Laser Power Fluctuations Research Articles

Cavity-Length Spectrum Sensing (CLSS) based on multi fiber Fabry-Pérot(F-P) cavities for refractive index monitoring

A Cavity-Length Spectrum Sensing (CLSS) based on multi fiber Fabry-Pérot(F-P) cavities is proposed for detecting refractive index. Traditional wavelength spectrum sensing (WSS) achieves refractive index monitoring by demodulating the wavelength shifting with changing of refractive index under fixed F-P cavity with a certain length. Fixing the wavelength as a reference, changing the refractive index and observing the intensity of wavelength, the curve shifting with length of the cavity, Length as the independent variable is called CLSS. Eight F-P cavities were fabricated and used to reconstruct the cavity-length spectrum. CLSS only need a single-frequency laser and photodetector, and avoid some expensive equipments, such as spectrometers and broadband lasers. On the other hand, compared to optical intensity sensing, CLSS remains immune to laser power fluctuations and allows for software-adjustable sensitivity, utilizing the sampling vernier effect. By demodulating the shifting of the interference dips, the refractive index of glucose solutions of different concentrations can be monitored. Experimental results show that when λ= 1550.00 nm, the sensitivity can reach −75.9778μm/RIU near a glucose solution with a refractive index of 1.341. In addition, the sampling vernier effect was utilized, and the sensitivity can be increased −2089.9705μm/RIU by changing the sampling interval. CLSS has low system cost, excellent stability and adjustable sensitivity, which has great potential applications in biomedical analysis, drug discovery, chemical production, etc.

Simultaneous sensing profiles of beam attenuation coefficient and volume scattering function at 180° using a single-photon underwater elastic-Raman lidar.

Lidar has emerged as a promising technique for vertically profiling optical parameters in water. The application of single-photon technology has enabled the development of compact oceanic lidar systems, facilitating their deployment underwater. This is crucial for conducting ocean observations that are free from interference at the air-sea interface. However, simultaneous inversion of the volume scattering function at 180° at 532 nm (βm) and the lidar attenuation coefficient at 532 nm (K l i d a r m) from the elastic backscattered signals remains challenging, especially in the case of near-field signals affected by the geometric overlap factor (GOF). To address this challenge, this work proposes adding a Raman channel, obtaining Raman backscattered profiles using single-photon detection. By normalizing the elastic backscattered signals with the Raman signals, the sensitivity of the normalized signal to variations in the lidar attenuation coefficient is significantly reduced. This allows for the application of a perturbation method to invert βm and subsequently obtain the K l i d a r m. Moreover, the influence of GOF and fluctuations in laser power on the inversion can be reduced. To further improve the accuracy of the inversion algorithm for stratified water bodies, an iterative algorithm is proposed. Additionally, since the optical telescope of the lidar adopts a small aperture and narrow field of view design, K l i d a r m tends to the beam attenuation coefficient at 532 nm (cm). Using Monte Carlo simulation, a relationship between cm and K l i d a r m is established, allowing cm derivation from K l i d a r m. Finally, the feasibility of the algorithm is verified through inversion error analysis. The robustness of the lidar system and the effectiveness of the algorithm are validated through a preliminary experiment conducted in a water tank. These results demonstrate that the lidar can accurately profile optical parameters of water, contributing to the study of particulate organic carbon (POC) in the ocean.

Atomic-Scale Dislocation Structure Evolution and Crystal Ordering Analysis of Melting and Crystallization Microprocesses in Laser Powder Bed Melting of γ-TiAl Alloys

