, and seismic signal amplification from the recorded seismic waveforms, these attributes providing additional indicators of subsurface elastic property variations and potential gas-related anomalies. The method further comprises, using at least one gravimeter, recording gravimetric data at multiple positions over said geographical region of interest, and jointly inverting the gravimetric and seismic data to produce one or more integrated tomographic models for identifying potential gas reservoir locations in the geographical region of interest. The one or more integrated tomographic models are interpreted to evaluate the potential presence of subterranean gas, such as hydrogen, in the geographical region of interest. The method of detecting subsurface hydrogen accumulations can also be used to detect other subsurface gas accumulations. These may include gases such as methane, carbon dioxide, nitrogen, helium, hydrogen sulphide, or combinations thereof.

[0004] Optionally, the step of processing the recorded seismic data may further comprise comparing P-wave velocities in different directions to detect azimuthal anomalies, which may serve as indicators of gas accumulation. Optionally, the step of processing the recorded seismic data may further comprise a forward modelling feedback loop, which comprises using density anomalies to estimate the depth of a potential reservoir and performing a forward modelling step.

[0005] The tomographic models may be two-, three-or four-dimensional. The tomographic models are generated based on the Vp and / or Vs velocity calculated from the seismic data. The tomographic models of Vp and Vs may be further combined to generate a tomographic model of the Vp / Vs ratio.

[0006] The seismic data, and gravitational field data may be collected or recorded by each seismic sensor, or gravimeter (respectively):at specified times over an extended period of time; orcontinuously over a given time period; orin response to specific events, such as microearthquakes, for example such as microearthquakes caused by events related to subterranean hydrogen formation and migration.

[0007] The tomographic models may provide a visual display of density reductions, velocity anomalies, quality factor changes, and resonance frequency shifts, which are combined together. This has the advantage of assisting with identifying gas reservoir locations in the geographical region of interest.

[0008] The method can further comprise using at least one gas analyser for sampling the presence of hydrogen at multiple points on the geographical region of interest. Alternatively, the gas analyser may be used for sampling the presence of other gases such as methane, carbon dioxide, nitrogen, helium, hydrogen sulphide, or combinations thereof. The step of interpreting said one or more tomographic models can be carried out in light of the additional data provided by gas samples collected by said at least one gas analyser.

[0009] The method can further comprise disposing a gas analyser underground at multiple points across the geographical region of interest. Preferably, the gas analyser will be disposed at least 0.8 metres underground. Optionally, each measurement system comprises a gas analyser associated with the location of that measurement system.

[0010] The step of processing the recorded seismic data may further comprise using a data fusion processor to align seismic P-wave and S-wave time series, interpolate the gravimetric data, and merge data collected from the gas analyser.

[0011] The step of disposing a network of measurement systems may further comprise calculating the optimal sensor deployment geometries by considering the surface of the geographical region of interest, the estimated depth of a potential hydrogen source, the number of sensors available, and the location of anticipated seismic sources.

[0012] The present disclosure also relates to a computerised apparatus for detecting subterranean hydrogen in a geographical region of interest. The computerised apparatus for detecting subsurface hydrogen can also be used to detect other subsurface gas accumulations, such as methane, carbon dioxide, nitrogen, helium, hydrogen sulphide, or combinations thereof. The apparatus comprises a plurality of seismic sensors disposed to form a network of measurement systems on said geographical region of interest, at least one gravimeter, and a data integration and processing module arranged to convert the data into a common format and generate a visual output. The computerised apparatus is configured to carry out the method of detecting subterranean hydrogen as discussed above.

[0013] The seismic sensors may be 3-component seismic sensors. The seismic sensors may be Micro Electrical Mechanical System based (MEMS-based), broadband sensors or seismometers, or geophones. The gravimeter may be spring-based, or Micro Electrical Mechanical System based (MEMS-based).

[0014] The network may comprise measurement systems disposed to form at least one grid on said geographical region of interest.

[0015] The network may comprise measurement systems disposed to form two or more grids on said geographical region of interest, at least two of said grids having different spacing between adjacent measurement systems.

[0016] The adjacent measurement systems may be spaced less than 1 kilometre apart from each other; optionally, adjacent measurement systems may be spaced less than 500 metres apart from each other.

[0017] The computerised apparatus may further comprise at least one instrument for soil gas measurements. The computerised apparatus may further comprise sensor nodes to integrate seismic, gravimetric, and / or soil gas measurement instruments.DESCRIPTIONS OF THE DRAWINGS

[0018] FIG. 1 is a block diagram of an integrated system for non-invasive detection and characterization of subsurface hydrogen indicators.

[0019] FIG. 2 is a flow diagram of a passive seismic acquisition unit configured to acquire ambient seismic data (including seismic data from natural and anthropic sources) across target survey areas.

[0020] FIG. 3 is a flow diagram of an adaptive survey design and field deployment method for geophysical sensing.

[0021] FIG. 4 is a schematic diagram of a multimodal data processing and analysis architecture for integrating geophysical and geochemical measurements.

[0022] FIG. 5 is an illustration of the different methods that can be used for interpreting and visualizing direct hydrogen indicators derived from integrated data.

[0023] FIG. 6 is a schematic representation of a database system for storing and managing hydrogen and soil gas measurement data.

[0024] FIG. 7 is a flow diagram of a semi-quantitative reservoir assessment method utilizing integrated geophysical and geochemical indicators.DEFINITIONS

[0025] The term “geological model” refers to a model which integrates multiple geological and geophysical datasets (e.g. field geological data, seismic data, gravity data) and interprets them to reconstruct subsurface structures, such as subsurface hydrogen systems.

[0026] In the context of the present patent specification, the term “tomographic model” is interchangeable with “velocity model”. These models are physics-based reconstructions of subsurface properties, representative of subterranean seismic wave propagation. A tomographic model may comprise one or more two- (X and Y directions) three-(X, Y and Z directions) or four-dimensional models (X, Y and Z directions, over time T) of the pressure velocity (Vp) or shear velocity (Vs) associated with the seismic waves propagating underground.

[0027] ‘ ‘X” refers to a first linear space coordinate (distance in km along the surface of the Earth, along a given direction).

[0028] “Y” refers to second linear space coordinate (distance in km along a direction perpendicular to the X direction).

[0029] “Z” refers to a third linear space coordinate (distance in km along a direction perpendicular to the X and Y directions, height or depth in km, e.g. measured in the subsurface of the Earth).

[0030] The term “network” refers to the spatial arrangement of multiple measurement systems as described herein or multiple measurement systems comprising the sensors described herein, in addition to the at least one seismic sensor, such as a passive seismic sensor, disposed at different locations in space.

[0031] In the context of the present patent specification, the term “reservoir” refers to a subsurface geological formation where natural gas accumulates, trapped by impermeable rock layers. Specifically, the term “hydrogen reservoir” refers to a large-scale underground storage system where hydrogen gas is stored in natural underground formations.

[0032] The wording “hydrogen kitchen” refers to subsurface areas where hydrogen is generated as a result of serpentinization and / or other chemical reactions, and from where the hydrogen migrates to reach hydrogen reservoirs.

[0033] The term “direct hydrogen indicator” (DH2I) in the context of the present patent specification refers to a geophysical and / or geochemical measurement that indicates the presence of subsurface accumulations of hydrogen gas within a porous reservoir, and allows for the characterization of the subsurface accumulations.

[0034] The ratio of compressional (P-wave) velocity to shear (S-wave) velocity, “(Vp / Vs)” is a geophysical parameter commonly used to characterize the properties of subsurface materials. Variations in the Vp / Vs ratio can indicate changes in lithology, fluid content, or pore pressure within a reservoir. In the context of this specification, a decreasing Vp / Vs ratio may serve as a potential direct hydrogen indicator, reflecting the presence of gas within a porous subsurface formation.

[0035] The term “seismic quality factor”, also referred to throughout the present patent specification as the “Q factor”, is a dimensionless parameter that quantifies the attenuation of the energy of seismic waves as the waves propagate through the Earth. The energy is mainly lost due to intrinsic absorption (converted to heat) and scattering (due to heterogeneities in the rock). The Q factor can be determined by comparing amplitude spectra of seismic signals at recorded at sensors located at different distances from a source, or by using coda waves (late-arriving scattered energy arriving after the main wave) and measuring amplitude decay over time and frequency.

[0036] The term “Horizontal -to-Vertical Spectral ratio” (HVSR) refers to a geophysical method that compares the amplitude spectrum of horizontal ground motion to that of vertical ground motion, typically from ambient seismic noise. It is used to estimate resonance frequencies and site effects, helping to characterize subsurface layers, such as sediment thickness or impedance contrasts due to gas accumulation.

[0037] The term “adaptive survey design” refers to a method in survey research where design decisions (the geometry of the survey, the spacing between seismic sensors) are modified in real-time or in phases during the data collection process, based on incoming data.

[0038] In the context of the present patent specification, the wording “interpretation routines” refers to sets of computational or manual processes used to analyse geophysical data in order to identify meaningful patterns, anomalies, or signals useful to the detection of direct hydrogen indicators. In the case of the present patent specification, a decrease in Vp and / or Vp / Vs ratio, low density anomaly, low qualify factor, and low frequency of the HVSR curve will be targeted.

[0039] The term “fusion” in the context of the present patent specification relates to the fusion of datasets, more specifically referring to the process of combining data from multiple sources (passive seismic, gravimetric, and soil gas measurements) into a single, coherent dataset that can be interpreted by a user. The fusion aligns and integrates the different types of data to enhance detection confidence and reduce uncertainty, and is achieved by: ensuring that all datasets refer to the same locations and time periods, normalizing the data to allow meaningful comparison, and visually overlying the anomalies for interpretation.

[0040] The term “phase velocity” in the context of the present patent specification relates to the propagation velocity of a specific phase of a seismic wave, such as a constant phase point (e.g., a crest or trough), as it travels through a geological medium. Phase velocity characterizes how the phase of a wave propagates in space and is determined by the physical properties of the subsurface, including elastic parameters, density, and fluid content. Variations in phase velocity may therefore be used to infer changes in geological formations.

[0041] The term “group velocity” in the context of the present patent specification relates to the propagation velocity of the envelope or energy packet of a seismic wave as it travels through a geological medium. Group velocity characterizes the rate at which seismic energy or wave packets propagate and is generally associated with the transport of energy and information within the wavefield. Group velocity depends on the physical and elastic properties of the subsurface, including lithology, density, and fluid content, and may therefore be used to identify variations in geological formations.

[0042] The term “seismic signal amplification” in the context of the present patent specification relates to an increase in the amplitude or energy of a recorded seismic signal relative to a reference level, resulting from physical processes occurring during wave propagation through the subsurface or due to local geological conditions. Such amplification may arise from resonance effects, impedance contrasts, scattering, or other wave-medium interactions within geological structures. Variations in seismic signal amplification may therefore provide information about subsurface properties and may be used to detect accumulation of fluids such as hydrogen.

