A short offset transient electromagnetic method super deep detection system and signal analysis method

Abstract

The application discloses a short offset transient electromagnetic method super-deep detection system and a signal analysis method, relates to the technical field of geological detection, and comprises a signal receiving and collecting adaptive module, an original signal preprocessing module, a geometric effect correction and deep gain reconstruction module, a layered response separation module, an inversion imaging and prior constraint module and an analysis result output and visualization module. The near-field coupling, amplitude and phase distortion and deep energy attenuation caused by the short offset are systematically compensated through the quantitative geometric effect correction and the depth self-adaptive gain reconstruction. The mechanism not only synchronously recovers the late and weak amplitude signal in the time domain and the phase domain, but also maintains the amplitude and phase consistency, ensures the use of the homologous and comparable data input for the subsequent layered separation and inversion, and fundamentally expands the applicability of the short offset transient electromagnetic method to the super-deep target. Compared with the method of increasing the transmission or expanding the array, the scheme can stably extract the deep response when the equipment and the field layout are limited, and fundamentally expands the applicability of the short offset transient electromagnetic method to the super-deep target.

Description

Technical Field

[0001] This invention relates to the field of geological exploration technology, specifically to a short-offset transient electromagnetic method for ultra-deep exploration and a signal analysis method. Background Technology

[0002] Short-offset transient electromagnetic methods are widely used in deep mineral exploration, geological hazard investigation, and deep geological structure detection due to their advantages such as high detection efficiency and flexible field deployment. In ultra-deep exploration scenarios, the large depth of the target and the weak amplitude and low signal-to-noise ratio of the deep component signals, coupled with the significant energy attenuation and amplitude-phase distortion caused by the short-offset geometry, place stringent requirements on the signal reception, correction, and resolution capabilities of the detection system.

[0003] The core shortcomings of existing short-offset transient electromagnetic detection technologies lie in the difficulty of accurately quantifying and correcting the attenuation law of deep components due to short-offset geometric effects, and the lack of a targeted deep gain reconstruction mechanism. This results in the inability to effectively recover the signal energy of deep components, and they are also susceptible to amplitude and phase distortion caused by near-field coupling interference. Existing technologies mostly rely on increasing transmission power or expanding the detection array to improve the signal strength of deep components, which is difficult to apply in scenarios with high requirements for equipment portability and limited field deployment space. This leads to unstable deep component extraction, false anomalies in inversion imaging, and poor physical interpretability of the model.

[0004] In response to this problem, this application proposes a short-offset transient electromagnetic method for ultra-deep detection and a signal analysis method to solve the aforementioned issues. Summary of the Invention

[0005] The purpose of this invention is to provide a short-offset transient electromagnetic method for ultra-deep detection and a signal analysis method to solve the problems of existing technologies that rely on increasing transmission power or expanding the detection array to improve the signal strength at depths. These methods are difficult to apply in scenarios with high requirements for equipment portability and limited on-site deployment space, which leads to unstable extraction of deep components, false anomalies in inversion imaging, and poor physical interpretability of the model.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] In a first aspect, this application provides a short-offset transient electromagnetic method ultra-deep sounding system, comprising:

[0008] The signal receiving and acquisition adaptation module is used to receive the raw time-series transient electromagnetic signals in the field and adaptively configure the sampling rate and gain based on the weak amplitude and low signal-to-noise characteristics of the received signal and the detection offset constraint to obtain the initial state acquisition data.

[0009] The raw signal preprocessing module is used to perform noise characteristic identification, targeted filtering, baseline drift correction and weak signal enhancement on the initial state acquisition data to obtain preprocessed time series data.

[0010] The geometric effect correction and deep gain reconstruction module is used to analyze the attenuation law of the short offset geometric effect on the deep component, perform depth weighting compensation and phase correction on the preprocessed time series data based on the attenuation law, and reconstruct the deep gain curve by combining the weak signal enhancement characteristics of the preprocessed time series data to obtain the corrected and reconstructed data.

[0011] The layered response separation module is used to perform multi-scale layered medium separation and deep response extraction on the corrected and reconstructed data to obtain a segmented depth response set;

[0012] The inversion imaging and prior constraint module is used to construct a regularized inversion model based on geological priors and perform iterative inversion and resolution enhancement on the segmented depth response set to obtain a three-dimensional stratigraphic structure model.