Laser Powder Bed Fusion (LPBF) technology exhibits significant advantages in the manufacturing of components with high dimensional accuracy and intricate internal cavities. However, due to the inherent room-temperature brittleness and high-temperature gradient induced by the laser forming process, the LPBF fabrication of γ-TiAl alloy is often accompanied by the initiation and propagation of defects. The aim of this study is to investigate the forming process of γ-TiAl alloy by the LPBF method through molecular dynamics simulation, and to explain the microparticle arrangement and displacement evolution of the melting and crystallization processes, thus elucidating the link between the variations in the laser process parameters and defect generation during microscopic laser heating. The results show that during the melting process, the peaks of the radial distribution function (RDF) decrease rapidly or even disappear due to laser heating, and the atomic disorder is increased. Although subsequent cooling crystallization reorders the atomic arrangement, the peak value of the RDF after crystallization is still 19.3% lower than that of the original structure. By setting different laser powers (200–800 eV/ps) and scanning speeds (0.2–0.8 Å/ps), the effects of various process parameters on microforming and defect evolution are clarified. When the laser power increases from 200 to 400 eV/ps, the stable value of atomic displacement rises from 6.66 to 320.87, while it rises from 300.54 to 550.14 when the scanning speed is attenuated from 0.8 to 0.4 Å/ps, which indicates that, compared with the scanning speed, the atomic mean-square displacements are relatively more sensitive to the fluctuation of laser power. Dislocation analysis reveals that a higher laser power significantly increases the cooling rate during the forming process, which further aggravates the generation and expansion of dislocation defects.

Simultaneous atmospheric CH4, CO2 and O2 detection using fiber-optic switch (FOS)-based time-division multiplexed NIR laser long optical path absorption spectroscopy

A near-infrared (NIR) distributed feedback (DFB) laser sensor based on long optical path absorption spectroscopy technique was developed for simultaneous measurement of atmospheric methane (CH4), carbon dioxide (CO2) and oxygen (O2). A fiber-optic switch (FOS) in conjunction with the time-division multiplexing (TDM) technique was adopted to enable different lasers scan over the target gas absorption lines in turn. The long optical path wavelength modulation spectroscopy (WMS) technique with second harmonic detection as well as a laser power fluctuation correction method was used to improve the sensor’s precision and accuracy. For the selected gas absorption lines, located at 6046.945 cm-1, 6361.245 cm-1 and 7877.647 cm-1, the detection limit (1σ) of 0.034 parts per million (ppm) for CH4, 11.921 ppm for CO2 and 0.14% for O2 was achieved according to the concentration and noise level. Allan deviation analysis indicates that the 1-s measurement precision was 0.030 ppm for CH4, 11.518 ppm for CO2 and 0.14% for O2, which could be improved to 6.5E-4 ppm, 0.255 ppm and 0.005% at an optimal average time of 800 s. A two-day continuous measurement of the CH4, CO2, and O2 concentration from ambient air was carried out, which verifies the stability and robustness of the developed multi-gas sensor.

Unveiling the impact of laser power variations on microstructure, corrosion, and stress-assisted surface crack initiation in laser powder bed fusion-processed Ni-Fe-Cr alloy 718

Corrosion and stress-corrosion related failures often compromise the integrity of critical metallic components during their service, raising significant concerns. It is crucial to comprehend the crack initiation mechanism and the impact of alloy microstructure on this crack initiation process. It is known that the introduction of unique microstructures through metal additive manufacturing brings new challenges. This study aims to investigate, for the first time, the effects of microstructural alterations resulting from fluctuations in laser power during laser powder bed fusion on the surface cracking initiation mechanism and electrochemical behaviour of Ni-Fe-Cr alloy 718, which is widely used in applications that require exceptional strength and corrosion resistance. To carry out this investigation, microcapillary electrochemical methods were combined with high-resolution techniques (TEM, SEM, AFM). The findings emphasize the existence of an optimal range of process parameters that effectively mitigate corrosion and crack initiation susceptibility. This work demonstrated that slight deviations in laser power from this optimal value result in diverse alterations at the micro and submicron scales. These alterations include increased subgrain width, porosity, dislocation density, density of nanovoids, and distribution of carbides. Importantly, these changes, particularly in dislocation and nanovoid densities caused by minor variations in process parameters, significantly affect the material's susceptibility to corrosion initiation and stress-assisted surface cracking.