[0043] In the present patent specification, the term “alignment” of data means to ensure that all datasets refer to the same locations and time periods, and to correct them if needed from time and spatial drift.

[0044] In the context of the present patent specification, the wording “wireless mesh” relates to a type of communication network where multiple devices connect to each other wirelessly and relay data across the network, while consuming minimal energy. These are especially useful in environments where wiring is impractical and power availability is limited.

[0045] In the context of the present patent specification, the term “inverted density modeler” refers to a tool that creates detailed underground density maps by analysing gravity data. It helps visualize how dense different rock layers are and highlights areas with low density that may contain gas, aiding in the interpretation of subsurface structures.

[0046] In the context of the present patent specification, the term “ruggedised” refers to equipment that is specially built to withstand harsh environments. This equipment is provided with tough enclosures, and designed for shock and vibration resistance, temperature tolerance, dust and water resistance, and power stability.

[0047] In the context of the present patent specification, the term “Bouguer anomalies” refers to the difference between the measured gravitational acceleration at a specific location on Earth and the theoretical gravitational acceleration expected after correcting for elevation, latitude, and the gravitational effect of the intervening rock mass between the measurement point and sea level. It represents subsurface variations in rock density and is commonly used in geophysical exploration to infer geological structures.

[0048] In the context of the present patent specification, the term “continuous” when referring to data collection by the seismic sensor or gravimeters refers to continuous collection of data over a given time period, rather than in response to specific events.

[0049] In the context of the present patent specification, the term “aperture” refers to the maximum dimension (span) of the seismic network or the distance between the two most widely separated sensors.DETAILED DESCRIPTION

[0050] Disclosed herein are methods and related systems for detecting subsurface hydrogen, and more specifically for detecting characteristics of subsurface hydrogen systems based on the production of tomographic models of the subsurface. Examples of the present disclosure will be described more fully hereinafter with reference to the accompanying drawings in which like numerals represent like elements throughout the figures. The invention should not be construed as being limited to the examples set forth herein.

[0051] The method and apparatus of the present patent specification, relating to the detection and characterization of subsurface hydrogen described herein further comprises an analysis of available geological and geophysical data to evaluate the potential for hydrogen exploration in a chosen target geographical region on the Earth’s surface. The method and apparatus for detecting subsurface hydrogen can also be used to detect other subsurface gas accumulations. These may include gases such as methane, carbon dioxide, nitrogen, helium, hydrogen sulphide, or combinations thereof. Potential sources of geological and geophysical data that may provide indication of the presence of subsurface gaseous hydrogen include surface-wave seismic records, bodywave seismic records, other seismic data, local gravitational field data, and soil gas sampling data.

[0052] Gravimetric anomalies (that is, local changes in the gravity field) identified at low spatial resolutions may provide a first indication of the presence of mantle rocks underground. Gravimetric anomalies are leveraged in the initial step of identifying the initial target geographical region, considered as a geographically favourable area. On a geographically favourable area, seismic sensors may be deployed for continuing in the process of detecting subsurface hydrogen.

[0053] Typical geographical regions of interest may measure in the range of 1 to 50 km2. These dimensions are however to be considered as being variable and will need to be selected as a function of a geological model, for example so that an entire reservoir system may be captured.

[0054] The design of the network of measurement points is a function of the location of the seismic sources and the coverage area needed to image the entire suspected hydrogen reservoir and the spacing of the sensors required to increase or even maximise the resolution of the datasets. The aperture of the network and the spacing of the sensors is also defined by the depth of the target objects. Typically, a network with 5 km aperture and a sensor spacing of about 500 m or less can be used for reservoir targets are in the 0 to 5 km range. In the method of the present patent specification, the sensor spacing reconfiguration may be calculated, and may be adjusted in response to terrain reality.

[0055] Typically, reservoir targets (0 to 5 km) require a sensor spacing of about 500 m. Based on previous geophysical data (anomalous Vp / Vs ratios and gravimetric lows), zones that require high-resolution surveying may be identified. The sensor deployment planner will then recalculate the sensor array configuration on the selected area with a spacing in the range of about 50m-500m, as requested by a user.

[0056] In addition, seismic waves are used to provide indications of geological formations present as well as the presence of potential hydrogen accumulations. Seismic waves may comprise body waves and surface waves. Surface waves are waves that travel along the Earth’s surface. Body waves comprise of waves that travel through the Earth’s body. Both are controlled by the materials through which they pass and diminish as they get further away from the source. Both of these types of waves may be utilised in the methods described herein for detecting subsurface hydrogen. Body waves may be pressure (P) waves, or shear (S) waves. The velocities of P- and S-waves depend on the density of the materials through which they propagate, and therefore will differ depending on what subsurface material (type of rock formation and presence of gas accumulation) they travel through.

[0057] By measuring seismic waves as described herein at different locations on a target region for hydrogen exploration using the seismic sensors described herein, differences in the velocities of propagation of the seismic waves recorded can be compared and utilised in the detection and characterization of subsurface (also referred to as subterranean) gas accumulations.

[0058] Where low Vp and abnormal Vp / Vs ratios have been detected, these can be utilised to identify potential direct hydrogen indicators in reservoirs at depth. This analysis exploits the fact that seismic waves propagate differently in gas and water-saturated reservoirs. In this context, a low Vp would correspond to a reduction of about 20-35% relative to the background brine-saturated velocity of the reservoir, more preferably a reduction greater than ~30%. In this context, an abnormal Vp / Vs ratio would be less than 1.7, more preferably, a valueless than 1.5. The geometries of the gas accumulation can be used to ultimately evaluate the quantity and nature of gas trapped.

[0059] Further, the gravity data can highlight anomalies in the density readings which could also serve as indicator of subsurface gas accumulations.

[0060] The step of processing the recorded seismic data may further include a step of comparing P-wave velocities measured at different azimuths and offsets to detect azimuthal anomalies. In an anisotropic medium, P-wave velocities vary with azimuth. The presence of gas may reduce the P-wave velocity and thereby modify the azimuthal velocity variation. Such anomalies may be indicative of a gas accumulation.

[0061] In addition, the the step of processing the recorded seismic data may include a forward modelling feedback loop. Once density anomalies are identified, and the depth of the potential reservoir can be estimated (from geological interpretation and tomography results), a forward modelling step can be performed. The forward modelling step tests whether the presence of a gas accumulation explains the wavelength and magnitude of the gravimetric anomaly observed at the surface. This can be introduced as an optional feedback loop to validate the results.

[0062] Additionally, by comparing the frequency content of horizontal and vertical ground motion recorded by a seismic sensor, typically during ambient (natural or human-made) vibrations, Horizontal-to-Vertical Spectral Ratios are calculated. The horizontal-to-vertical spectral ratio may be used to characterize subsurface layers as a result of gas accumulation. The horizontal-to-vertical spectral ratio may be calculated by recording ambient seismic noise with three-component seismometers, transforming the recorded signal from the time domain to the frequency domain using a Fast Fourier Transform (FFT), obtaining the amplitude spectra for the horizontal and vertical components and calculating the ratio of the horizontal and vertical amplitude spectra as a function of frequency in each direction.

[0063] Figure 1 illustrates an example of a direct hydrogen indicator detection system 100. The direct hydrogen indicator detection system 100 may be implemented as a mobile field instrumentation platform, a permanently installed geophysical network, or a modular exploration workstation, among other configurations. The primary function of the direct hydrogen indicator detection system 100 is to enable integrated acquisition, processing, and interpretation of geophysical and geochemical data at the surface and near-surface, with the specific objective of detecting and characterizing subsurface accumulations of gas within a porous reservoir of all nature, including hydrogen. The direct hydrogen indicator detection system 100 may comprise an interconnected suite of data acquisition devices, data handling modules, and interpretation tools which together facilitate the non-invasive identification of direct hydrogen indicators. Specifically, the direct hydrogen indicator detection system 100 may include a passive seismic acquisition unit (102), a gravimetric acquisitionunit (104), a soil gas measurement unit (106), a data integration and processing module (108), and a direct hydrogen indicator interpretation and visualisation module (110).

[0064] In this example, a passive seismic acquisition unit 102 can be connected to the direct hydrogen indicator detection system 100 and configured to acquire ambient seismic data (including natural and anthropic sources) across target survey areas as determined by system 100. The passive seismic acquisition unit 102 may comprise, for example, a broadband seismometer array deployed at multiple surface points and / or geophones grid, or a battery-powered micro-electromechanical system (MEMS) seismic sensor grid. The passive seismic acquisition unit 102 may produce digital time series records of surface and body wave activity which are subsequently made available within the direct hydrogen indicator detection system 100.

[0065] In this example, a gravimetric acquisition unit 104 is incorporated within the direct hydrogen indicator detection system 100 and configured to obtain measurements of spatial gravity field variations across the assigned survey area. The gravimetric acquisition unit 104 may include, as examples, a portable, relative or absolute gravimeter for field use or a stationary gravimetric sensor positioned for long-duration measurements. The gravimeter may be spring-based. The gravimeter may be Micro Electrical Mechanical System based (MEMS-based). The gravimetric sensor may be positioned for long-duration measurements for monitoring density variation in a reservoir and / or monitoring fluid migration. The gravimetric acquisition unit 104 can generate datasets that quantify gravitational accelerations at each measurement site as referenced by the direct hydrogen indicator detection system 100.

[0066] Soil gas measurements may be carried out after geological structures linking the reservoir to the surface are identified. Gas leaks are typically detected around the surface leakage point. In this example, a soil gas measurement unit 106 is also integrated into the direct hydrogen indicator detection system 100 and configured to collect, analyse, and log soil gas samples at designated locations specified by system 100. The soil gas measurement unit 106 may incorporate, for example, a portable gas chromatograph or analyser for on-site measurement of hydrogen and methane concentrations, or a tunable diode laser absorption spectrometer used for real-time geochemical profiling. The soil gas measurement unit 106 may incorporate gas sensors for continuous hydrogen measurements, for monitoring purposes. The hydrogen measurements can be taken over several months, for example over more than 6 months, for long-term monitoring. The soil gas measurement unit 106 may provide chemical composition records to the direct hydrogen indicator detection system 100. The soil gas measurement unit could also be configured to collect, analyse and log soil gas samples to detect the presence of other gases, such as methane, carbon dioxide, nitrogen, helium, hydrogen sulphide, or combinations thereof.