[0013] The analysis results output and visualization module is used to visualize the three-dimensional stratigraphic structure model and corresponding signal response, and output data files for anomaly identification and field calibration.

[0014] Furthermore, the geometric effect correction and deep gain reconstruction module also includes a short offset multi-observation angle synthesis submodule, which is used to synthesize equivalent offset information between multiple near-offset survey lines to enhance deep energy and obtain a synthesized offset correction curve.

[0015] Furthermore, the geometric effect correction and deep gain reconstruction module further includes: a deep energy recovery submodule based on time-frequency sparse reconstruction, used to perform sparse tracking of weak deep energy in the time-frequency domain and reconstruct its amplitude and phase characteristics.

[0016] Furthermore, the geometric effect correction and deep gain reconstruction module performs weighted compensation on the deep gain using an adaptive depth contribution factor, Cd, which is formulated as follows:

[0017]

[0018] Where d is the depth estimate, s is the short offset of the survey line, and α, β, γ are scalar parameters adaptively estimated based on field calibration; the depth contribution factor is used to modulate the deep amplitude and phase correction.

[0019] Furthermore, the hierarchical response separation module employs interlayer orthogonal projection and multi-resolution ensemble decomposition to weaken near-field geometric crosstalk and improve the discriminability of deep responses.

[0020] Furthermore, the system also includes a parsing software architecture module, which integrates multiple parsing processes within the modular software and supports parameter customization, batch processing, and on-site calibration write-back to obtain a reproducible parsing process.

[0021] Secondly, this application provides a short-offset transient electromagnetic method for analyzing ultra-deep sounding signals, applied to the system described in the first aspect, the method comprising:

[0022] Reception and parameter matching steps: Receive the raw transient electromagnetic timing signal from the field and automatically match the sampling rate and receiving gain based on the signal amplitude and noise characteristics and the offset of the measurement line to obtain the initial state acquisition data;

[0023] Preprocessing steps: The initial state acquisition data is subjected to noise identification, targeted filtering, baseline drift correction, and weak signal enhancement to obtain preprocessed time series data;

[0024] Geometric correction and deep reconstruction steps: Analyze the attenuation law of the short offset geometric effect on the deep component, perform depth weighting compensation and phase correction on the preprocessed time series data based on the attenuation law, and reconstruct the deep gain curve by combining the weak signal enhancement characteristics of the preprocessed time series data to obtain the corrected and reconstructed data;

[0025] Separation and extraction steps: Perform multi-scale hierarchical response separation on the corrected and reconstructed data to obtain the deep segment response set;

[0026] Inversion imaging steps: Based on geological priors and regularization constraints, iterative inversion and resolution enhancement are performed on the deep segment response set to obtain a three-dimensional stratigraphic structure model;

[0027] Interpretation and calibration steps: Match the three-dimensional stratigraphic structure model with the signal response and identify anomalies. Adjust the analytical parameters based on the field calibration data and output the final analytical report.

[0028] Furthermore, short-offset multi-line synthesis and depth-weighted stacking are employed to enhance the energy of deep signals, and the compensation weights are adaptively adjusted based on field calibration data to ensure stable recovery under low signal-to-noise ratio conditions.

[0029] Compared with existing technologies, this invention provides a short-offset transient electromagnetic ultra-deep detection system and signal analysis method. Through quantitative geometric effect correction and depth-adaptive gain reconstruction, it systematically compensates for near-field coupling, amplitude and phase distortion, and deep energy attenuation caused by short offset. This mechanism not only synchronously recovers the weak amplitude signal in the late time domain and phase domain, but also maintains amplitude and phase consistency, ensuring that subsequent layer separation and inversion use data from the same source and comparable input. Compared with the traditional approach of simply increasing the transmission or expanding the array, this scheme can still stably extract the deep response even when equipment and field deployment are limited. It fundamentally expands the applicability of short-offset transient electromagnetic methods to ultra-deep targets, reduces the generation of false anomalies, and improves the physical interpretability of the inversion model. Attached Figure Description

[0030] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0031] Figure 1 A block diagram of a short-offset transient electromagnetic ultra-deep detection system provided in an embodiment of the present invention;

[0032] Figure 2 This is a flowchart of a short-offset transient electromagnetic method for analyzing ultra-deep detection signals according to the present invention. Detailed Implementation

[0033] To enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings.