Optical-domain modulation cancellation method for background-suppression and dual-gas detection in light-induced thermo-elastic spectroscopy

To address the issues of signal saturation and measurement range reduction caused by the background in light-induced thermoelastic spectroscopy (LITES) employing the first harmonic (1 f) modulation, an optical-domain modulation cancellation method (MOCAM) was reported in the 1f-based LITES. A balance laser source with the opposite sine modulation phase as the excitation laser source synchronously excites the quartz tuning fork (QTF) to realize background suppression. By optimizing the modulation amplitude of the balance laser and the modulation phase difference between the two laser sources, the background level was suppressed by two orders of magnitude from ∼ 7 mV to ∼ 18 μV, close to the QTF thermal noise level (∼ 1 μV). Combining the excitation laser with the balance laser enables MOCAM to overcome common mode interferences, e.g. optical path shift, laser power fluctuation, and focus position change. Furthermore, methane and water vapor (CH4/H2O) dual-gas detection based on MOCAM and time-division multiplexing method was demonstrated, and measurement range is enlarged with easy calibration due to zero background. The MOCAM-based 1f-LITES technique delivers effective background suppression and provides robustness to common mode interferences, thus establishes a feasible solution for mobile vehicle or airborne gas monitoring applications with serious environmental disturbances.

In-Situ Detection for Atomic Density in the K-Rb-21Ne Co-Magnetometer via an Optical Heterodyne Interferometry

The low-frequency fluctuations of the atomic density within the cell can induce the longterm drift of the K-Rb-21Ne spin-exchange relaxation-free (SERF) co-magnetometer output, such that the accurate measurement of in situ atomic density is of great significance for improving the performance of co-magnetometer. In this paper, the complex refractive index model of the spin ensembles under the hybrid optical pumping condition is established first, according to which the relation between atomic density and its complex refractive index is revealed and an optical heterodyne-based scheme for atomic density detection is proposed. The dependence of the atomic density on the demodulated phase signal from the optical heterodyne-based scheme is provided by numerical simulations. After that, a dual acousto-optics frequency shifter (AOFS)-based optical heterodyne interferometry is constructed with a noise level below 1 mrad/Hz for frequencies > 1 Hz, and a compact SERF co-magnetometer is implemented as the testing medium, by which the atomic density detection with resolution of 0.40 K @ 473 K is reached and the experimental results agree well with theoretical simulations. Moreover, the detection scheme proposed in this paper has the properties of high detection sensitivity and immunity to laser power fluctuation, which are also proved experimentally.

Modeling noise in global Mølmer-Sørensen interactions applied to quantum approximate optimization

Many-qubit Molmer-Sorensen (MS) interactions applied to trapped ions offer unique capabilities for quantum information processing, with applications including quantum simulation and the quantum approximate optimization algorithm (QAOA). Here, we develop a physical model to describe many-qubit MS interactions under four sources of experimental noise: vibrational mode frequency fluctuations, laser power fluctuations, thermal initial vibrational states, and state preparation and measurement errors. The model parameterizes these errors from simple experimental measurements, without free parameters. We validate the model in comparison with experiments that implement sequences of MS interactions on two and three $^{171}$Yb$^+$ ions. The model shows good agreement after several MS interactions as quantified by the reduced chi-squared statistic $\chi^2_\mathrm{red}< 2$. As an application we examine MaxCut QAOA experiments on three and six ions. The experimental performance is quantified by approximation ratios that are $91\%$ and $83\%$ of the optimal theoretical values. Our model predicts $0.93^{+0.03}_{-0.02}$ and $0.92^{+0.06}_{-0.06}$, respectively, with disagreement in the latter value attributable to secondary noise sources beyond those considered in our analysis. With realistic experimental improvements to reduce measurement error and radial trap frequency variations the model achieves approximation ratios that are 99$\%$ of the optimal. Incorporating these improvements into future experiments is expected to reveal new aspects of noise for future modeling and experimental improvements.