[0067] In this example, a data integration and processing module 108 is operatively connected to the direct hydrogen indicator detection system 100 and may serve to coordinate the ingestion, synchronization, and fusion of all datasets acquired by the passive seismic acquisition unit 102, the gravimetric acquisition unit 104, and the soil gas measurement unit 106. Once the data integration and processing module 108 has collected raw data from multiple sources (seismic, gravimetric, soil gas), the data integration and processing module 108 may clean the data (for example, removes noise, corrects errors), aligns the data temporally and spatially, and converts the different data formats into a common structure which is suitable for analysis. The data integration and processing module 108 can then apply algorithms (for example, statistical modelling, signal processing) to extract key geophysical features (for example, anomalies, gradients, signal spikes) from the data in order to generate an output in a form of structured geospatial layers (maps or 3D grids), CVS files, or database tables. The processed datasets can then be delivered to interpretation routines.

[0068] The data integration and processing module 108 may take the form of a centralized computing workstation with multimodal analytics software, or a cloud-hosted geophysical data platform. The data integration and processing module 108 may deliver harmonized and processed datasets to downstream interpretation routines within the direct hydrogen indicator detection system 100.

[0069] A direct hydrogen indicator interpretation and visualization module 110 forms part of the direct hydrogen indicator detection system 100. The direct hydrogen indicator interpretation and visualization module 110 is configured to receive output from the data integration and processing module 108 and to generate graphical or tabular representations of direct hydrogen indicators derived from the input data. The direct hydrogen indicator interpretation and visualization module 110 may include a scientific visualization software suite executing on a control workstation, or a web-based dashboard render engine. The direct hydrogen indicator interpretation and visualization module 110 may provide final interpretation tools and output views to support technical decision making and reporting requirements associated with the direct hydrogen indicator detection system 100.

[0070] In some examples, the direct hydrogen indicator detection system 100 may be solar powered. In some examples, the direct hydrogen indicator detection system 100 may be deployed as a compact, mobile exploration unit for delineating hydrogen anomalies in a targeted area. For example, the passive seismic acquisition unit 102 may consist of a 64-node array of MEMS-based wireless gravimeters deployed in a quasiradial layout across a survey grid, capturing ambient body wave and surface wave data with sub-millisecond synchronization accuracy. A seismic vibrator may be used to create augmented passive seismic waves for additional sources for seismic acquisition. The survey grid area may be in the range of from about 1 km2to 50 km2, more preferably in the range of 2km2to 10km2. As an example, the area of the survey grid may be about 7km2. The array should cover the surface are above the location where gas accumulation is expected and itsaperture should be sufficient to resolve subsurface structures down to the expected depth of the targeted reservoir. The gravimetric acquisition unit 104 may comprise two precision MEMS gravimeters mounted in shock-isolated enclosures on tracked survey vehicles, or a quantum gravimeter operational on a mobile vehicle executing grid-aligned stops and relaying measurements via encrypted short-range telemetry. The mobile vehicle may execute grid-aligned stops at regular intervals, which may be in the range of between about 50 m and about 500 m apart. For example, the grid aligned stops may be at 200 m intervals, or 100 m intervals. Gravimetric sensors may be positioned for long-duration measurement to detect density anomaly shifts related to fluid migration. Simultaneously, the soil gas measurement unit 106 may conduct continuous hydrogen and methane sampling using a dual-chamber gas chromatograph installed in a ruggedised trailer, collecting gas from shallow auger holes drilled near previously identified geological structure reaching the reservoir and that can be used as the surface end of the fluid migration pathways. All datasets may be relayed to the data integration and processing module 108, implemented as a hardened field computing workstation equipped with GPU-accelerated multimodal fusion software. This module may align the seismic, gravimetric, and gas concentration data spatially and temporally, correcting for acquisition time offsets and environmental drift. After the data has been combined and processed by the processing module 108, the direct hydrogen indicator interpretation and visualization module 110 may render on-demand heatmaps of Vp / Vs ratio depressions, Q factor anomalies, and coincident hydrogen concentration zones, overlaying these results with gravity minima contours and subsurface density models. Survey personnel may interactively annotate target zones and adjust deployment geometry in real time using the module’s visual feedback, enabling immediate refinement of the sensor grid to concentrate sampling in areas of highest direct hydrogen indicator confidence. At the conclusion of each field day, the system state may reflect new time-tagged seismic and gravimetric datasets, updated gas concentration records, harmonized cross-modal anomaly volumes, and field-validated direct hydrogen indicator visualizations guiding the next phase of deployment.

[0071] In other examples, the direct hydrogen indicator detection system 100 may be configured as a distributed semi-permanent sensing array deployed above a previously identified location. As another example, the passive seismic acquisition unit 102 may consist of thirty-two buried broadband sensors installed along a 5-kilometer structural transect, each equipped with autonomous data loggers and synchronized to GPS -disciplined timing modules. These passive seismic acquisition units 102 may continuously record ambient body and surface wave signals and microtremors, transmitting buffered waveforms at hourly intervals to a regional control station. The gravimetric acquisition unit 104 may employ low-drift gravimeters housed in weather-shielded concrete pedestals, recording diurnal gravity values at each transect node and relaying time-stamped readings to the system over a low-power wireless mesh. Low-power wireless meshes may transmit power in a range of from about 5 mW to about 120 mW, depending on the range and technology.

[0072] The soil gas measurement unit 106 may include a network of shallow subsurface sampling ports located next to previously identified fluid leaking point, each outfitted with a low-maintenance diode laser absorption spectrometer programmed to collect hydrogen concentration data every 12 hours. These measurements, along with metadata on soil temperature and barometric pressure, may be logged and transmitted to the data integration and processing module 108, realized in this example as a remote cloud server interfaced through a secure cellular gateway. The data integration and processing module 108 may execute rolling harmonization and anomaly -detection routines, registering newly received geophysical and geochemical measurements into a composite time-series model. Upon each update cycle, the data may be processed with the integration and processing module 108, and the direct hydrogen indicator interpretation and visualization module 110 may auto-generate time-lapse visualizations of Vp / Vs ratio trends, density anomaly shifts, Q factor variation maps, and hydrogen concentration changes, flagging areas that exhibit co-evolving signals consistent with episodic migration of hydrogen-rich fluids. Researchers may access these visualizations via an interactive dashboard to identify emergent direct hydrogen indicator clusters or initiate targeted field investigations. Over successive months, the system may accumulate a temporally indexed library of integrated multi-modal measurements, enabling detection of slow-onset geochemical transients and the construction of a regional baseline model for hydrogen occurrence and migration dynamics.

[0073] In further examples, the direct hydrogen indicator detection system 100 may be deployed during the initial screening phase of a frontier hydrogen exploration campaign across a structurally ambiguous geological setting. The passive seismic acquisition unit 102 may be fielded as a lightweight deployment of twenty MEMS seismic pods arranged in a staggered cross pattern spanning 3 to 5 kilometres, capturing both ambient surface waves and seismic body waves for initial velocity profiling. The gravimetric acquisition unit 104 may be implemented using a rover-mounted gravimeter performing dense transects across areas of potential gas accumulation, acquiring relative gravity data at 100-meter intervals along each line. At selected central points, the soil gas measurement unit 106 may conduct hydrogen and methane sampling via handheld laser spectrometers inserted into hand-augered boreholes, focusing on zones with anomalous vegetation, microfracturing, or low resistivity from satellite data. All recorded signals may be uploaded daily to a portable server implementing the data integration and processing module 108, which processes time-aligned gravimetric and seismic data into a preliminary multi-modal anomaly map. The direct hydrogen indicator interpretation and visualization module 110 may then overlay these results with the sparse soil gas measurements to identify convergent indicators suggestive of direct hydrogen presence. Upon detection of coincident gravity lows, depressed Vp / Vs ratios, and moderate hydrogen concentrations, the field team may use the interpretation outputs to trigger real-time re-tasking of the passive seismic acquisition unit 102, extending its sensor grid along a mapped fault trace. The field team may use the output created by the direct hydrogen indicator interpretation and visualization module 110 to adjust their sensor layout, to achieve an improved view of thearea. The information gathered may comprise of spatially correlated seismic, gravity, and gas datasets; preliminary velocity and Q factor models; candidate direct hydrogen indicator clusters; and a decision tree guiding expansion of future surveys. This enables the operator to down-select exploration blocks and allocate resources toward zones with demonstrable hydrogen prospectivity, even in the absence of prior subsurface control.

[0074] Figure 2 illustrates an example of a passive seismic acquisition unit 102. In this example, the passive seismic acquisition unit 102 is configured to acquire ambient seismic data across target survey areas by detecting seismic waves using seismometers. The passive seismic acquisition unit 102 can be configured to acquire both natural sources and anthropic sources of seismic data.

[0075] In the example illustrated in Figure 2, the passive seismic acquisition unit 102 comprises a survey planning module 200. The survey planning module 200 may define survey boundaries, target depths, spatial coverage, and / or measurement objectives that are matched to geological, geochemical, and / or structural site considerations. The survey planning module 200 may be implemented using geographic information system (GIS) software for digital terrain analysis, a standalone geological survey planning workstation, or a remote cloud-based platform orchestrating field campaigns. For example, the survey planning module 200 may be used to visualize geological strata and optimize subsurface profiles for hydrogen indicator detection, or alternatively, to delineate fault systems with a high risk of gas occurrence.

[0076] In this example, a sensor deployment planner 202 is provided in the passive seismic acquisition unit 102.In this example, the sensor deployment planner 202 is configured to calculate and determine optimal sensor deployment geometries, including the number, spacing, and grid arrangement of seismic and gravimetric sensors to be used during field operations. To calculate the optimal sensor deployment geometries, the sensor deployment planner may take into account the surface of the targeted area, the estimated depth of the “potential accumulation” of gas, the number of sensors available, and the location of the anticipated seismic sources (based on previous acquisition). All of the aforementioned parameters may need to be integrated in a script base module able to calculate the optimal deployment geometries for the specific target. The sensor deployment planner 202 may be an automated computational tool embedded in survey -planning platforms, or alternatively, as a script-based module run on local or cloud computing resources. For example, the sensor deployment planner 202 may generate a hexagonal deployment grid for maximizing lateral coverage, or alternatively, may optimize a linear array for targeted depth profiling depending on the survey objective.

[0077] In this example, a sensor spacing logic 204 is provided in the passive seismic acquisition unit 102. The sensor spacing logic 204 may receive survey geometry and sensor requirements from the sensor deployment planner 202 and calculate specific spatial intervals and positions of sensors according to criteria established inthe exploration framework. The sensor spacing logic 204 may be executed as a rule-based optimization engine embedded in survey design software, or alternatively as a library function invoked from a seismic array configuration utility. For example, the sensor spacing logic 204 may adapt sensor separation distances to target formation depths as indicated by geological data, or may introduce additional sensors in areas of anticipated lithological heterogeneity

[0078] In the example illustrated in Figure 2, a seismic sensor interface 206 is provided in the passive seismic acquisition unit 102. The seismic sensor interface 206 may manage communication, synchronization, and quality control for seismic sensors deployed in the field as defined by the preceding survey geometry. The seismic sensor interface 206 may be provided as a hardware control unit physically distributed among connected sensors, or as a software controller running on a portable field acquisition terminal. For example, the seismic sensor interface 206 may coordinate wireless time synchronization among seismometers deployed in a seismic array, or may log temperature and environmental metadata from MEMS-based seismic modules along the survey line.