[0034] As attached Figure 1 As shown:

[0035] Example 1:

[0036] A short-offset transient electromagnetic ultra-deep sounding system includes:

[0037] The signal receiving and acquisition adaptation module is used to receive the raw time-series transient electromagnetic signals in the field and adaptively configure the sampling rate and gain based on the weak amplitude and low signal-to-noise characteristics of the received signal and the detection offset constraint to obtain the initial state acquisition data.

[0038] Specifically, it is equipped with a broadband receiving coil or differential coil array, a reference noise channel, and a high-energy pulse transmitting device;

[0039] Based on the estimated target depth and the on-site noise spectrum, the early high sampling rate and late logarithmic sampling strategies, as well as the gain and the number of superpositions, are adaptively set.

[0040] Use GPS / total station to record precise location and geometric offset;

[0041] The sampling / amplification parameters are automatically adjusted through on-site rapid self-test (no-load / short-circuit) and noise monitoring to ensure that the energy at night can be acquired and the necessary phase information is recorded.

[0042] The raw signal preprocessing module is used to perform noise characteristic identification, targeted filtering, baseline drift correction and weak signal enhancement on the initial state acquisition data to obtain preprocessed time series data.

[0043] Specifically, reference channel-driven adaptive interference cancellation (ANC) suppresses power frequency and narrowband interference; burst noise removal and time-domain filtering are combined with robust baseline fitting (median or polynomial) to correct drift; and short-time Fourier or wavelet time-frequency windowing is used to improve SNR for late-night periods.

[0044] Furthermore, if necessary, instrument response deconvolution and phase correction are performed to output amplitude- and phase-preserving preprocessed timing data for subsequent use.

[0045] The geometric effect correction and deep gain reconstruction module is used to analyze the attenuation law of the short offset geometric effect on the deep component, perform depth weighting compensation and phase correction on the preprocessed time series data based on the attenuation law, and reconstruct the deep gain curve by combining the weak signal enhancement characteristics of the preprocessed time series data to obtain the corrected and reconstructed data.

[0046] Specifically, it also includes: a short offset multi-observation angle synthesis submodule, which is used to synthesize equivalent offset information between multiple near-offset survey lines to enhance deep energy and obtain a synthetic offset correction curve;

[0047] Further includes: a deep energy recovery submodule based on time-frequency sparse reconstruction, used to perform sparse tracking of weak deep energy in the time-frequency domain and reconstruct its amplitude and phase characteristics;

[0048] The geometric effect correction and deep gain reconstruction module performs weighted compensation on the deep gain using an adaptive depth contribution factor, Cd, which is formulated as follows:

[0049]

[0050] Where d is the depth estimate, s is the short offset of the survey line, and α, β, γ are scalar parameters adaptively estimated based on field calibration; the depth contribution factor is used to modulate the deep amplitude and phase correction.

[0051] Furthermore, equivalent offset synthesis is performed using adjacent survey lines to obtain greater equivalent offset information; the compensation weight sequence varying with depth is estimated through simulation and field calibration (borehole or known body) and the amplitude of the later time is weighted; phase consistency correction is used to eliminate geometric / system phase shift; all steps can be implemented using parameterized engineering functions or adaptive iterative calibration, and the reconstructed time series data after amplitude and phase correction is output.

[0052] The layered response separation module is used to perform multi-scale layered medium separation and deep response extraction on the corrected and reconstructed data to obtain a segmented depth response set;

[0053] Specifically, interlayer orthogonal projection and multi-resolution ensemble decomposition are used to weaken near-field geometric crosstalk and improve the resolvability of deep response.

[0054] Furthermore, multi-scale methods such as wavelet packet or multi-scale decomposition and empirical mode decomposition (EMD) are used, combined with inter-layer orthogonal projection or blind source separation (ICA) to suppress crosstalk; then, the components related to the depth are screened and retained through multi-channel consistency and cross-correlation criteria; several non-overlapping segmented depth responses are output for inversion, and phase information is retained to ensure imaging consistency.