Bespoke pulse design for robust rapid two-qubit gates with trapped ions

Two-qubit gate performance is vital for scaling up ion-trap quantum computing. Optimized quantum control is needed to achieve reductions in gate-time and gate error-rate. We describe two-qubit gates with addressed Raman beams within a linear trapped-ion chain by a quantum master equation (QME). The QME incorporates the single-ion two-photon effective Rabi frequency, Autler-Townes and vibrational Bloch-Siegert energy shifts, off-resonant transitions, Raman and Rayleigh scattering, laser-power fluctuations, motional heating, cross-Kerr phonon coupling, laser spillover, asymmetric addressing beams and an imperfect initial motional ground state, with no fitting parameters. Whereas state-of-the-art methods are oblivious to these effects in the gate design procedure. We employ global optimization to design pulse sequences for achieving a robust rapid two-qubit gate for seven trapped $^{171}$Yb$^{+}$ ions by optimizing over numerically integrated QME solutions. Here, robust means resilient against slow drift of motional frequencies, and rapid means gate execution where the effective Rabi frequency is comparable to the detuning of the laser from the ion's bare electronic transition. Our robust quantum control delivers rapid high-quality two-qubit gates in long ion chains, enabling scalable quantum computing with trapped ions.

On-line multi-gas component measurement in the mud logging process based on Raman spectroscopy combined with a CNN-LSTM-AM hybrid model

The qualitative and quantitative analysis of gas components extracted from drilling fluids during mud logging is essential for identifying drilling anomalies, reservoir characteristics, and hydrocarbon properties during oilfield recovery. Gas chromatography (GC) and gas mass spectrometers (GMS) are currently used for the online analysis of gases throughout the mud logging process. Nevertheless, these methods have limitations, including expensive equipment, high maintenance costs, and lengthy detection periods. Raman spectroscopy can be applied to the online quantification of gases at mud logging sites due to its in-situ analysis, high resolution, and rapid detection. However, laser power fluctuations, field vibrations, and the overlapping of characteristic peaks of different gases in the existing online detection system of Raman spectroscopy can affect the quantitative accuracy of the model. For these reasons, a gas Raman spectroscopy system with a high reliability, low detection limits, and increased sensitivity has been designed and applied to the online quantification of gases in the mud logging process. The near-concentric cavity structure is used to improve the signal acquisition module in the gas Raman spectroscopic system, thus enhancing the Raman spectral signal of the gases. One-dimensional convolutional neural networks (1D-CNN) combined with long- and short-term memory networks (LSTM) are applied to construct quantitative models based on the continuous acquisition of Raman spectra of gas mixtures. In addition, the attention mechanism is used to futher improve the quantitative model performance. The results indicated that our proposed method has the capability to continuously on-line detect 10 hydrocarbon and non-hydrocarbon gases in the mud logging process. The limitation of detection (LOD) for different gas components based on the proposed method are in the range of 0.0035%–0.0223%. Based on the proposed CNN-LSTM-AM model, the average detection errors of different gas components range from 0.899% to 3.521%, and their maximum detection errors range from 2.532% to 11.922%. These results demonstrate that our proposed method has a high accuracy, low deviation, and good stability and can be applied to the on-line gas analysis process in the mud logging field.

Simultaneous measurement of temperature and water concentration using a novel laser dispersion spectrum extraction method immune to carrier phase variation

Quantitative measurement of temperature and concentration is of wide interest in combustion diagnostics. In this paper, simultaneous temperature and water concentration measurements in a combustion flame were realized by using a novel laser dispersion spectrum extraction method immune to carrier phase variation. The laser dispersion phase signal obtained in the frequency domain was converted into the molecular free induction decay (FID) signal in the time domain by inverse Fourier transform. Then the FID signals of the dispersion phase and the carrier phase were separated in the time domain. The optimal temperature and concentration values were efficiently searched by an improved simulated annealing algorithm through the FID signal in the window function. Numerical simulations verified the noise immunity of the proposed method at different noise levels. Experiments were implemented on a methane-air laminar premixed flame, and the results showed that the proposed method performed better in measurement stability and accuracy than laser absorption spectrometry, especially in the presence of incident laser power fluctuations. This method is proven promising in the field of combustion diagnosis.

