Real-time Decomposition Research Articles

Development a novel gas chromatography-mass spectrometry equipment for rapid offshore oil and gas logging

Abstract With the development of offshore oil and gas exploration, the exploration area and region are constantly expanding, and the demand for efficient and accurate response of drilling real-time oil and gas data is becoming increasingly urgent. In response to this demand, this paper introduces a new fast field oil and gas logging instrument based on gas chromatography-mass spectrometry technology, which increases the measurement speed of existing instruments by more than 2 times. Conventional gas phase logging methods can only measure light hydrocarbon components (C1-C5) data, but cannot measure heavy hydrocarbon components (C6-C8), which are the key data reflecting oil content. For this reason, the FLAIR gas chromatography-mass spectrometer developed by Schlumberger can detect gaseous and liquid alkanes within 90 seconds. However, during offshore drilling, this detection method is slow and cannot guarantee real-time detection, and data loss may occur in key formations. In response to the above problems, we have developed a fast field oil and gas logging instrument based on chromatography-mass spectrometry technology. The instrument creatively uses mass spectrometry real-time decomposition technology to complete the detection of C1-C8 components within 45 seconds, completely solving the problem of rapid on-site determination of C1-C8 multi-components, and the on-site experiment has the same accuracy as the FLAIR instrument. The instrument can significantly improve the speed and quality of on-site measurement data, thereby improving the production efficiency and decision-making ability of offshore drilling platforms. The instrument has a wide range of application potential and may even become an essential instrument for conventional oil and gas exploration in the future.

Two-stage forecasting of TCN-GRU short-term load considering error compensation and real-time decomposition

Two-stage forecasting of TCN-GRU short-term load considering error compensation and real-time decomposition

Why Have Long-term Treasury Yields Fallen Since the 1980s? Expected Short Rates and Term Premiums in (Quasi-) Real Time

Treasury yields have fallen since the 1980s. Standard decompositions of Treasury yields into expected short-term interest rates and term premiums suggest term premiums account for much of the decline. In an alternative real-time decomposition, term premiums have fluctuated in a stable range, while long-run expected short-term interest rates have fallen. For example, a real-time decomposition of the 10-yr. Treasury yield shows term premiums essentially equal in late 2013 and 2023, while the long-run value of expected short-term interest rates is estimated to have fallen in a manner similar to the FOMC’s Summary of Economic Projections and estimates from research on long-run neutral interest rates. These results suggest standard decompositions may overstate the role of term premiums in fluctuations of the yield curve.

Real-time decomposition technique for compressive light field display using the multiplex correlations

How to compress and decompose the high-dimensional light field information in real time is still a challenging task for compressive light field display. Traditional iterative algorithms suffer from slow convergence speed and limited image quality. Therefore, a real-time decomposition technique for compressive light field display using multiplex correlations is proposed. Firstly, the iteration initial value of the algorithm is optimized, by utilizing the spatial correlations of pixel multiplex light fields, which significantly improves the convergence speed and reduces noise. Secondly, the iterative task of high-dimensional matrix in the non-negative matrix factorization (NMF) algorithm is divided into highly parallel linear iterative tasks. A stochastic gradient descent (SGD) optimizer and GPU are used to parallel compress and decompose the light fields. Thirdly, addresses of light field data are reordered using the sign distance field (SDF) transformation in sheared camera frustum space, making the addressing process of compression and decomposition more efficient. A rendering pipeline is constructed that renders the compressive light fields using 3D model data directly. For a light field containing 5 × 5 viewpoints and 1024 × 1024 × 2 pixels, only 2-3 iterations are needed to approach the optimal solution. The decomposition efficiency is increased by 15.24 times. The frame rate of decomposition exceeds 30 frames per second (fps). A compressive light field display system has been built to realize 3D display, verifying the feasibility of the technique.