[0055] The inversion imaging and prior constraint module is used to construct a regularized inversion model based on geological priors and perform iterative inversion and resolution enhancement on the segmented depth response set to obtain a three-dimensional stratigraphic structure model.

[0056] Specifically, it employs hierarchical or volumetric parameterization and introduces geological / borehole / seismic priors as constraints; it selects regularization frameworks (such as Tikhonov) and combines L-curve or cross-validation to automatically select regularization parameters; it uses coarse grid positioning → local refinement iterative inversion to improve resolution; it can perform parallel computation and multi-scale preconditioning to accelerate convergence, and outputs a three-dimensional conductivity / resistivity model with confidence for interpretation.

[0057] The analysis results output and visualization module is used to visualize the three-dimensional stratigraphic structure model and corresponding signal response, and output data files for anomaly identification and field calibration.

[0058] Specifically, it supports the export of 3D volumetric rendering, profiles, contour surfaces, and target lists (supporting formats such as SEG-Y / CSV / VTK / GeoTIFF); it provides an interactive interface for overlaying borehole and seismic data on profiles and visualizing uncertainty and confidence plots; it generates printable analytical reports and supports writing back to the parameter database on-site.

[0059] Specifically, the system also includes: a parsing software architecture module, which integrates multiple parsing processes within modular software and supports parameter customization, batch processing, and on-site calibration write-back to obtain a reproducible parsing process.

[0060] As shown above, through quantitative geometric effect correction and depth-adaptive gain reconstruction, the near-field coupling, amplitude and phase distortion, and deep energy attenuation caused by short offsets are systematically compensated. This mechanism not only synchronously recovers the weak amplitude signal in the late time domain and phase domain, but also maintains amplitude and phase consistency, ensuring that subsequent layer separation and inversion use data from the same source and comparable input. Compared with the traditional approach of simply increasing the emission or expanding the array, this scheme can still stably extract the deep response even when equipment and field deployment are limited, fundamentally expanding the applicability of short-offset transient electromagnetics to ultra-deep targets, reducing the generation of false anomalies, and improving the physical interpretability of the inversion model.

[0061] Example 2:

[0062] Ultra-deep exploration of sulfide ore bodies (hard rock area, target depth 650 m):

[0063] 1. Background and Objectives:

[0064] Objective: To discover and locate an approximately rectangular conductive body (typical metal sulfide), with a center depth of approximately 650m and dimensions of approximately 200m × 100m × 50m. The target exhibits a significant difference in conductivity compared to the surrounding rock (the target's conductivity is higher than that of the surrounding rock). The site environment consists of hard rock with a gentle surface, but is subject to interference from power frequency and man-made noise.

[0065] 2. On-site collection:

[0066] Survey line and station layout: The survey line spacing is 100m, and the station is set up with receiving points every 50m along the survey line; each station makes multiple receptions at short offset distances (mainly near offset, offset distance s is 20m).

[0067] Transmission system: small ring-shaped transmitting coil, ring diameter 30m, transmitting current I=300A ​​(pulse interruption transient, interruption rise / fall time <10µs), single-pole or dual-loop switchable.

[0068] Receiving system: Broadband induction coil (or differential coil array) records magnetic field attenuation; three-segment sampling:

[0069] 0–10ms: High sampling, fs=200kHz (capture fast early time);

[0070] 10ms–1s: Log-interval sampling (saving information during the process);

[0071] Total record length Trec = 2s (to ensure deep late-night coverage);

[0072] Stacking strategy: Stack Nstack=256 times per station to reduce random noise.

[0073] On-site reference channel: Deploy one reference noise sensor (to record power frequency and environmental noise) for subsequent adaptive noise suppression.

[0074] 3. Pretreatment:

[0075] Power frequency / strip noise suppression: Multi-channel adaptive interference cancellation (ANC) is used to estimate and remove 50 / 60Hz harmonics and stable narrowband interference using a reference channel.

[0076] Baseline drift correction: For each pulse sequence, local median filtering is first used to remove long-term drift, and then linear / quadratic baseline fitting is performed and subtracted.