Single shot imaging for cold atoms based on machine learning

The ability to detect atoms in high spatiotemporal resolution provides a powerful tool for us to investigate the quantum properties of ultracold quantum gases. Plenty of useful imaging methods, including absorption imaging, phase contrast imaging and fluorescence imaging, have been implemented in detecting atoms. Among them, absorption imaging is the most widely used method in cold atoms laboratory. However, the traditional absorption imaging method is affected by perturbations such as interference between optical elements, fluctuation of laser power, frequency, and spatial position, resulting in residual spatially structured noise and degradation of imaging quality. Especially for regions with lower density or for longer time-of-flight, a large number of repetitions are often required to obtain better signal-to-noise ratio, which would increase the time cost and induce other noise. One must reduce the time between two imaging pulses to suppress the spatial noise. A better charge coupled device (CCD) with higher frame transfer rate or other method like fast-kinetic mode will be used to improve the imaging quality. In this paper, a single-shot cold atom imaging method based on machine learning is proposed, in which only one absorption imaging of cold atoms is required, and the corresponding background image can be generated through the neural network of an autoencoder. This effectively reduces the spatial striped noise in imaging, significantly improves the imaging quality, and makes it possible for cold atoms to be imaged multiple times in a single cycle.

High Precision and Large Dynamic Range Measurement of Laser Triangulation Displacement Sensor Using Diffraction Grating

The conventional laser triangulation displacement sensor (LTDS) is difficult to achieve large dynamic range and high precision measurement simultaneously, since LTDS is affected by factors such as limited detector size, nonlinear input and output variables, laser power fluctuation, speckle noise, electronic noise, etc. Given the above background, we proposed a modified LTDS using a diffraction grating to improve the dynamic range and precision simultaneously. Different from the conventional LTDS, the modified LTDS generates more than one spots on the image sensor, thereby the displacements with different ranges can be obtained from the multiple order diffraction spots. Ultimately, the dynamic range and measurement accuracy will be improved simultaneously by integrating these displacements. In this paper, the principle of the modified LTDS for large dynamic range and high precision measurement is described in detail, and we verified the validity and effectiveness of this idea through the experiment. Compared with the traditional LTDS, the experimental results show that the linearity of the modified LTDS is improved by a factor of 1.5685 and the dynamic range is improved 1.2986 times as well as maintaining the same linearity.

Suppression of laser beam’s polarization and intensity fluctuation via a Mach–Zehnder interferometer with proper feedback

Long ground-Rydberg coherence lifetime is interesting for implementing high-fidelity quantum logic gates, many-body physics, and other quantum information protocols. However, the potential well formed by a conventional far-off-resonance red-detuned optical-dipole trap that is attractive for ground-state cold atoms is usually repulsive for Rydberg atoms, which will result in the rapid loss of atoms and low repetition rate of the experimental sequence. Moreover, the coherence time will be sharply shortened due to the residual thermal motion of cold atoms. These issues can be addressed by a one-dimensional magic lattice trap, which can form a deeper potential trap than the traveling wave optical dipole trap when the output power is limited. In addition, these common techniques for atomic confinement generally have certain requirements for the polarization and intensity stability of the laser. Here, we demonstrated a method to suppress both the polarization drift and power fluctuation only based on the phase management of the Mach–Zehnder interferometer for a one-dimensional magic lattice trap. With the combination of three wave plates and the interferometer, we used the instrument to collect data in the time domain, analyzed the fluctuation of laser intensity, and calculated the noise power spectral density. We found that the total intensity fluctuation comprising laser power fluctuation and polarization drift was significantly suppressed, and the noise power spectral density after closed-loop locking with a typical bandwidth of 1–3000 Hz was significantly lower than that under the free running of the laser system. Typically, at 1000 Hz, the noise power spectral density after locking was about 10 dB lower than that under the free running of a master oscillator power amplifier system. The intensity–polarization control technique provides potential applications for atomic confinement protocols that demand fixed polarization and intensity.