A novel paradigm: Addressing real-time decomposition challenges in carbon price prediction

Carbon price prediction serves as a market indicator and economic driver, facilitating the adoption of more environmentally friendly production methods to achieve the emission reduction targets outlined in the Paris Agreement. In recent years, time series analysis and decomposition techniques have been widely applied to carbon price forecasting. However, few researchers have considered the issue of data feature drift caused by real-time decomposition. Specifically, as the sample size increases, data features undergo changes, rendering the trained models unable to fit the new data. This paper explains the underlying reasons for this phenomenon from new perspectives and proposes a novel paradigm that replaces the intrinsic mode function with a single-step fuzzy particle. This new paradigm corrects the issue of data feature drift and concentrates noise into a completely new sequence during the preprocessing stage. In the subsequent processing steps, the loss of correlation between the sequence and time lag, as well as the sequence and carbon prices, is addressed through multi-information association. This paradigm hybrid model can be applied to deterministic and interval multi-step predictions. Experimental results on the datasets from Guangdong and Hubei demonstrate that the proposed model outperforms other comparative models, achieving better predictive results than existing decomposition-based forecasting models. In the case of Guangdong, the normalized root mean square error (NRMSE%) for one-step, three-step, and five-step deterministic predictions are 2.18%, 2.51%, and 2.89%, respectively. The average interval score (AIS) for interval predictions are −0.357 and − 0.2375, respectively.

A Bi-GRU-attention neural network to identify motor units from high-density surface electromyographic signals in real time.

To utilize surface electromyographics (sEMG) for control purposes, it is necessary to perform real-time estimation of the neural drive to the muscles, which involves real-time decomposition of the EMG signals. In this paper, we propose a Bidirectional Gate Recurrent Unit (Bi-GRU) network with attention to perform online decomposition of high-density sEMG signals. The model can give different levels of attention to different parts of the sEMG signal according to their importance using the attention mechanism. The output of gradient convolutional kernel compensation (gCKC) algorithm was used as the training label, and simulated and experimental sEMG data were divided into windows with 120 sample points for model training, the sampling rate of sEMG signal is 2048 Hz. We test different attention mechanisms and find out the ones that could bring the highest F1-score of the model. The simulated sEMG signal is synthesized from Fuglevand method (J. Neurophysiol., 1993). For the decomposition of 10 Motor Units (MUs), the network trained on simulated data achieved an average F1-score of 0.974 (range from 0.96 to 0.98), and the network trained on experimental data achieved an average F1-score of 0.876 (range from 0.82 to 0.97). The average decomposition time for each window was 28 ms (range from 25.6 ms to 30.5 ms), which falls within the lower bound of the human electromechanical delay. The experimental results show the feasibility of using Bi-GRU-Attention network for the real-time decomposition of Motor Units. Compared to the gCKC algorithm, which is considered the gold standard in the medical field, this model sacrifices a small amount of accuracy but significantly improves computational speed by eliminating the need for calculating the cross-correlation matrix and performing iterative computations.

A fast gradient convolution kernel compensation method for surface electromyogram decomposition

Decomposition of EMG signals provides the decoding of motor unit (MU) discharge timings. In this study, we propose a fast gradient convolution kernel compensation (fgCKC) decomposition algorithm for high-density surface EMG decomposition and apply it to an offline and real-time estimation of MU spike trains. We modified the calculation of the cross-correlation vectors to improve the calculation efficiency of the gradient convolution kernel compensation (gCKC) algorithm. Specifically, the new fgCKC algorithm considers the past gradient in addition to the current gradient. Furthermore, the EMG signals are divided by sliding windows to simulate real-time decomposition, and the proposed algorithm was validated on simulated and experimental signals. In the offline decomposition, fgCKC has the same robustness as gCKC, with sensitivity differences of 2.6 ± 1.3 % averaged across all trials and subjects. Nevertheless, depending on the number of MUs and the signal-to-noise ratio of signals, fgCKC is approximately 3 times faster than gCKC. In the real-time part, the processing only needed 240 ms average per window of EMG signals on a regular personal computer (IIntel(R) Core(TM) i5-12490F 3 GHz, 16 GB memory). These results indicate that fgCKC achieves real-time decomposition by significantly reducing processing time, providing more possibilities for non-invasive neuronal behavior research.

Integrated strategy for real-time wind power fluctuation mitigation and energy storage system control

To address the impact of wind-power fluctuations on the stability of power systems, we propose a comprehensive approach that integrates multiple strategies and methods to enhance the efficiency and reliability of a system. First, we employ a strategy that restricts long- and short-term power output deviations to smoothen wind power fluctuations in real time. Second, we adopt the sliding window instantaneous complete ensemble empirical mode decomposition with adaptive noise (SW-ICEEMDAN) strategy to achieve real-time decomposition of the energy storage power, facilitating internal power distribution within the hybrid energy storage system. Finally, we introduce a rule-based multi-fuzzy control strategy for the secondary adjustment of the initial power allocation commands for different energy storage components. Through simulation validation, we demonstrate that the proposed comprehensive control strategy can smoothen wind power fluctuations in real time and decompose energy storage power. Compared with traditional empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) decomposition strategies, the configuration of the energy storage system under the SW-ICEEMDAN control strategy is more optimal. Additionally, the state-of-charge of energy storage components fluctuates within a reasonable range, enhancing the stability of the power system and ensuring the secure operation of the energy storage system.