[0077] Weak signal enhancement: Apply short-time Fourier framing + multi-window averaging (time-frequency stability criterion) to the late period (>10ms) to improve the late-time SNR; retain phase information for deep imaging.

[0078] 4. Geometric correction and deep gain reconstruction:

[0079] Objective: To quantitatively correct near-field geometric distortion and deep amplitude attenuation caused by short offsets, thereby recovering the effective deep energy spectrum that can be used for inversion.

[0080] Key points for handling:

[0081] Perform "short offset equivalent synthesis": In the horizontal direction, adjacent survey lines (±1~2 survey lines) are weighted and superimposed to increase the equivalent offset ratio information (synthetic offset curve).

[0082] Adaptive depth contribution factor (engineering representation, field calibration): A set of depth weight sequences is calibrated using field calibration holes / known targets or simulation data to perform weighted compensation on the amplitude of late-time data (the weights are adjusted as the estimated depth increases).

[0083] Phase correction: Based on the comparison of the synthetic curve with the baseline phase, the phase shift introduced by the system and geometry is removed (to ensure the phase consistency of subsequent inversion).

[0084] Output: Corrected and reconstructed data (amplitude and phase have been corrected, and equivalent offset has been synthesized).

[0085] 5. Hierarchical response separation:

[0086] Multi-scale wavelet packet decomposition + inter-layer orthogonal projection is adopted: scale decomposition is performed in the time domain to separate near-field coupling and deep slow decay components; orthogonal projection is applied to the decomposed components to suppress near-field crosstalk.

[0087] Output: A set of segmented depth responses (a subset of non-overlapping depth components).

[0088] 6. Inversion Imaging and Prior Constraints:

[0089] Prior information: Existing geological information (borehole resistivity, geological profile) is used as prior constraints. An iterative inversion process based on Tikhonov regularization is adopted, but the regularization parameters are automatically selected by L-curve combined with field cross-validation (to avoid manual parameter tuning).

[0090] Iterative strategy: First, perform coarse mesh inversion for localization, then locally refine the mesh for inversion to improve resolution (resolution enhancement strategy).

[0091] Output: Three-dimensional conductivity / resistivity distribution model.

[0092] 7. Interpretation, on-site calibration, and output of results (corresponding to claim: analytical result output and visualization module):

[0093] By comparing with borehole data and known ore body locations, the detection depth, positioning accuracy, and volume estimation error are assessed.

[0094] Output visualization products: 3D contour surface, depth profile, vectorized list of anomalies and confidence score.

[0095] 8. Test Results (Simulation + Blind Field Testing):

[0096] Simulation (based on 3D forward modeling): On noisy simulation data, after applying the geometric correction and reconstruction of this scheme, the average SNR in the late period is improved by about 4.2dB (compared to the conventional short offset direct processing), and the signal identifiability of the 650m depth target is improved from difficult to identify to invertible.

[0097] Blind field test (including borehole verification): Two 2km survey lines were tested in the known ore body area to finally detect and locate the target. The deviation between the inversion model and the borehole center was ≤40m, and the target volume estimation error was ±18%.

[0098] Example 3:

[0099] Detection of aquifers / fault conductors in sedimentary basins (shallow-deep mixed medium, target depth 900m);

[0100] 1. Background and Objectives:

[0101] Objective: To identify low-contrast conductive bodies (such as water-bearing infills or fault mudstone zones) within the basin, at a depth of approximately 900m, with conductivity relatively high compared to the background. The site's background noise is primarily industrial and traffic noise, and the terrain is relatively flat.

[0102] 2. On-site data acquisition, signal reception and acquisition adapter module:

[0103] Survey line layout: survey line spacing 150m, station spacing 75m; short offset acquisition mainly uses s=30m (small offset to ensure the economic efficiency of on-site deployment).

[0104] Transmission: Ring diameter 40m, I=350A, pulse interruption <10µs, to obtain a strong low-frequency energy delay component.

[0105] Sampling: Same as in Example 2, recording up to Trec=3s (to extract slower decaying late time), early time fs=250kHz, late time logarithmic sampling.

[0106] Stacking: Nstack=320.