[0079] In this example, a field data acquisition controller 208 is provided in the passive seismic acquisition unit 102. The field data acquisition controller 208 is a module that may manage and coordinate the real-time collection and quality control of multiple types of geophysical data (seismic, geochemical, and gravimetric). In this example, the field data acquisition controller 208 first determines what data to collect as well as from where and when, based on configurations set by earlier planning modules. The field data acquisition controller 208 may then collect raw or partially processed data from distributed sensors and ensure temporal alignment between different sensor types. The field data acquisition controller 208 finally may generate alerts or automated adjustments (e.g., re-trigger sampling, recalibrate instruments) based on thresholds or quality control rules. The field data acquisition controller 208 may be implemented as embedded acquisition firmware in a portable survey controller, or as a distributed field logging application executed on multiple synchronized acquisition devices. For example, the field data acquisition controller 208 may control the triggering and data streaming from autonomous seismic nodes over a wireless mesh, or may synchronize gravimetric sensor readings at intervals matched to site logistics and instrument availability.

[0080] Figure 3 illustrates an example of an adaptive survey design and field deployment method 300. The adaptive survey design and field deployment method 300 may be executed as part of a geophysical exploration campaign, a regional resource assessment, or a research study requiring non-invasive subsurface imaging. The adaptive survey design and field deployment method 300 may enable systematic planning, sensor array configuration, and data acquisition for subsurface geophysical sensing with improved efficiency and alignment to site-specific geological targets.

[0081] A survey planning module 302 is included in the adaptive survey design and field deployment method 300. The survey planning module 302 may define survey boundaries, target depths, spatial coverage, and measurement objectives that are matched to geological, geochemical, or structural site considerations. The survey planning module 302 may be implemented using geographic information system (GIS) software for digital terrain analysis, a standalone geological survey planning workstation, or a remote cloud-based platform orchestrating field campaigns. For example, the survey planning module 302 may be used to visualize geological strata and optimize subsurface profiles for hydrogen indicator detection, or alternatively, to delineate fault systems with a high risk of gas occurrence.

[0082] In the example illustrated in Figure 3, a sensor deployment planner 304 is connected to the adaptive survey design and field deployment method 300. The sensor deployment planner 304 may be configured to calculate and determine optimal sensor deployment geometries, including the number, spacing, and grid arrangement of seismic and gravimetric sensors to be used during field operations. The sensor deployment planner 304 may be realized as an automated computational tool embedded in survey -planning platforms, or alternatively, as a script-based module run on local or cloud computing resources. For example, the sensor deployment planner 304 may generate a hexagonal deployment grid for maximizing lateral coverage, or alternatively, may optimize a linear array for targeted depth profiling depending on the survey objective.

[0083] A sensor spacing logic 306 is provided in the adaptive survey design and field deployment method 300.The sensor spacing logic 306 may receive survey geometry and sensor requirements from the sensor deployment planner 304 and calculate specific spatial intervals and positions of sensors as discussed above. The sensor spacing logic 304 may be executed as a rule-based optimization engine embedded in survey design software, or alternatively as a library function invoked from a seismic array configuration utility. For example, the sensor spacing logic 306 may adapt sensor separation distances to target formation depths as indicated by geological data, or may introduce additional sensors in areas of anticipated lithological heterogeneity.

[0084] In this example, a seismic sensor interface 308 is part of the adaptive survey design and field deployment method 300. The seismic sensor interface 308 may manage communication, synchronization, and quality control for seismic sensors deployed in the field as defined by the preceding survey geometry. The seismic sensor interface 308 may be provided as a hardware control unit physically distributed among connected sensors, or as a software controller running on a portable field acquisition terminal. For example, the seismic sensor interface 308 may coordinate wireless time synchronization among seismometers deployed in a seismic array, or may log temperature and environmental metadata from MEMS-based seismic modules along the survey line.

[0085] In the example illustrated in Figure 3, a field data acquisition controller 310 is included in the adaptive survey design and field deployment method 300. The field data acquisition controller 310 may orchestrate the active sampling, collection, timestamping, and quality monitoring of seismic, geochemical, and gravimetric data streams according to configurations established by the seismic sensor interface 308 and earlier planning modules. The field data acquisition controller 310 may manage and coordinates the real-time collection and quality control of multiple types of geophysical data (seismic, geochemical, and gravimetric). The field data acquisition controller 310 may first determine what data to collect as well as from where and when, based on configurations set by earlier planning modules. The field data acquisition controller 310 may collect raw or partially processed data from distributed sensors and ensure temporal alignment between different sensor types. The field data acquisition controller 310 may also generate alerts or automated adjustments (e.g., re-trigger sampling, recalibrate instruments) based on thresholds or quality control rules. The field data acquisition controller 310 may be implemented as embedded acquisition firmware in a portable survey controller, or as a distributed field logging application executed on multiple synchronized acquisition devices. For example, the field data acquisition controller 310 may control the triggering and data streaming from autonomous seismic nodes over a wireless mesh, or may synchronize gravimetric sensor readings at intervals matched to site logistics and instrument availability. The field data acquisition controller 310 may be implemented as embedded acquisition firmware in a portable survey controller, or as a distributed field logging application executed on multiple synchronized acquisition devices. For example, the field data acquisition controller 310 may control the triggering and data streaming from autonomous seismic nodes over a wireless mesh, or may synchronize gravimetric sensor readings at intervals matched to site logistics and instrument availability.

[0086] The adaptive survey design and field deployment method 300, employing the survey planning module 302, sensor deployment planner 304, sensor spacing logic 306, seismic sensor interface 308, and field data acquisition controller 310, as shown in Figure 3, may provide programmable, site-adaptive coordination of field sensor networks for subsurface geophysical acquisition that is responsive to evolving geological objectives and practical constraints of survey logistics and sensor capabilities.

[0087] In some examples, the adaptive survey design and field deployment method 300 may be applied during an early-stage reconnaissance survey across a geographical region of interest characterized by rugged topography and minimal pre-existing geophysical control. The survey planning module 302 may be executed on a compact computing device such as tablet or laptop, by a three-person field team, importing coarse elevation data and gravity and / or magnetic interpretations from airborne surveys to define an initial 10 km2polygon focused geological structure suspected to host hydrogen accumulation. The sensor deployment planner 304 may generate a hybrid layout combining a central hexagonal array with peripheral transects, selecting deployment geometries that balance spatial coverage with known access constraints and slope stability criteria. Thesegeometries may be submitted to the sensor spacing logic 306, which modifies spacing densities based on topographic roughness and overburden variability, flagging deployment gaps and automatically allocating reserve sensors to under-constrained sections. The seismic sensor interface 308, operating via a low-bandwidth field controller, may communicate with each MEMS node upon installation, conducting environmentdependent sampling checks, firmware verification, and wireless synchronization. Concurrently, the field data acquisition controller 310 may coordinate staggered acquisition windows to account for unpredictable anthropogenic noise, timestamp all waveforms and gravity readings, and maintain a real-time log of deployed sensor health and data completeness. During the campaign, the adaptive system may revise deployment coordinates on a rolling basis in response to terrain obstructions and incomplete gravimetric coverage, rerouting crews to nearby fallback positions while maintaining coherence with the original survey design. The adaptive system may revise deployment coordinates dynamically in the field by analysing real-time feedback from sensor installation, terrain data, and coverage gaps. The adaptive system can detect obstructions (e.g., steep slopes, inaccessible terrain, absence of soil) or incomplete gravimetric data, and automatically identify fallback positions nearby that maintain alignment with the overall survey geometry. These adjustments can then be communicated to field crews and can be incorporated into the sensor deployment planner, ensuring continuous coverage and data quality without interrupting the survey flow. As a result, the system state may comprise an adaptively deployed sensor array with terrain-constrained geometry, synchronized and time- stamped data streams from seismic and gravimetric sources, and field-adjusted metadata flags that feed directly into downstream fusion and interpretation modules.

[0088] In other examples, the adaptive survey design and field deployment method 300 may be applied during a targeted hydrogen indicator campaign across a deformed sedimentary basin using pre-positioned acquisition nodes from a prior deployment. The survey planning module 302 may be executed locally on a geologist’s field laptop pre-loaded with structural interpretations and depth slices from previous geophysical images, allowing the user to define revised high-resolution survey zones that overlap historically anomalous Vp / Vs and gravimetric lows. The sensor deployment planner 304 may then recalculate a nested sensor array configuration with an aperture of 5km and up to 500-meter sensor spacings across shallower targets (about 0km to 5 km deep) near mapped structural closures (traps), and 500-meter to 1 -kilometer sensor spacings around less resolved regions, adapting the deployment geometry to maintain fidelity across variable imaging depths. The sensor spacing logic 306 may receive these geometries and apply refinement rules to concentrate node densify where prior P-wave velocity depressions and resonance frequency drops had aligned, automatically repositioning certain gravimeter stands to enhance sensitivity in previously under-sampled quadrants. Areas where anomalous Vp / Vs. resonance frequency drops and gravimetric lows may be identified as target areas as these anomalies may suggest the presence of gas accumulation. By knowing the expected depth of the gas accumulation, refinement rules may be applied by the sensor spacing logic 306 to recalculatea nested sensor array configuration in order to maximize the resolution of the image of the targeted area. In order to increase the resolution of the image of the targeted area, the spacing between sensors may be decreased. Upon arrival at each updated sensor location, the seismic sensor interface 308 may wirelessly interrogate MEMS gravimeters embedded in the prior grid, perform time sync and health diagnostics, and inject updated firmware routines supporting HVSR and Q factor recording modes. Simultaneously, the field data acquisition controller 310 may trigger gravimetric readings at relocated stands while initiating synchronized rolling acquisition sessions across the passive seismic network, scheduling data bursts during ambient quiet periods and storing timestamped body waveforms and gravity field readings in the local data buffer. The data fusion processor 400 may combine data (seismic, gravimetric, soil gas measurements) by matching their timestamps, locations, and sensor settings. Time and position information may be used to align the data correctly, then merge geophysical and geochemical readings into one clear format. The completed deployment may yield harmonized, high-resolution seismic and gravimetric data streams that align with earlier soil gas sampling zones, enabling subsequent modules to produce updated direct hydrogen indicator visualizations for comparison with prior surveys. As a result of this operation, the system may reflect updated survey geometries, revised sensor coordinates, enriched time-synchronized geophysical datasets, and reactivated acquisition hardware, all prepared for downstream fusion and interpretation workflows.