An Improved Ensemble-Strategy-Assisted Wind Speed Prediction Method for Railway Strong Wind Warnings

Reliable short-term wind speed prediction is one of the core technologies in the strong wind warning system for railway applications, which is of great significance for ensuring the safety of high-speed train operations and ancillary railway facilities. To improve forecasting accuracy, decomposition-based methods have attracted extensive attention due to their superior ability to address complex data characteristics (e.g., nonstationarity and nonlinearity). Currently, there are two pre-processing schemes for decomposition-based methods, i.e., one-time decomposition and real-time decomposition. In order to apply them better, this paper first expounds the difference between them, based on a combination of DWT (discrete wavelet transform) and CKDE (conditional kernel density estimation). The results show that although the one-time decomposition-based method has an unexceptionable accuracy, it only can provide offline prediction and thus may not be practical. The real-time decomposition-based method possesses stronger practicability and is able to provide online prediction, but it has limited accuracy. Then, an improved ensemble strategy is developed by optimizing the selection of appropriate decomposed components to conduct the prediction on the basis of real-time decomposition. This improved ensemble strategy provides an effective guidance for this selective combination, including taking historical information into consideration in the data. Finally, numerical examples and practicality analysis using two groups of measured wind speed data demonstrate that the proposed method is effective in providing high-precision online wind speed prediction. For example, compared with CKDE, the average degrees of improvement achieved by the proposed method in terms of MAE, RMSE, and MRPE, are 16.25%, 17.66%, and 16.93, respectively, while those compared with the traditional real-time decomposition method are 17.11%, 18.54%, and 16.84, respectively.

Unveiling decomposition dynamics: leveraging 3D models for advanced forensic analysis.

Forensic taphonomy, the study of post-mortem processes, is pivotal in modern forensic science. This short communication illuminates limitations in traditional 2D imaging, specifically digital photographs, within forensic taphonomy, and highlights the vast potential of 3D modeling techniques. Drawing from a recent study in Hawaii's tropical savanna, we unveil disparities between real-time observations and 2D photographs when assessing decomposition, emphasizing the importance of scoring method selection and the need to scrutinize 2D imaging's accuracy in forensic taphonomy. Conversely, 3D modeling techniques, an emerging powerhouse in forensic science, offer multidimensional data, including volume, surface area, and spatial relationships, allowing for comprehensive and precise representation of decomposition dynamics. Despite concerns about texture quality, 3D models yield objective data amenable to analysis by multiple experts, thus minimizing subjectivity and augmenting the reliability of forensic assessments. The potential for 3D modeling to bridge the gap between 2D imaging and real-time decomposition requires tailored methodologies. Future research should focus on standardizing protocols and fostering collaboration among forensic experts, technologists, and researchers to unleash 3D technology's full potential in advancing forensic taphonomy.

Quantifying uncertainty of marine water quality forecasts for environmental management using a dynamic multi-factor analysis and multi-resolution ensemble approach

Unpredictable climate change and human activities pose enormous challenges to assessing the water quality components in the marine environment. Accurately quantifying the uncertainty of water quality forecasts can help decision-makers implement more scientific water pollution management strategies. This work introduces a new method of uncertainty quantification driven by point prediction for solving the engineering problem of water quality forecasting under the influence of complex environmental factors. The constructed multi-factor correlation analysis system can dynamically adjust the combined weight of environmental indicators according to the performance, thereby increasing the interpretability of data fusion. The designed singular spectrum analysis is utilized to reduce the volatility of the original water quality data. The real-time decomposition technique cleverly avoids the problem of data leakage. The multi-resolution-multi-objective optimization ensemble method is adopted to absorb the characteristics of different resolution data, so as to mine deeper potential information. Experimental studies are conducted using 6 actual water quality high-resolution signals with 21,600 sampling points from the Pacific islands and corresponding low-resolution signals with 900 sampling points, including temperature, salinity, turbidity, chlorophyll, dissolved oxygen, and oxygen saturation. The results illustrate that the model is superior to the existing model in quantifying the uncertainty of water quality prediction.