[0107] 3. Pretreatment:

[0108] Enhancement strategy: Use dual-reference channel ANC (simultaneously record power frequency and traffic noise) to perform steady-state narrowband suppression and instantaneous impulse noise removal (pulse threshold filtering) in parallel.

[0109] Baseline and instrument response are uniformly calibrated (on-site no-load and short-circuit tests are conducted to obtain the system response function and deconvolve it).

[0110] 4. Geometric correction and deep reconstruction:

[0111] The method employs "multi-line equivalent migration synthesis" and "adaptive depth weight compensation". Unlike Example 1, the depth weight calibration here relies on the prediction of the basin formation velocity / conductivity model (with borehole or seismic data as priors) to more conservatively compensate for late-time amplitudes (avoiding overcompensation leading to false anomalies).

[0112] Output corrected and reconstructed data for hierarchical separation.

[0113] 5. Hierarchical response separation:

[0114] In the time domain, empirical mode decomposition (EMD) or wavelet decomposition combined with interlayer projection is used to focus on removing strong responses in shallow layers in order to improve the visibility of weak responses in deep layers.

[0115] 6. Inversion Imaging and Prior Constraints:

[0116] We employ layered priors and spatial smoothing regularization, iterating from coarse to fine, with local refinement focusing on refining the mesh around the target.

[0117] Output a 3D model and interpret it together with seismic / borehole information.

[0118] 7. Interpretation and Calibration:

[0119] The back borehole (single-hole verification) and seismic reflection profiles were used to confirm whether the inversion anomaly corresponded to the actual water-bearing / fault body.

[0120] 8. Test Results (Simulation + Actual Measurement):

[0121] Simulation and field comparisons show that, compared to conventional short-offset processing (without geometric correction), the detection probability of low-contrast conductors at a depth of 900m is significantly improved (from 65% to 80%); the average SNR at night is improved by approximately 3.0dB. The positioning deviation is approximately 60m, and the volume estimation error is ±22%.

[0122] The comprehensive comparison is shown in Table 1 below (Example 2 and Example 3: Conventional short offset direct processing (Baseline) vs. the proposed solution).

[0123] Table 1

[0124] Indicators (including definitions) Example 2 Baseline Example 2 (Proposed) Improved proportions Example 3 Baseline Example 3 (Proposed) Improved proportions Target detectable depth (m) 600 700 +16.7% 800 920 +15.0% Average SNR improvement (dB) during the late hour 0.0 (Baseline for reference) +4.2 dB — 0.0 +3.0 dB — Target lateral resolution (m) 60 48 -20.0% (better) 80 66 -17.5% Target detection rate / True positive rate (%) 72 88 +22.2% 65 80 +23.1% False alarm rate / False positive rate (%) 14 8 -42.9% (lower) 18 10 -44.4% (lower) Positioning deviation (m,) 60 40 -33.3% (more accurate) 85 60 -29.4% Volume estimation error (%) ±25% ±18% improve ±28% ±22% improve Processing time (hours / km) 2.5 3.2 +28% 2.8 3.6 +28% Notes (Main Improvement Measures) Direct temporal denoising and conventional inversion Multi-line synthesis, depth-weighted compensation, hierarchical separation, and prior-guided inversion — Same as above (parameters and longer recording times). Same as above (longer records and stronger superposition) —

[0125] Parameter description:

[0126] Offset distance s:

[0127] Meaning: The horizontal distance (m) between the receiving point and the center of the transmitting coil or the reference point.

[0128] Acquisition method: Direct measurement during on-site survey line layout (GPS / total station), determined during the data acquisition and design phase (20 m for Example 2, 30 m for Example 3).

[0129] Emitting current I (A):

[0130] Meaning: Peak current of the emitted pulse (A).

[0131] Acquisition method: Parameter settings of the transmitting equipment (pulse power supply) and measurement by a measuring ammeter (take 300–350 A).

[0132] Launch ring diameter (m):

[0133] Meaning: The diameter or equivalent coverage dimension of the transmitting loop coil.

[0134] Acquisition method: On-site physical measurement (30–40 m).

[0135] Sampling strategy (fs, record length Trec):

[0136] Meaning: Early high sampling rate fs (Hz) and total record length (s).