[0089] Figure 4 illustrates an example of a data integration and processing module 108, as described in Figure 1.The data integration and processing module 108 may be implemented, for example, as a rack-mounted high- performance computing workstation deployed in a field operations trailer, a networked multi -core server in a central processing facility, or a cloud-hosted analytics appliance which is accessible using secure connections. The data integration and processing module 108 may be configured to perform synchronization, fusion, and multimodal analysis of geophysical and geochemical data acquired from a survey operation, facilitating the identification and characterization of subsurface hydrogen accumulations.

[0090] In the example illustrated in Figure 4, a data fusion processor 400 is connected to the data integration and processing module 108 and configured to align, synchronize, and merge disparate datasets received from external geophysical and geochemical acquisition units. The data fusion processor 400 may align, synchronize, and merge disparate datasets by applying timestamp matching, spatial georeferencing, and sensor-specific calibration routines. The data fusion processor 400 may use shared time codes and location metadata to ensure temporal and spatial coherence, then integrates geophysical and geochemical inputs into a unified data structure. This may enable consistent cross-domain analysis and supports accurate interpretation in downstream modules. The data fusion processor 400 may, for example, operate on digital waveform time series from seismic arrays, digital gravimetric field logs, or point-based soil gas concentration records. In some instances, the data fusion processor 400 may perform temporal alignment of seismic and gravimetric datasources acquired asynchronously from geographically distributed sensors. In other examples, the data fusion processor 400 may execute spatial registration routines to produce combined voxel-based survey volumes containing both geophysical and geochemical attributes. The data fusion processor 400 may also be configured to supply harmonized multi-modal datasets to downstream processing engines within the data integration and processing module 108.

[0091] In this example, a gravimetric anomaly mapper 402 is connected to the data integration and processing module 108 and configured to extract, visualize, and model significant variations in the measured gravitational field across a survey area. The gravimetric anomaly mapper 402 may extract, visualize, and model variations in the gravitational field by analysing gravity data combined with location information. The gravimetric anomaly mapper 402 may calculate changes and / or gradients in gravity across the area and creates visual maps showing where gravity is lower than expected, indicative of less dense rocks below. The gravimetric anomaly mapper 402 may also build 3D models to highlight areas that might contain trapped hydrogen or gas, based on these low-density zones. The gravimetric anomaly mapper 402 may be coupled to the data fusion processor 400 and may operate on merged datasets including gravity field readings and spatial context metadata. The gravimetric anomaly mapper 402 may, for example, calculate spatial gravity gradients and interpolate these into rasterized anomaly maps displaying zones of gravity reduction suggestive of reduced bulk density of the subsurface rocks. The gravimetric anomaly mapper 402 may calculate spatial gravity gradients by measuring how gravity values change between nearby sensor locations across the survey area. The gravimetric anomaly mapper 402 may then use interpolation methods, such as kriging or inverse distance weighting, to estimate gravity values between the measured points. These estimates may be used to create a rasterized anomaly map, helping to identify subsurface features such as low-density zones that may suggest trapped gases. In further examples, the gravimetric anomaly mapper 402 may generate three-dimensional contour models of the subsurface, highlighting regions likely to harbour trapped hydrogen or other gases due to anomalously low densify contrast in a water-saturated environment. The gravimetric anomaly mapper 402 may provide outputs to the data integration and processing module 108 for use by associated components.

[0092] In the example illustrated in Figure 4, a seismic velocify / ratiometric analyser 404 is connected to the data integration and processing module 108 and configured to compute subsurface elastic property indicators from seismic data streams processed by the data fusion processor 400. The seismic velocify / ratiometric analyser 404 may, for example, extract first-arrival P-wave velocity (Vp) profiles from multichannel seismic waveform records, extract the polarity of the first wave, or generate two-dimensional velocity cross-sections by applying inversion techniques. In additional examples, the seismic velocify / ratiometric analyser 404 may calculate Vp / Vs ratios by using both P-wave and S-wave arrivals to characterize formation gas saturation, or apply ratiometric analysis to compare velocity reductions across structurally analogous survey sites. The analyser404 may further determine wave propagation attributes including phase velocity, group velocity, and seismic wave amplification derived from the recorded waveforms or recorded seismic data to identify variations in elastic properties and potential gas-related anomalies. Outputs from the seismic velocity / ratiometric analyser 404 may be transmitted to the data integration and processing module 108 for subsequent multimodal interpretation.

[0093] In this example, a seismic Q factor calculator 406 is connected to the data integration and processing module 108 and configured to quantify seismic wave attenuation properties as a function of frequency and travel path. The seismic Q factor calculator 406 may work by analysing how seismic wave energy decreases as it travels through the ground. The seismic Q factor calculator 406 may use field data such as amplitude and waveform shape to measure how much energy is lost at different frequencies and along different paths. By fitting this data to known models, the seismic Q factor calculator 406 may estimate how much of the seismic wave energy loss is due to the rock itself or scattering effects. The seismic Q factor calculator 406 can also create maps showing where more wave absorption happens, which helps identify areas with gas, which absorbs more energy, water, or brine. The seismic Q factor calculator 406 may receive input datasets harmonized by the data fusion processor 400, including spectral amplitude and waveform envelope data obtained from field- acquired seismic records. The seismic Q factor calculator 406 may, for example, estimate intrinsic and scattering attenuation contributions by fitting theoretical decay models to observed amplitude spectra, or compute Q factor maps that visualize spatial patterns in seismic wave absorption. In further applications, the seismic Q factor calculator 406 may support discrimination between gas-filled and water / brine-saturated formations based on observed differences in wave energy dissipation. The computational results produced by the seismic Q factor calculator 406 may be stored in the data integration and processing module 108 for integrated reservoir assessment.

[0094] In this example, a HVSR analyser 408 is connected to the data integration and processing module 108 and configured to process noise-based seismic measurements for the purpose of calculating horizontal-to-vertical spectral ratios and detecting resonance frequency shifts associated with buried gas accumulations. The HVSR analyser 408 may interface with the data fusion processor 400 to access waveform data that has been spatially and temporally normalized. The HVSR analyser 408 may, for example, compute localized HVSR curves using single-station noise records acquired over target sites, or generate maps showing variations in ellipticity and resonance frequency attributable to changes in seismic impedance. Additionally, the HVSR analyser 408 may facilitate statistical comparison across multiple survey locations to aid in the identification of anomalous spectral signatures that correlate with direct hydrogen indicators. The results generated by the HVSR analyser 408 may be retained within the data integration and processing module 108 and provided to visualization and interpretation modules for final direct hydrogen indicator assessment.

[0095] In some examples, the data integration and processing module 108 may be deployed as a high-availability rack-mounted computing system at a remote hydrogen exploration site, where the data fusion processor 400 receives data feeds from multiple seismometers, digital gravimeters, and portable gas analysis instruments via secure fibre optic or wireless connections. The data fusion processor 400 can align seismic P- and S-wave time series, interpolate gravimetric measurements onto a common grid, and merge hydrogen and methane concentration data, to produce a cohesive dataset for downstream analytical modules. The data fusion processor 400 can take different types of data (seismic waves, gravity readings, gas concentrations) and align the data in time and space. The data fusion processor 400 can fill in gaps by estimating values where measurements are missing, and combine all the data into one complete dataset. This makes it easier to combine, analyse and understand the data. The gravimetric anomaly mapper 402 can then use these merged datasets to generate geospatial maps highlighting gravity lows, constructed using inverse distance interpolation and visualized as colour-coded anomaly surfaces on workstation displays. The seismic velocity / ratiometric analyser 404 can operate on processed seismic waveforms to compute first-break P-wave velocity models, calculate Vp / Vs ratios by combining P- and S-wave picksand derive waveform propagation attributes including phase velocity, group velocity, and seismic signal amplification, with the resulting measurements providing indicators of subsurface elastic properties and potential gas-filled formations. Seismic attenuation properties can be analysed by seismic Q factor calculator 406, which fits spectral decay functions to the amplitude data and generates maps that delineate Q anomalies coincident with mapped gravity lows. Simultaneously, the HVSR analyser 408 can processes passive tremor recordings to extract site-specific resonance frequencies, overlaying HVSR shift contours on the joint anomaly map to strengthen candidate direct hydrogen indicator confidence for subsequent visualization and reporting.

[0096] In other examples, the data integration and processing module 108 may be operated at a regional exploration headquarters to support cross-site anomaly screening and prioritization across a portfolio of candidate hydrogen provinces. The data fusion processor 400 may receive and import asynchronous geophysical and geochemical datasets received via secure upload from multiple survey teams operating in diverse geological settings, including passive seismic recordings, gravimetric transects, and soil gas concentration logs. Upon import, the data fusion processor 400 may perform spatial normalization by resampling all inputs to a unified voxel grid, apply time-alignment corrections to match acquisition epochs, and harmonize metadata fields to enable inter-site comparison. The gravimetric anomaly mapper 402 may then compute standardized residual gravity maps for each site using a consistent Bouguer correction and trend removal protocol, flagging zones exhibiting multi-sigma departures from regional baselines. In parallel, the seismic velocity / ratiometric analyser 404 may execute a lightweight inversion pass to produce normalized Vp and Vp / Vs models using shared velocity constraints across the portfolio, optionally integrating waveform propagation attributes derived from the seismic records. The seismic Q factor calculator 406 may derivefrequency-averaged attenuation volumes for each site, with all Q anomalies indexed to equivalent geological horizons using interpreted depth markers. The seismic Q factor calculator 406 may derive frequency-averaged attenuation volumes by first estimating the seismic attenuation at different frequencies and locations using field seismic data. The seismic Q factor calculator 406 may then measures how seismic wave amplitudes decrease over distance and frequency and averages these attenuation values over a range of frequencies to produce a 3D volume showing how much seismic energy is absorbed in different parts of the subsurface. This volume helps identify zones with varying rock properties, such as areas containing gas or fluids. The HVSR analyser 408 may process low-frequency microtremor records to extract site-specific resonance peaks and ellipticity parameters, tagging anomalous frequency shifts and curve shapes consistent with prior direct hydrogen indicator training sets. Outputs from all modules are compiled into a centralized multi-site database, where sites are automatically ranked based on an integrated anomaly strength index that weights gravity minima, velocity depression magnitude, Q factor contrast, and HVSR deviation. Operators of the equipment may use this data to assess where further follow-up analysis is required and to allocate additional field resources toward top-quartile direct hydrogen indicator clusters. As a result of this operation, the system state includes crosscalibrated, modality-aligned geophysical indicator maps from multiple projects, prioritized candidate zones for detailed direct hydrogen indicator interpretation, and a unified database supporting comparative geological and operational decision-making across the exploration portfolio.