WHAT DRIVES CHINA’S LONG-TERM ECONOMIC GROWTH TREND? A RE-MEASUREMENT BASED ON A TIME-VARYING MIXED-FREQUENCY DYNAMIC FACTOR MODEL

The unprecedented downward pressure of China’s economic growth trend raises several questions, including what the current level of China’s long-term economic growth trend is, and what drives and how to inhibit the downward trend. Therefore, we develop a time-varying mixedfrequency dynamic factor model using data with different start dates to measure the trend, and perform a real-time decomposition of changes in the trend. We find that the trend has entered a downward stage since 2007, left a high-speed phase since 2012, and stepped in an accelerated downward stage since 2018. The current level of the trend is about 4%. However, the lower limit of the 90% confidence interval is below 2%, which is lower than natural rate level. Additionally, decelerated capital deepening, diminishing demographic dividend and technological recession all drive the downward trend. Compared to the relatively weak push-down effects of capital deepening and demographic dividend that are less than two percentage points, the downward trend is mainly driven by technological recession. Given that technological progress is unlikely to improve significantly in the short run, mitigating the mismatch between technological progress and obsolete capital, revitalizing existing capital stock, and increasing the efficiency of technology utilization become more feasible means.

Photonic super-resolution millimeter-wave joint radar-communication system using self-coherent detection.

A millimeter-wave (MMW) joint radar-communication (JRC) system with super-resolution is proposed and experimentally demonstrated, using optical heterodyne upconversion and self-coherent detection downconversion techniques. The point lies in the designed coherent dual-band constant envelope linear frequency modulation-orthogonal frequency division multiplexing (LFM-OFDM) signal with opposite phase modulation indexes for the JRC system. Then the self-coherent detection, as a simple and low-cost means, is accordingly facilitated for both de-chirping of MMW radar and frequency downconversion reception of MMW communication, which circumvents costly high-speed mixers along with MMW local oscillators and, more significantly, achieves the real-time decomposition of radar and communication information. Furthermore, a super-resolution radar range profile is realized through the coherent fusion processing of dual-band JRC signals. In experiments, a dual-band LFM-OFDM JRC signal centered at 54 GHz and 61 GHz is generated. The two bands feature an identical instantaneous bandwidth of 2 GHz and carry an OFDM signal of 1 Gbaud, which helps to achieve a 6-Gbit/s data rate for communication and a 1.76-cm range resolution for radar.

Forecasting carbon prices based on real-time decomposition and causal temporal convolutional networks

Recently, global attention has been paid to climate change. On this account, the market-based carbon pricing scheme is developed to limit greenhouse gas emissions, where a proper grasp of the pricing mechanism is crucial for alleviating global warming. Accordingly, we propose a novel method to interpret carbon price dynamics, concurrently deriving the precise prediction and causality. Due to the nonlinearity and nonstationarity of carbon prices, we develop a real-time decomposition approach coupling the multiple ensemble patch transform (MEPT) and the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN). The MEPT captures the multi-resolution trends of the carbon prices series exactly, and then the ICEEMDAN extracts the fluctuation patterns. Additionally, we collect the numerous potential factors, involving energy sources, energy prices, stock market indices, and economic information. Furthermore, we developed causal temporal convolutional networks (CTCNs) to realize the accurate prediction and the proper causal inference simultaneously. The experimental results on the European Union Allowance (EUA) confirm the effectiveness and necessity of the real-time MEPT-ICEEMDAN decomposition. Moreover, the proposed MEPT-ICEEMDAN-CTCN model exhibits significant superiority in multi-step-ahead and quantile forecast, which realizes the 0.73881%, 1.04461%, and 1.23495% MAPE in one-, five-, and ten-step-ahead forecast respectively and 0.00032 PDQ0.1 and the 0.00285 PDQ0.9 in the quantile forecast. Meanwhile, it reveals the nonlinear Granger causality across the various horizons and quantiles for the first time. It is instructive and inspiring for policymakers, carbon-consumed industries, investors, and researchers.