Atomic spin precession detection method based on the Mach-Zehnder interferometer in an atomic comagnetometer.

A new method for the detection of atomic spin precession based on the Mach-Zehnder interferometer (MZI) is proposed and experimentally demonstrated. Different from the conventional polarization detection methods which obtain the atomic spin precession signal by measuring the change of the probe laser power, the proposed method uses the laser modulated by an electro-optic phase modulator (EOM) as the source of the interferometer, and obtains the atomic spin precession signal by measuring the phase difference between the two arms of the MZI. The output of interferometer is independent of the probe laser power, which avoids the system error caused by the fluctuation of the probe laser power, and the long-term stability of the system is effectively improved. At the same time, the method adopts high-frequency electro-optic modulation, which can effectively suppress low-frequency noise, such as 1/f noise, and can significantly improve the detection sensitivity. The rotation sensitivity and long-term stability of the atomic comagnetometer were tested using the MZI detection method and a typical detection method, respectively. The comparison results show that the proposed method has the highest low frequency sensitivity and the potential to improve the long-term stability of the system.

A Low-Drift Laser-Driven FOG Suitable for Trans-Pacific Inertial Navigation

We report a laser-driven fiber-optic gyroscope (FOG) with an angle random walk of 0.9 mdeg/√h, an Allan-deviation minimum of 4.9 mdeg/h, and an Allan-deviation maximum (or true drift) of 12.6 mdeg/h. This state-of-the-art performance was achieved by implementing an optical gate and a power-control feedback loop to suppress several sources of drift in the FOG, namely optical spikes that result from the square-wave biasing modulation, the nonlinear Kerr effect, drift in the detected power arising from spurious interferometers in the fiber circuit and laser-power fluctuations, and bias offset drift in the demodulation electronics. The measured output of this FOG was used to simulate 20 10-h trans-Pacific flights of an aircraft guided solely by inertial sensors. 95% of the flights landed within ±10 nmi of their intended destination. To our knowledge, this is the first report of a laser-driven FOG that meets the Federal Aviation Administration's Required Navigation Performance 10 criteria for aircraft navigation.

Light shift suppression with pulsed light detection in magnetic-state-selected cesium beam clocks.

Light detection is widely used in atomic clocks. The simple detecting structure induces the light shift which influences the clock's long-term stability. We introduce a new method to suppress light shift by using pulsed light instead of continuous light to detect atomic states. Under a suitable pulsed sequence, the part of the atoms which do not simultaneously interact with light and microwave field are detected. We demonstrate the validity of our approach in a magnetic-state-selected cesium beam clock. Using a well-tuned sequence, the light shift coefficient is reduced by a factor of about 10, in comparison with the continuous light detection scheme. In a clock stability test with extra light power noise, the result shows good immunity of the method to laser power fluctuations. We also analyze the sources of the clock short-term stability degradation, including the Dick effect and the fact that a reduced number of atoms is detected in the pulsed detection case.

Random Laser Based on Ytterbium-Doped Fiber with a Bragg Grating Array as the Source of Continuous-Wave 976 nm Wavelength Radiation

A random narrow-linewidth lasing at a wavelength of 976 nm was obtained in an ytterbium-doped germanophosphosilicate fiber with an array of weakly reflecting fiber Bragg gratings (FBGs). A random laser cavity was formed by implementing the standard phase mask method of FBG inscription directly during the fiber drawing process. The UV radiation pulses of a KrF excimer laser (248 nm wavelength) synchronized with the fiber drawing speed were used to fabricate the in-fiber array of hundreds of similar FBGs. The developed laser’s slope efficiency in the backward-pumping scheme was measured as high as 33%. The stable continuous-wave operation mode of the laser was detected. The magnitude of the laser power fluctuations depends linearly on the cavity length. The random laser cavity modified with a single highlyreflected (90%) FBG demonstrates significantly better power stability and higher slope efficiency than the same one without an FBG.