[0097] Figure 5 illustrates an example of a direct hydrogen indicator interpretation and visualization module 110, as introduced in Figure 1. The direct hydrogen indicator interpretation and visualization module 110 may be implemented within a desktop geological analysis workstation, an integrated exploration server on a seismic survey vehicle, or a remote-access interpretation terminal. The direct hydrogen indicator interpretation and visualization module 110 may interpret processed data from multiple geophysical and geochemical sources to identify direct hydrogen indicators and provides visual representations supporting subsurface reservoir evaluation. The uses of gravimetric data are shown in the visual representations 500 and 504 of Fig. 5. The low-density zone created by the gas accumulation produces a slightly negative gravity anomaly. The bodywave seismic records are shown in illustrations 506 and 508 of Fig. 5. A gas accumulation can be identified by a decrease in Vp and a decrease of the Vp / Vs ratio. The uses of surfaces-wave seismic records are shown in illustrations 502 and 510 of Fig. 5. Above a gas accumulation, a shift towards the low frequencies of the HVSR curve are observed, and the quality factor describing how much seismic energy is lost decreases. Illustration 512 of Fig. 5 represents the final geological interpretation produced from the combination of the above-mentioned data.

[0098] A gravimetric results visualizer 500 can be connected to the direct hydrogen indicator interpretation and visualization module 110 and configured to display gravity anomaly information mapped across a surveyregion. The gravimetric results visualizer 500 may operate as a three-dimensional mapping engine, a two- dimensional contoured output generator, or a grid-based spatial visualization tool. Within the direct hydrogen indicator interpretation and visualization module 110, the gravimetric results visualizer 500 may receive processed gravity anomaly data for example from a gravity field modelling engine or an inversion analysis platform, and may be used to depict reduced bulk density zones potentially associated with gas accumulations. In one example, the gravimetric results visualizer 500 may display colour-coded maps of Bouguer anomalies overlaid upon topographic or geological base layers. In another possible example, the gravimetric results visualizer 500 may generate interactive spatial cross sections allowing users to interrogate regional gravity minima correlated with location metadata.

[0099] A resonance frequency shift display 502 can be connected to the direct hydrogen indicator interpretation and visualization module 110 and configured to present graphical representations of seismic resonance frequency curves derived from horizontal-to-vertical spectral ratio analysis. The resonance frequency shift display 502 may be implemented as a frequency spectrum charting utility or as a time-frequency plot generation component. When used within the direct hydrogen indicator interpretation and visualization module 110, the resonance frequency shift display 502 can receive results from an HVSR spectral analysis engine and can be used to highlight downward or anomalous shifts in resonance frequency, which are indicative of reductions in subsurface seismic velocity and impedance above suspected hydrogen or other gas accumulations. For example, the resonance frequency shift display 502 may generate plots showing frequency trends over multiple locations along a survey transect. In another example, the resonance frequency shift display 502 may provide overlays comparing reference backgrounds against survey anomalies for rapid operator assessment.

[0100] An inverted density modeler 504 is connected to the direct hydrogen indicator interpretation and visualization module 110 and configured to build subsurface density models by applying inversion algorithms to gravimetric data. The inverted density modeler 504 can work in different ways depending on the method used to create the subsurface density model. The inverted density modeler 504 may operate as a three- dimensional voxel-based imaging pipeline, a finite-element inversion engine, or a direct profile-generation utility. The three-dimensional voxel-based imaging pipeline may allow the inverted density modeler 504 to build a 3D image made up of small cubes (voxels), each representing a tiny volume underground with its own estimated density value. The finite-element inversion engine may use a mathematical approach that breaks the subsurface into smaller, interconnected elements to solve complex equations and estimate density variations more precisely. The direct profile-generation utility may allow the inverted density modeler 504 to quickly produce simpler, layered profiles or cross-sections of density without full 3D modelling, useful for faster or less detailed analysis.

[0101] When functioning as part of the direct hydrogen indicator interpretation and visualization module 110, the inverted density modeler 504 may reconstruct the internal density distribution of stratigraphic layers and highlights zones of anomalously low density that are consistent with the presence of free gas within pore spaces. In one instance, the inverted density modeler 504 may compute and render a layer-by-layer pseudosection of density estimated to several kilometres below ground level, for example, in a range of 0 - 10 kilometres below ground level. In a second instance, the inverted density modeler 504 may generate animated visualizations of density changes temporalized with respect to data acquisition intervals.

[0102] A velocity anomaly visualizer 506 is connected to the direct hydrogen indicator interpretation and visualization module 110 and configured to present spatial maps or cross-sectional plots of seismic wave velocity variations. The velocity anomaly visualizer 506 may utilize P-wave velocity (Vp) data, S-wave velocity (Vs) differences, or composite travel time surfaces derived from field measurements. Within the direct hydrogen indicator interpretation and visualization module 110, the velocity anomaly visualizer 506 may be used to indicate potential gas-bearing formations by depicting areas of Vp reduction relative to geological expectations or background models. For example, the velocity anomaly visualizer 506 may generate colour contour plots of Vp variation with depth alongside interpreted well logs. In another example, the velocity anomaly visualizer 506 may display interactive three-dimensional cubes where velocity anomalies are juxtaposed with structural features extracted from geological models.

[0103] A Vp / Vs ratio plotter 508 may be connected to the direct hydrogen indicator interpretation and visualization module 110 and can be configured to create visual displays of the ratio between body wave velocity and shear wave velocity as measured in the survey area. The Vp / Vs ratio plotter 508 may be realized as a spatial trend mapper or as a scatter plot generator comparing ratios across surveyed locations. Within the direct hydrogen indicator interpretation and visualization module 110, the Vp / Vs ratio plotter 508 may receive processed velocity information for display, enabling identification of zones where the ratio is consistently reduced, a known direct indicator of gas saturation in geological formations. For instance, the Vp / Vs ratio plotter 508 can generate longitudinal plots along a transect showing abrupt decreases in ratio at key survey points. Alternatively, the Vp / Vs ratio plotter 508 may output two-dimensional heat maps correlating anomalous values spatially with other geophysical datasets in real time.

[0104] A quality factor map generator 510 may be connected to the direct hydrogen indicator interpretation and visualization module 110 and can be configured to render two-dimensional or three-dimensional maps of the seismic quality factor (Q value), representing wave attenuation throughout the region of interest. The quality factor map generator 510 may be implemented as a digital attenuation surface generator or as a profile-based Q distribution plotter. In its role within the direct hydrogen indicator interpretation and visualization module 110, the quality factor map generator 510 may visualize spatial variations in seismic wave energy absorption,which is heightened in gas-charged zones due to increased scattering and attenuation. For example, the quality factor map generator 510 may output Q value overlays on depth sections extracted from seismic surveys. In another case, the quality factor map generator 510 may produce temporal Q variation graphs to examine dynamic changes during repeat monitoring surveys.

[0105] A geological context integrator 512 may be connected to the direct hydrogen indicator interpretation and visualization module 110 and can be configured to provide integrated visualization and correlation of all geophysical indicator outputs with geological or stratigraphic models of the survey area. The geological context integrator 512 may be embodied as a three-dimensional earth model viewer, an automated stratigraphic registration engine, or a multi-layer overlay compositor. Within the direct hydrogen indicator interpretation and visualization module 110, the geological context integrator 512 may receive outputs from the gravimetric results visualizer 500, resonance frequency shift display 502, inverted density modeler 504, velocity anomaly visualizer 506, Vp / Vs ratio plotter 508, and quality factor map generator 510 and overlay these datasets onto structure maps, geological cross sections, well log data, or stratigraphic columns. For example, the geological context integrator 512 may enable the display of interpreted geophysical attributes alongside mapped faults and formation boundaries. In a further example, the geological context integrator 512 can dynamically correlate interpreted gas indicator zones with known lithological unit extents to support reservoir assessment and report generation.

[0106] In some examples, the direct hydrogen indicator interpretation and visualization module 110 may be deployed as part of a regional hydrogen exploration project using a mobile field workstation installed in an off-road survey vehicle. Raw and pre-processed data streams can be delivered to the gravimetric results visualizer 500, which can render three-dimensional grids highlighting spatial zones of gravity minima after each new gravimetric survey line is completed, allowing field geophysicists to adjust survey spacing in real time. Simultaneously, the resonance frequency shift display 502 may receive horizontal-to-vertical spectral ratio results from previous passive seismic acquisitions, displaying a continuously updated suite of frequency spectra where downward trends above interpreted anomaly zones can be tracked by technical staff, also referred to as equipment operators. The inverted density modeler 504 can processes each gravimetric dataset by running iterative model inversions, producing updated cross-sectional density pseudosections linked to interpreted well markers for validation. Within the same workflow, the velocity anomaly visualizer 506 can display P-wave velocity profiles overlaid onto topographic baselines and cut surface models, while the Vp / Vs ratio plotter 508 allows operators to mark and annotate map sections that fall below pre-determined critical ratio thresholds set for hydrogen prospectivity. The quality factor map generator 510 can be used to compare Q value surface maps between baseline and repeated surveys, supporting dynamic flagging of locations exhibiting increasing attenuation. The geological context integrator may be used to 512 unify all visual outputs, superimposing1interpreted anomaly boundaries onto three-dimensional surface and borehole-constrained geological models, with all visualizations adjusted interactively through the main workstation’s multi-display setup for active field decision-making.

[0107] In other examples, the direct hydrogen indicator interpretation and visualization module 110 may be deployed within a multi-disciplinary technical review environment to support final prospect definition for hydrogen exploration blocks under pre-drill evaluation. Following upstream processing by the data integration and processing module 108, the gravimetric results visualizer 500 may deliver three-dimensional anomaly maps for each candidate site, highlighting consistent gravity lows across structurally favourable zones. The resonance frequency shift display 502 may then overlay HVSR-derived resonance shifts and ellipticity anomalies onto the same survey grid, enabling geophysicists to cross-reference spectral results with structural geologists’ interpretations of potential migration conduits and traps. Simultaneously, the inverted density modeler 504 may present voxelized pseudo-sections of subsurface density estimated through joint inversion, with uncertainty bands shaded to support risk assessment discussions. Velocity anomaly visualizer 506 may render stacked and depth-converted P-wave velocity profiles, while the Vp / Vs ratio plotter 508 may generate threshold-filtered ratio maps allowing reviewers to isolate zones of reduced ratios below 1.5 for gas saturation screening. The quality factor map generator 510 may produce Q factor differentials between primary and control transects, allowing discrimination between potentially gas-charged compartments and brine-filled equivalents. All outputs may be combined by the geological context integrator 512 into a synchronized workspace where geophysical anomalies are registered against interpreted fault traces, lithologic contacts, and borehole stratigraphy where available. Operators may collaboratively annotate outputs, export visual slices for reservoir simulation inputs, or generate bundled deliverables for management presentations. As a result, each prospect area may be resolved into one or more direct hydrogen indicator-confirmed targets with spatially integrated visual documentation, quantitative anomaly metrics, and a cross-disciplinary consensus on drill prioritization.