Heat utilization efficiency analysis of gas production from hydrate reservoir by depressurization in conjunction with heat stimulation

The heat utilization efficiency of heat injection is a key factor for the available heat stimulation method in natural gas hydrate, and the heat transfer characteristics during natural gas production from hydrate reservoir require further analysis. In this work, based on the laboratory tests by the Pilot Hydrate Stimulator (PHS), the heat transfer process of hydrate decomposition in the PHS was investigated, and the real-time decomposition rate and the energy utilization efficiency of the hydrate decomposition were quantitatively studied. The experimental results showed that the energy consumption of hydrate decomposition was mainly obtained from the sensible heat hydrate bearing sediments and the heat transfer from the environment in the depressurization process. In the initial stage of heat injection, the energy consumption of hydrate decomposition mainly came from the heat contained in the injected hot water and the heat transfer through the boundaries. In the later stage of heat injection, all of the energy consumption for hydrate decomposition was obtained from heat injection. In this work, the heat transfer coefficient between the reservoir and the environment was first proposed to quantify the heat transfer through the boundary. The results showed that the heat transfer coefficient presented large fluctuations during the initial stage of the depressurization and the period when the reservoir temperature reached the environment temperature in the heat injection process. Meanwhile, the coefficient remained stable at the later stage of heat injection. These results indicated that the change in gas-water hydrate inside the reservoir and the change in reservoir temperature affected the heat transfer coefficient between the reservoir and the environment. Furthermore, the energy utilization efficiency and gas production rate were optimized for hydrate production when the reservoir temperature was controlled within the range close to the environmental temperature. These results may be of great significance for heat injection control during field production from hydrate reservoirs.

A ML-Based Wind Speed Prediction Model with Truncated Real-Time Decomposition and Multi-Resolution Data

Accurate wind speed prediction is of importance for long-span cross-sea bridges. To this end, data decomposition techniques are usually employed to promote accuracy of the prediction model. Since wind speed data come sequentially, real-time decomposition should be adopted. However, real-time decomposition may degrade the accuracy due to the end effect. In this paper, a novel scheme of real-time decomposition that is a combination of truncated real-time decomposition and multi-resolution data is developed. Specifically, truncated real-time decomposition firstly denoises the data and eliminates the end effect; high-resolution data are then introduced to compensate for the information loss. Further, a novel wind speed prediction model that consists of the proposed scheme and neural networks is proposed. Specifically, two gated recurrent unit neural networks are employed to extract features from the obtained original-resolution and high-resolution data, respectively, and a multi-layer perceptron is adopted to utilize extracted features and make predictions. The proposed model is validated on the monitoring wind speed data of two long-span cross-sea bridges. Specifically, the mean absolute error and the root of mean square error of the proposed model on the two datasets are 0.334, 0.445 and 0.233 and 0.316 m/s, which are smaller than benchmark models and demonstrate superiority of the proposed model.

Big multi-step ship motion forecasting using a novel hybrid model based on real-time decomposition, boosting algorithm and error correction framework

Big multi-step ship motion forecasting plays an essential role as ship safety warning in maritime operations. However, big multi-step prediction will weaken the correlation of time series and introduce large error accumulation. Real-time prediction also requires a simple prediction model structure, data input-output relationship in line with the actual application, and a high prediction accuracy. To realize the above requirements, a hybrid big multi-step forecasting model is proposed based on real-time wavelet packet decomposition (RTWPD), outlier robust extreme learning machine (ORELM), boosting algorithm, and least squares support vector machine (LSSVM)-based error correction method. Taking 10-step, 20-step, and 30-step as the research objects of big multi-step prediction, two sets of ship roll and pitch motion data obtained by practical experiment are provided to complete four comparison experiments, including the comparison of different decomposition algorithms, the comparison of different classical forecasting models, the comparison of different error correction methods and the comparison of the proposed model with different benchmark models. Taking the 10-step, 20-step, and 30-step prediction results of the pitch of dataset #2 as an example, the mean absolute errors (MAEs) of the proposed model are 0.1619°, 0.1742°, and 0.1808°, respectively; and the minimum Diebold-Mariano (DM) value is 4.88, which is greater than the upper limit of the 1 % confidence level, indicating that the prediction results of the proposed model differ significantly from those of the other models. And the root mean square error (RMSE) of the proposed model is about 0.20° for different data sets and different ship motions. The experimental results indicate that the proposed RTWPD-ORELM-AdaBoost.MRT-LSSVM (RWOALS) model is stable and feasible in practical applications, and it can provide reliable guidance for maritime operations.

Adaptive Real-Time Decomposition of Electromyogram During Sustained Muscle Activation: A Simulation Study.