[0137] Acquisition method: Designed based on target depth and expected temporal decay characteristics; early high sampling (200–250 kHz) is used to capture early transients, and Trec is set to 2–3 s to cover deep slow decay.

[0138] Stack count Nstack:

[0139] Meaning: Number of pulse superpositions at each station.

[0140] Acquisition method: determined based on on-site noise level and time budget (take 256–320).

[0141] Deep contribution factor / deep weight (engineered weight sequence):

[0142] Meaning: A scalar sequence used for deep correlation weighted compensation of amplitude values ​​in the late period.

[0143] Acquisition methods: Identified through two types of means:

[0144] a. Simulation calibration: Generate reference curves and estimate the required compensation curves based on 3D forward modeling (using known / hypothetical formation models);

[0145] b. Field calibration: Actual measurements are taken at locations with boreholes or known shallow, medium, or deep targets, and the weighting function is fitted (field calibration is preferred to reduce model bias).

[0146] SNR (dB):

[0147] Meaning: Signal-to-noise energy ratio (average during late hours or over a whole time window); used to measure the availability of weak signals.

[0148] Acquisition method: Statistical estimation in the processing chain (e.g., the energy ratio of the signal window to the noise window, expressed logarithmically).

[0149] Resolution, positioning deviation, volume estimation error, etc.

[0150] Meaning: Conventional geophysical indicators (based on comparison with boreholes or known references).

[0151] Acquisition method: The results are obtained by spatially registering the inversion results with the borehole or ground truth values ​​(mean absolute error, relative error).

[0152] As shown above, this scheme integrates field calibration, equivalent migration synthesis, multi-scale separation, and prior constraint inversion into a closed-loop workflow. Field observation data is continuously fed back to the compensation weights, filtering strategies, and inversion constraints, thereby achieving real-time or near-real-time parameter self-adaptation. This closed-loop mechanism significantly enhances robustness under complex noise, multi-layered media, and geological prior uncertainties: it avoids artifacts caused by overcompensation through field calibration and improves the consistency between target location and volume interpretation using multi-source prior constraints. This translates to a lower false alarm rate, higher detection reliability, and analytical products that are easier for engineers to interpret and make decisions in field engineering applications.

[0153] In one embodiment, this application also provides a short-offset transient electromagnetic method for analyzing ultra-deep sounding signals, applied to the system in Embodiment 1, the method comprising:

[0154] The system receives the raw time-series transient electromagnetic signals from the field and automatically matches the sampling rate and receiving gain based on the signal amplitude and noise characteristics and the offset of the survey line to obtain the initial state acquisition data.

[0155] The initial state acquisition data is subjected to noise identification, targeted filtering, baseline drift correction, and weak signal enhancement to obtain preprocessed time series data;

[0156] The attenuation law of the deep component due to the short offset geometric effect is analyzed. Based on the attenuation law, depth weighting compensation and phase correction are performed on the preprocessed time series data. The deep gain curve is reconstructed by combining the weak signal enhancement characteristics of the preprocessed time series data to obtain the corrected and reconstructed data.

[0157] Multi-scale hierarchical response separation is performed on the corrected and reconstructed data to obtain the deep segment response set;

[0158] Based on geological priors and regularization constraints, the response set of the deep section is iteratively inverted and its resolution enhanced to obtain a three-dimensional stratigraphic structure model.

[0159] The three-dimensional geological structure model is mapped to the signal response and anomalies are identified. The analytical parameters are adjusted based on the field calibration data, and the final analytical report is output.

[0160] Among them, short-offset multi-line synthesis and depth-weighted stacking are used to enhance the energy of deep signals, and the compensation weights are adaptively adjusted based on field calibration data to ensure stable recovery under low signal-to-noise ratio conditions.

[0161] The beneficial effects of this system are the same as those of the embodiment of the short offset transient electromagnetic method for ultra-deep exploration, and will not be repeated here.