Mid-IR dispersion spectroscopy – A new avenue for liquid phase analysis

Mid-IR dispersion spectroscopy is an attractive, novel approach to liquid phase analysis that extends the possibilities of traditional methods based on the detection of absorption via intensity attenuation. This technique detects inherent refractive index changes (phase shifts) induced by IR light interaction with absorbing matter. In contrast to classic absorption spectroscopy, it provides extended dynamic range, baseline-free detection, constant sensitivity, and inherent immunity to power fluctuation. In this paper, we provide a detailed experimental and theoretical characterization and verification of this method with special focus on broadband liquid sample analysis. For this purpose, we develop a compact benchtop dispersion spectroscopy setup based on an EC-QCL coupled to a Mach-Zehnder interferometer. Phase-locked interferometric detection enables to fully harness the advantages of the technique. By instrument operation in the quadrature point combined with balanced detection, the full immunity towards laser power fluctuations and the environmental noise can be achieved. On the example of ethanol (0.5–50% v/v) dissolved in water, it is experimentally demonstrated that changes of the refractive index function are linearly related to concentration also for strongly absorbing, highly concentrated samples beyond the validity of the Beer-Lambert law. Characterization of the sensitivity and noise behavior indicates that the optimum applicable pathlength for liquid analysis can be extended beyond the ones for absorption spectroscopy. Experimental demonstration of the advantages over classical absorption spectroscopy illuminates the potential of dispersion spectroscopy as upcoming robust and sensitive way of recording IR spectra of liquid samples.

Study on Characteristic Raman Shift Screening Method Based on MPA for Raman Spectrum of Mine Water Inrush Source

In the process of mine water inrush disaster prevention, accurate and rapid identification of water inrush source type is of great significance to coal mine safety production. However, traditional hydrochemical methods have shortcomings such as time-consuming and complex detection. Therefore, a new idea of identifying mine water inrush source by Raman spectroscopy is proposed. Goaf water, roof sandstone fissure water, Ordovician limestone water, Taiyuan limestone water, and surface water as well as their mixed water samples are selected as research objects, and Raman spectral data of different water samples are collected by the Raman spectroscopy system. To eliminate the influence of laser power fluctuation and spectrometer system noise in Raman spectrum acquisition, detrend correction (DC), multiplicative scatter correction (MSC), standard normal variate transformation (SNV), first derivative (FD), and mean centering (MC) were used to preprocess the raw Raman spectra. Due to the large dimension and long analysis time of Raman spectrum data, the marine predator algorithm (MPA) is used to screen the characteristic Raman shifts of the Raman spectrum of water samples, and the characteristic Raman shift information that can best characterize the mine water samples is obtained. Finally, to verify the feasibility of MPA screening the characteristic Raman shifts of Raman spectrum of mine water inrush source, the selected characteristic Raman displacement information is used as input to construct BP neural network (BP), k-nearest neighbor algorithm (KNN), support vector machine (SVM), and decision tree (DT) classification models, respectively. Experiments show that SNV has the best preprocessing effect on the raw Raman spectrum, which can effectively eliminate part of the noise in the Raman spectrum data and improve the accuracy of Raman spectrum identification. Using MPA, 226 characteristic Raman shifts can be screened from 2048 Raman data points, reducing the number of Raman shifts to 11.04%, and the modeling accuracy of characteristic Raman shift information screened by MPA is higher than that of full Raman data. In addition, the average analysis speed of BP, KNN, SVM, and DT water source identification models is 7.61 times faster than that of all Raman data. The results show that MPA is adopted to screen the characteristic Raman displacement of mine water source Raman spectrum, which can effectively reduce the redundancy of Raman spectral data and greatly improve the speed of Raman spectral analysis, which is of great significance to ensure the real-time detection of the mine water source.