[0108] Figure 6 illustrates an example of a soil gas measurement database 600, as referenced in soil gas measurement unit 106. The soil gas measurement database 600 may be implemented, for example, as a relational data storage system deployed on a field-deployed ruggedised server, a centralized cloud-hosted laboratory platform, or a combination thereof. The soil gas measurement database 600 may be configured to store, index, and facilitate retrieval of all raw and processed measurements pertaining to gaseous species sampled from the soil during subsurface hydrogen surveying operations.

[0109] Hydrogen sample records 602 may be connected to the soil gas measurement database 600 and may be configured to record geolocated measurements of hydrogen concentration acquired by soil gas measurement unit 106. The hydrogen sample records 602 may be, for example, a structured data table within a relationaldatabase, a series of CSV data logs acquired by portable gas chromatographs, or a set of hydrogen concentration reports generated by tunable diode laser absorption spectrometers. The hydrogen sample records 602 may provide traceable storage of time-stamped hydrogen concentration values referenced to unique sample collection points within each survey grid defined by soil gas measurement unit 106.

[0110] In the example illustrated in Figure 6, methane sample records 604 may be connected to the soil gas measurement database 600 and configured to store methane concentration data collated in parallel with the hydrogen sample record 602. The methane sample records 604 may be implemented as a linked records data structure with entries for each site and sample, a field tablet collection module for digital methane entry, or as automated upload logs from environmental monitoring platforms. The methane sample records 604 can allow for the cross-correlation of methane and hydrogen measurements to discriminate between different types of hydrocarbon and hydrogen reservoirs, supporting interpretations generated by soil gas measurement unit 106.

[0111] In this example, a spatial location metadata 606 may be connected to the soil gas measurement database 600 and may be configured to store geospatial reference data corresponding to each sample record maintained within the hydrogen sample records 602 and methane sample records 604. The spatial location metadata 606 may be a geographic coordinates table indexed by a global navigation satellite system (GNSS) receiver, a digital mapping record created during site survey with a portable GIS unit, or a geocoding service operating on a mobile field data capture application. The spatial location metadata 606 may enable precise mapping of all gas sample locations, which is referenced during integrated analysis by soil gas measurement unit 106 and data integration and processing module 108.

[0112] A measurement timestamp log 608 may be connected to the soil gas measurement database 600 and is configured to store time-referenced records for each individual measurement event. The measurement timestamp log 608 may be populated by synchronized network time protocol (NTP) data loggers attached to each sampling instrument, as well as manual annotation fields entered by laboratory technicians upon sample processing. The measurement timestamp log 608 may facilitate chronological alignment of hydrogen and methane data streams, supporting the temporal synthesis required by soil gas measurement unit 106 when interpreting dynamic gas migration patterns.

[0113] A sensor calibration data 610 is connected to the soil gas measurement database 600 and is configured to document the calibration status, latest calibration time, and traceability for each measurement instrument used by soil gas measurement unit 106. The sensor calibration data 610 may be implemented, for example, as electronically signed calibration certificates stored as database records, as periodic field calibration logs automatically captured from field-deployed calibration management modules, or as validation entries recorded by laboratory personnel using calibration gas standards. The sensor calibration data 610 ensures data qualityassurance for all entries in the soil gas measurement database 600 and enables traceability for all hydrogen and methane concentration results referenced during further processing by data integration and processing module 108.

[0114] The soil gas measurement database 600 is structured to facilitate real-time querying and export of the hydrogen sample records 602, methane sample records 604, spatial location metadata 606, measurement timestamp log 608, and sensor calibration data 610 by integrated analytic systems, including data integration and processing module 108. For example, the soil gas measurement database 600 may support direct data exchange with cloud-based statistical analysis engines or enable field teams to download recent hydrogen and methane concentration data during ongoing surveys for adaptive survey management. In another example, the soil gas measurement database 600 may be used as a data source for semi-quantitative reservoir assessment method 700, in which multi-parametric records are accessed to calculate gas saturation estimates and to inform comparative indicator analysis. The soil gas measurement database 600 may also take measurements of gases such as helium, carbon dioxide, carbon monoxide, radon, hydrogen sulphide, nitrogen, and volatile organic compounds.

[0115] Each record maintained within the hydrogen sample records 602, methane sample records 604, spatial location metadata 606, measurement timestamp log 608, and sensor calibration data 610 can be persistently stored, regularly backed up, and indexed for high-throughput access during periods of heavy usage such as multi-day field campaigns, laboratory reanalysis sessions, or inter-site comparison studies. The soil gas measurement database 600 can thus provide a foundational repository and audit trail for all primary soil gas measurements obtained within the direct hydrogen indicator detection system 100, supporting reliable integration with higher-level modules and ensuring the scientific integrity of reservoir characterization outcomes.

[0116] In some examples, the soil gas measurement database 600 may be implemented at a remote desert exploration site using a ruggedised field server equipped with wireless connectivity, where hydrogen sample records 602 are populated by real-time output from a set of portable gas chromatographs carried by field staff or operators for each borehole sampling location. Methane sample records 604 may be recorded alongside every hydrogen entry using a portable methane spectrometer, and both data sets are automatically tagged with spatial location metadata 606 by a global navigation satellite system receiver integrated into the sampling kits. All entries, including sampling depth and local stratigraphic description, may be timestamped by measurement timestamp log 608, with logs referenced to a site-wide network time protocol-synchronized clock. Prior to each sampling shift, calibration gas standards can be used for gas chromatograph and methane spectrometer validation, with the calibration outcomes digitally recorded in sensor calibration data 610 as part of the daily instrument management protocol. The soil gas measurement database 600 can be accessed by a satellite-connected computer (such as a laptop) for immediate quality control review and later synchronized with a central laboratory database, where the full set of hydrogen and methane records can be used by the data integration and processing module 108 for alignment with geophysical survey results and by the semi- quantitative reservoir assessment method 700 for multi-point hydrogen saturation trend mapping.

[0117] In other examples, the soil gas measurement database 600 may be used as a central geochemical screening platform to guide survey prioritization across a portfolio of frontier exploration permits spanning diverse geological settings. Field teams operating in each permit block may collect and analyze hydrogen sample records 602 using portable hydrogen analyzers, with optional gas chromatography analysis during shallow borehole campaigns, while methane sample records 604 are acquired in parallel using portable diode laser spectrometers. These measurements provide critical insights into gas origin, enabling discrimination between biological and abiotic sources and improving the interpretation of both field data and subsurface fluid processes. All readings may be tagged with spatial location metadata 606 generated by GNSS-enabled computing devices (such as tablets) and automatically uploaded to the centralized database. The measurement timestamp log 608 may record sampling intervals down to 30 seconds, enabling detection of transient fluxes where readings are repeated over time. Sensor calibration data 610 may be linked to each reading via QR- coded calibration certificates scanned on-site and cross-verified against daily reference gas standards. Once uploaded, the database 600 may be queried by geochemical analysts to generate hydrogen anomaly heatmaps and gas composition ratio plots across all blocks, with automated filtering to isolate zones where hydrogen exceeds 100 ppm and methane remains below background thresholds. These zones may be flagged as high- confidence surface leakage candidates and prioritized for follow-up deployment of seismic and gravimetric sensors. The curated results may be exported as geocoded target lists and shared with survey coordinators via integrated dashboards, allowing adaptive tasking of passive acquisition assets managed through the survey planning module 302. As a result, the soil gas measurement database 600 may function not only as a secure repository for raw chemical data, but as a cross-site screening engine that filters for geochemically promising zones, accelerates survey decision cycles, and increases the probability of intersecting subsurface hydrogen accumulations during early-stage campaigns.

[0118] Figure 7 illustrates an example of a semi-quantitative reservoir assessment method 700. The semi- quantitative reservoir assessment method 700 may be implemented on a field geophysical workstation, a centralized geological interpretation server, or a networked data analysis cluster. The semi-quantitative reservoir assessment method 700 may be configured to receive integrated outputs from geophysical and geochemical surveys, evaluate those outputs through a combination of automated and expert-driven analyses, and generate an assessment of both hydrogen saturation and reservoir quality for one or more survey sites. More specifically, the semi-quantitative reservoir assessment method may generate an evaluation report orresult that estimates how much hydrogen gas is present in the reservoir (hydrogen saturation) and how suitable the reservoir is for storing or producing hydrogen, based on its geological and physical properties (reservoir quality). This output provides information on the potential and condition of the reservoir.

[0119] A comparative indicator analysis step 702 can be included in the semi-quantitative reservoir assessment method 700. The comparative indicator analysis step 702 may be configured to process and compare various geophysical and geochemical indicators from survey data. For example, where the semi-quantitative reservoir assessment method 700 receives direct hydrogen indicator visualizations and corresponding geophysical parameter maps from a direct hydrogen indicator interpretation and visualization module 110, the comparative indicator analysis step 702 may be operable to evaluate correlations between gravimetric anomaly fields and soil gas hydrogen concentrations. In another example, the comparative indicator analysis step 702 may process P-wave velocity (Vp) reduction zones and Vp / Vs ratio anomalies against spatially coincident methane sample records from a soil gas measurement database 600, identifying patterns consistent with gas-bearing formation boundaries.

[0120] In this example, a gas saturation estimation step 704 may be connected to the semi-quantitative reservoir assessment method 700 and configured to generate relative and semi-quantitative estimates of hydrogen gas content based on the output of the comparative indicator analysis step 702. The gas saturation estimation step 704 may, for example, compute probabilistic maps of hydrogen reservoir extent using indicator crosscorrelation, or apply statistical reservoir simulations to produce hydrogen saturation likelihoods for user- specified volumes. In a further example, the gas saturation estimation step 704 may assign semi-quantitative ranges, such as “trace,” “moderate,” or “high,” to sampled regions based on the integrated results of Vp / Vs ratio depressions and soil gas anomaly magnitudes supplied by the comparative indicator analysis step 702. In the context of the present patent specification, “trace” readings refer to measurements between 150 and 500 ppmv, “moderate” readings refer to measurements between 500 and 1000 ppmv, and “high” readings refer to measurements of 1000 ppmv and above. Measurements below 150 ppmv are considered as the regional pattern readings.

[0121] In this example, a reservoir qualify evaluation step 706 can be connected to the semi-quantitative reservoir assessment method 700 and may receive processed outputs from the gas saturation estimation step 704. The reservoir qualify evaluation step 706 may be configured to provide a qualitative assessment of the surveyed hydrogen reservoir, specifically addressing factors such as reservoir seal integrity, geological heterogeneity, and the likelihood of economic gas extraction. For example, the reservoir quality evaluation step 706 may utilize joint evaluation of inverted densify models and seismic Q factor maps from previous analysis steps to determine the presence of suitable trapping structures. In another example, the reservoir quality evaluation step706 may cross-reference HVSR-derived resonance frequency shifts with geologically mapped unconformities to provide recommendations about reservoir compartmentalization or leak potential.