[0162] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A short-offset transient electromagnetic method ultra-deep sounding system, characterized in that, include: The signal receiving and acquisition adaptation module is used to receive the raw time-series transient electromagnetic signals in the field and adaptively configure the sampling rate and gain based on the weak amplitude and low signal-to-noise characteristics of the received signal and the detection offset constraint to obtain the initial state acquisition data. The raw signal preprocessing module is used to perform noise characteristic identification, targeted filtering, baseline drift correction and weak signal enhancement on the initial state acquisition data to obtain preprocessed time series data. The geometric effect correction and deep gain reconstruction module is used to analyze the attenuation law of the short offset geometric effect on the deep component, perform depth weighting compensation and phase correction on the preprocessed time series data based on the attenuation law, and reconstruct the deep gain curve by combining the weak signal enhancement characteristics of the preprocessed time series data to obtain the corrected and reconstructed data. The layered response separation module is used to perform multi-scale layered medium separation and deep response extraction on the corrected and reconstructed data to obtain a segmented depth response set; The inversion imaging and prior constraint module is used to construct a regularized inversion model based on geological priors and perform iterative inversion and resolution enhancement on the segmented depth response set to obtain a three-dimensional stratigraphic structure model. The analysis results output and visualization module is used to visualize the three-dimensional stratigraphic structure model and corresponding signal response, and output data files for anomaly identification and field calibration.

2. The short-offset transient electromagnetic ultra-deep sounding system according to claim 1, characterized in that, The geometric effect correction and deep gain reconstruction module also includes a short offset multi-observation angle synthesis submodule, which is used to synthesize equivalent offset information between multiple near-offset survey lines to enhance deep energy and obtain a synthetic offset correction curve.

3. The short-offset transient electromagnetic ultra-deep sounding system according to claim 1, characterized in that, The geometric effect correction and deep gain reconstruction module further includes a deep energy recovery submodule based on time-frequency sparse reconstruction, which is used to perform sparse tracking of weak deep energy in the time-frequency domain and reconstruct its amplitude and phase characteristics.

4. The short-offset transient electromagnetic ultra-deep sounding system according to claim 1, characterized in that, The geometric effect correction and deep gain reconstruction module performs weighted compensation on the deep gain using an adaptive depth contribution factor, Cd, which is formulated as follows: Where d is the depth estimate, s is the short offset of the survey line, and α, β, γ are scalar parameters adaptively estimated based on field calibration; The depth contribution factor is used to modulate the deep amplitude and phase correction.

5. The short-offset transient electromagnetic ultra-deep sounding system according to claim 1, characterized in that, The hierarchical response separation module employs interlayer orthogonal projection and multi-resolution ensemble decomposition to weaken near-field geometric crosstalk and improve the discriminability of deep responses.

6. The short-offset transient electromagnetic ultra-deep sounding system according to claim 1, characterized in that, The system also includes a parsing software architecture module, which integrates multiple modules within the modular software and supports parameter customization, batch processing, and on-site calibration write-back to obtain a reproducible parsing process.

7. A method for analyzing ultra-deep sounding signals using short-offset transient electromagnetic methods, characterized in that, Applied to the system as described in any one of claims 1-6, the method comprises: The system receives the raw time-series transient electromagnetic signals from the field and automatically matches the sampling rate and receiving gain based on the signal amplitude and noise characteristics and the offset of the survey line to obtain the initial state acquisition data. The initial state acquisition data is subjected to noise identification, targeted filtering, baseline drift correction, and weak signal enhancement to obtain preprocessed time series data; The attenuation law of the deep component due to the short offset geometric effect is analyzed. Based on the attenuation law, depth weighting compensation and phase correction are performed on the preprocessed time series data. The deep gain curve is reconstructed by combining the weak signal enhancement characteristics of the preprocessed time series data to obtain the corrected and reconstructed data. Multi-scale hierarchical response separation is performed on the corrected and reconstructed data to obtain the deep segment response set; Based on geological priors and regularization constraints, the response set of the deep section is iteratively inverted and its resolution enhanced to obtain a three-dimensional stratigraphic structure model. The three-dimensional geological structure model is mapped to the signal response and anomalies are identified. The analytical parameters are adjusted based on the field calibration data, and the final analytical report is output.

8. The method for analyzing ultra-deep detection signals using short-offset transient electromagnetic methods according to claim 7, characterized in that, Short-offset multi-line synthesis and depth-weighted stacking are used to enhance the energy of deep signals, and the compensation weights are adaptively adjusted based on field calibration data to ensure stable recovery under low signal-to-noise ratio conditions.

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