Real-time decomposition of electromyogram (EMG) into constituent motor unit (MU) activity has shown promising applications in neurophysiology and human-machine interactions. Existing decomposition methods could not accommodate stochastic variations in EMG signals such as drifts of action potential amplitudes and MU recruitment-derecruitment (rotation) patterns during long-term recordings. The objective of this study was to develop an adaptive real-time decomposition approach suitable for prolonged muscle activation. We developed a parallel-double-thread computation algorithm. The backend thread initiated and periodically refined and updated the MU information (separation matrix) using independent component analysis and convolution kernel compensation. The frontend thread performed the real-time decomposition. We evaluated our algorithm on synthesized high-density EMG signals, in which MUs were recruited-derecruited sporadically and MU action potentials amplitude drifted over time. Different signal-to-noise levels were also simulated. Compared with the decomposition without the adaptive processes, periodically fine-tuned and updated separation matrix increased identifiable MU number by 3-4 fold over 30-minute of signals. The increased MU number was more prominent at higher signal-to-noise ratios. The decomposition accuracy also increased by up to 10% with greater improvement observed at higher muscle contraction levels. The adaptive algorithm can maintain the decomposition performance over time, allows us to continuously track the same MUs during sustained activation, and, at the same time, can add newly recruited MU information to existing separation matrix. Our approach showed robust performance over time, which has the potential to longitudinally evaluate MU firing and recruitment properties and improve neural decoding performance for neural-machine interactions.

Highly Accurate Real-Time Decomposition of Single Channel Intramuscular EMG.

Real-time intramuscular electromyography (iEMG) decomposition, as an identification procedure of individual motor neuron (MN) discharge timings from a streaming iEMG recording, has the potential to be used in human-machine interfacing. However, for these applications, the decomposition accuracy and speed of current approaches need to be improved. In our previous work, a real-time decomposition algorithm based on a Hidden Markov Model of EMG, using GPU-implemented Bayesian filter to estimate the spike trains of motor units (MU) and their action potentials (MUAPs), was proposed. In this paper, a substantially extended version of this algorithm that boosts the accuracy while maintaining real-time implementation, is introduced. Specifically, multiple heuristics that aim at resolving the problems leading to performance degradation, are applied to the original model. In addition, the recursive maximum likelihood (RML) estimator previously used to estimate the statistical parameters of the spike trains, is replaced by a linear regression (LR) estimator, which is computationally more efficient, in order to ensure real-time decomposition with the new heuristics. The algorithm was validated using twenty-one experimental iEMG signals acquired from the tibialis anterior muscle of five subjects by fine wire electrodes. All signals were decomposed in real time. The decomposition accuracy depended on the level of muscle activation and was when less than 10 MUs were identified, substantially exceeding previous real-time results. Single channel iEMG signals can be very accurately decomposed in real time with the proposed algorithm. The proposed highly accurate algorithm for single-channel iEMG decomposition has the potential of providing neural information on motor tasks for human interfacing.

Decomposition variability between the scene and autopsy examination and implications for post-mortem interval estimations

Forensic experts rely on scene and/or autopsy photographs to estimate the post-mortem interval (PMI) when an in-situ assessment of decomposition is unfeasible. The degree of decomposition may vary between the scene and autopsy, which importantly could affect estimations of the unknown PMI in forensic casework. This study aimed to investigate decomposition variability between the scene and autopsy and assess the subsequent effect on the accuracy of PMI estimations. Scene and autopsy photographs from 94 cases with known PMI were used from the Allegheny County Office of the Medical Examiner in Pittsburgh, United States. The total decomposition scoring (TDS) method measured the overall decomposition level, and 28 markers of decomposition were recorded as a percentage of the total body surface area (TBSA). In 60% of cases the TDS had increased at autopsy causing significant overestimations of the autopsy PMI and 86% of decomposition markers varied between the scene and autopsy. Decomposition progressed during mortuary time lags (MTL) of 3–44 h, where bodies were stored in a pre-autopsy refrigerator at 4 °C, suggesting that refrigeration may not always delay decomposition. This research also assisted in validating photographs as a proxy for real-time decomposition assessments. While the autopsy photographs conferred higher quality than the scene photographs, the scene photographs produced more accurate PMI estimations. Forensic experts should exhibit caution when estimating the PMI from autopsy photographs alone, as they may not accurately reflect scene decomposition. To prevent misinterpretation of the PMI estimation, both scene and autopsy photographs should always be requested.