[0122] In the example illustrated in Figure 7, a multi-site baseline comparator 708 is included in the semi- quantitative reservoir assessment method 700 and is configured to support the analysis of multiple survey sites or investigation areas for calibration and baseline building. The multi-site baseline comparator 708 may receive reservoir quality and saturation results from the reservoir quality evaluation step 706 and may assemble comparative trends on both intra-site and inter-site bases. For example, the multi-site baseline comparator 708 may establish empirical baselines for hydrogen anomaly strengths observed across a regional portfolio of field trials. In another example, the multi-site baseline comparator 708 can be used to normalize semi-quantitative assessment scores between pilot projects, supporting future exploration strategy and investment prioritization.

[0123] A reporting interface 710 may be connected to the semi-quantitative reservoir assessment method 700 and may be operable to synthesize and output the results of all preceding steps to user-facing endpoints. The reporting interface 710 may generate comprehensive technical reports summarizing direct hydrogen indicator maps, saturation estimates, and reservoir quality interpretations for engineering teams. As a further example, the reporting interface 710 may visualize cross-site comparative findings with graphical dashboards and export data files suitable for integration with asset management databases. The reporting interface 710 thus serves as a communication point between the semi-quantitative reservoir assessment method 700 and commercial or scientific decision-makers responsible for subsequent exploration or resource extraction activities.

[0124] In some examples, the semi-quantitative reservoir assessment method 700 may be applied by a national exploration joint venture evaluating multiple adjacent permit blocks in a prospective zone with structurally complex targets. Upon completion of multimodal field surveys using direct hydrogen indicator detection system 100, the comparative indicator analysis step 702 may operate on input datasets comprising gravity anomaly maps, Vp / Vs ratio heatmaps, HVSR-derived resonance frequency shifts, and soil gas hydrogen concentrations drawn from the direct hydrogen indicator interpretation and visualization module 110 and soil gas measurement database 600. Analysts or operators may execute an integrated correlation algorithm that overlays these parameters onto shared grid references, producing anomaly clusters that show consistent spatial alignment across modalities. The gas saturation estimation step 704 may receive these clusters and invoke a Bayesian classification model trained on historical basin analogues, generating semi-quantitative hydrogen saturation probabilities for each grid block within the permit zone. Grid blocks may be in the range of 0.1km2to 1.5km2, optionally, grid blocks are in the range of 0.1 km2to 1 km2. For instance, grid blocks showing gravity reductions greater than 0.6 mGal, Vp / Vs ratios below 1.5, and persistent hydrogen concentrations exceeding 150 ppm may be assigned a “high” prospectivity score with associated confidence intervals. Next, the reservoir quality evaluation step 706 may compute structural integrity indices for each block by combiningseismic Q factor values and inverted density contrast maps, flagging compartments with inferred seal weakness due to high attenuation and shallow density anomalies. The multi-site baseline comparator 708 may then compare saturation and quality scores against neighbouring license blocks and archived data from similar tectonostratigraphic units, establishing percentile rankings for each target zone and enabling inter-block prioritization. Finally, the reporting interface 710 may render fully annotated geospatial dashboards for executive review, including block-by -block investment readiness scores, hydrogen prospectivity contours, and digital export packages formatted for ingestion into a portfolio management platform. As a result of the assessment, each grid block may be tagged with updated geophysical and geochemical indicators, a quantified saturation confidence rating, a structural viability score, and a normalized ranking for regional planning and risk-adjusted exploration allocation.

[0125] In other examples, the semi-quantitative reservoir assessment method 700 may be employed by an environmental monitoring agency evaluating hydrogen migration near abandoned wells in a region with suspected leaks. For this scenario, the comparative indicator analysis step 702 may focus on anomaly patterns generated from increased soil gas hydrogen concentrations recorded in soil gas measurement database 600, correlated with subtle localized changes in gravimetric anomalies imported from direct hydrogen indicator interpretation and visualization module 110. The gas saturation estimation step 704 may apply a custom algorithm designed to estimate shallow hydrogen mass balances based on both soil anomaly persistence and Vp depression patterns mapped during repeated site surveys. The reservoir quality evaluation step 706 may further include comparative assessment of historical resonance frequency data to assign risk scores for gas seepage in specific parcels potentially intersecting legacy boreholes or fault zones. The multi-site baseline comparator 708 may aggregate assessment outputs and normalize data from new monitoring events against earlier baselines, flagging sites where hydrogen signals have increased beyond control limits. The reporting interface 710 may generate automated compliance documentation and periodic anomaly trend summaries, forwarding summary statistics and georeferenced anomaly maps to a regulatory database for ongoing environmental oversight.

[0126] The functions performed in the processes and methods may be implemented in differing order.Furthermore, the outlined steps and operations are only provided as examples, and some of the steps and operations may be optional, combined into fewer steps and operations, or expanded into additional steps and operations without detracting from the essence of the disclosed examples. It is to be understood that the above description is intended to be illustrative, and not restrictive. Other implementations will be apparent to those of skill in the art upon reading and understanding the above description. Although the present disclosure has been described with reference to specific example implementations, it will be recognized that the disclosure is not limited to the implementations described, but can be practiced with modification and alteration within thespirit and scope of the appended claims. Accordingly, the specification and drawings are to be regarded in an illustrative sense rather than a restrictive sense. The scope of the disclosure should, therefore, be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled.

Claims

CLAIMS1. A method of detecting subterranean hydrogen, the method comprising:selecting a geographical region of interest;disposing a network of measurement systems on said geographical region of interest, each measurement system comprising at least a seismic sensor;using said network of measurement systems, recording subterranean seismic data at each of a plurality of locations on said geographical region of interest, each location corresponding to a respective measurement system, where the subterranean seismic data includes pressure wave (P-wave) and shear wave (S-wave) data relating to body -waves propagating underground and surface-waves propagating on the surface of the Earth;processing the recorded seismic data to generate one or more tomographic models of P-wave velocity (Vp) and / or S-wave velocity (Vs) associated with the seismic waves propagating underground;where the step of processing the recorded seismic data further comprises comparing the amplitude spectra of seismic signals recorded at sensors located at different distances from a source, or by using coda waves, and measuring the amplitude decay over time and frequency, to calculate a value for the Seismic Quality Factor (Q Factor);where the step of processing the recorded seismic data further involves calculating a Horizontal- to-Vertical Spectral Ratio (HVSR) value from passive seismic data to determine resonance frequency shifts and changes in wave ellipticity due to subsurface impedance variations.

2. The method of claim 1, wherein the step of processing the recorded seismic data further comprises determining wave propagation attributes including phase velocity, group velocity, and seismic signal amplification derived from the recorded seismic data to identify variations in elastic properties and potential gas-related anomalies.

3. The method of claim 1 or claim 2, where the step of processing the recorded seismic data further comprises comparing P-wave velocities in different directions to detect azimuthal anomalies.

4. The method of any preceding claim, where the step of processing the recorded seismic data further comprises a forward modelling feedback loop, which comprises using anomalies in densify data to estimate the depth of a potential reservoir, and then performing a forward modelling step.

5. The method of any preceding claim, wherein the tomographic models are two-, three-or fourdimensional.

6. The method of any preceding claim, wherein tomographic models are generated based on the Vp velocity calculated from the seismic data.

7. The method of any preceding claim, wherein tomographic models are generated based the Vs velocity calculated from the seismic data.

8. The method of claim 7, wherein tomographic models of Vp and Vs velocity may be combined to generate a tomographic model of the Vp / Vs ratio.

9. The method of any preceding claim, wherein the seismic data are continuously collected by at least one seismic sensor.

10. The method of any preceding claim, wherein gravimetric data is continuously collected by each gravimeter.

11. The method of any preceding claim, wherein the tomographic models provide a visual display of density reductions, velocity anomalies, quality factor changes, and resonance frequency shifts, which are combined together to collectively identify hydrogen reservoir locations in the geographical region of interest.

12. The method of any preceding claim, further comprising:using at least one gas analyser, sampling the presence of hydrogen at multiple points on the geographical region of interest;wherein said interpreting said one or more tomographic models is carried out additionally in light of said gas samples collected by said at least one gas analyser.

13. The method of claim 12, further comprising: disposing the gas analyser underground at said multiple points across the geographical region of interest; optionally, at least 0.8 metres underground.

14. The method of claim 12 or 13, wherein each measurement system comprises a gas analyser associated with the location of that measurement system.

15. The method of any preceding claim, where the step of processing the recorded seismic data further comprises using a data fusion processor to align seismic P-wave and S-wave time series, interpolate the gravimetric data, and merge data collected from the gas analyser.

16. The method of any preceding claim, where the step of disposing a network of measurement systems further comprises calculating the optimal seismic sensor deployment geometries by considering the surface of the geographical region of interest, the estimated depth of a potential hydrogen source, the number of seismic sensors available, and the location of anticipated seismic sources.

17. A computerised apparatus for detecting subterranean hydrogen in a geographical region of interest, the apparatus comprising:i.) a plurality of seismic sensors disposed to form a network of measurement systems on said geographical region of interest;ii.) at least one gravimeter; andiii.) a data integration and processing module arranged to convert the data into a common format and generate a visual output,wherein the computerised apparatus is configured to carry out the method of any one of claims 1 to 14.

18. The computerised apparatus of claim 17, wherein the seismic sensors are 3-component seismic sensors.

19. The computerised apparatus of claim 17 or 18, wherein the seismic sensors are Micro Electrical Mechanical System based (MEMS-based).

20. The computerised apparatus of claim 17 or 18, wherein the seismic sensors are broadband seismic sensors.

21. The computerised apparatus of claim 17 or 18, wherein the seismic sensors are geophones.

22. The computerised apparatus of any of claims 17 to 20, wherein the gravimeter is spring-based.

23. The computerised apparatus of any of claims 17 to 20, wherein the gravimeter is Micro Electrical Mechanical System based (MEMS-based).

24. The computerised apparatus of any of claims 17 to 22, wherein said network comprises measurement systems disposed to form at least one grid on said geographical region of interest.

25. The computerised apparatus of any of claims 17 to 24, wherein said network comprises measurement systems disposed to form two or more grids on said geographical region of interest, at least two of said grids having different spacing between adjacent measurement systems.

26. The computerised apparatus of any of claims 17 to 25, wherein adjacent measurement systems are spaced less than 1 kilometre apart from each other; optionally, wherein adjacent measurement systems are spaced less than 500 metres apart from each other.

1. The computerised apparatus of any of claims 17 to 26, further comprising at least one instrument for soil gas measurement.

28. The computerised apparatus of any of claims 17 to 27, further comprising sensor nodes to integrate seismic, gravimetric, and / or soil gas measurement instruments.

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