Applications of machine learning (ML) in earth sciences include geological mapping, gas leakage detection and geological feature identification. Machine learning is a subdiscipline of artificial intelligence aimed at developing programs that are able to classify, cluster, identify, and analyze vast and complex data sets without the need for explicit programming to do so.Mueller, J. P., & Massaron, L. (2021). Machine learning for dummies. John Wiley & Sons. Earth science is the study of the origin, evolution, and future of Earth. The planet's system can be subdivided into four major components including the solid earth, atmosphere, hydrosphere, and biosphere.
A variety of algorithms may be applied depending on the nature of the task. Some algorithms may perform significantly better than others for particular objectives. For example, convolutional neural networks (CNNs) are good at interpreting images, whilst more general neural networks may be used for soil classification, but can be more computationally expensive to train than alternatives such as support vector machines. The range of tasks to which ML (including deep learning) is applied has been ever-growing in recent decades, as has the development of other technologies such as unmanned aerial vehicles (UAVs), ultra-high resolution remote sensing technology, and high-performance computing. This has led to the availability of large high-quality datasets and more advanced algorithms.
Significance
Complexity of earth science Problems in earth science are often complex. It is difficult to apply well-known and described mathematical models to the natural environment, therefore machine learning is commonly a better alternative for such non-linear problems. Ecological data are commonly non-linear and consist of higher-order interactions, and together with missing data, traditional statistics may underperform as unrealistic assumptions such as linearity are applied to the model. A number of researchers found that machine learning outperforms traditional statistical models in earth science, such as in characterizing forest canopy structure, predicting climate-induced range shifts, and delineating geologic facies. Characterizing forest canopy structure enables scientists to study vegetation response to climate change. Predicting climate-induced range shifts enable policy makers to adopt suitable conversation method to overcome the consequences of climate change. Delineating geologic facies helps geologists to understand the geology of an area, which is essential for the development and management of an area.
Inaccessible data
In earth sciences, some data are often difficult to access or collect, therefore inferring data from data that are easily available by machine learning method is desirable. For example, geological mapping in tropical rainforests is challenging because the thick vegetation cover and rock outcrops are poorly exposed. Applying remote sensing with machine learning approaches provides an alternative way for rapid mapping without the need of manually mapping in the unreachable areas.
Reduce time costs Machine learning can also reduce the efforts done by experts, as manual tasks of classification and annotation etc. are the bottlenecks in the workflow of the research of earth science. Geological mapping, especially in a vast, remote area is labour, cost and time-intensive with traditional methods. Incorporation of remote sensing and machine learning approaches can provide an alternative solution to eliminate some field mapping needs.
Consistent and bias-free Consistency and bias-free is also an advantage of machine learning compared to manual works by humans. In research comparing the performance of human and machine learning in the identification of dinoflagellates, machine learning is found to be not as prone to systematic bias as humans. A recency effect that is present in humans is that the classification often biases towards the most recently recalled classes. In a labelling task of the research, if one kind of dinoflagellates occurs rarely in the samples, then expert ecologists commonly will not classify it correctly. The systematic bias strongly deteriorate the classification accuracies of humans.
Optimal machine learning algorithm The extensive usage of machine learning in various fields has led to a wide range of algorithms of learning methods being applied. Choosing the optimal algorithm for a specific purpose can lead to a significant boost in accuracy: for example, the lithological mapping of gold-bearing granite-greenstone rocks in Hutti, India with AVIRIS-NG hyperspectral data, shows more than 10% difference in overall accuracy between using support vector machines (SVMs) and random forest.
Some algorithms can also reveal hidden important information: white box models are transparent models, the outputs of which can be easily explained, while black box models are the opposite. For example, although an SVM yielded the best result in landslide susceptibility assessment accuracy, the result cannot be rewritten in the form of expert rules that explain how and why an area was classified as that specific class. In contrast, decision trees are transparent and easily understood, and the user can observe and fix the bias if any is present in such models.
If computational resource is a concern, more computationally demanding learning methods such as deep neural networks are less preferred, despite the fact that they may outperform other algorithms, such as in soil classification.
Usage
Mapping Geological or lithological mapping and mineral prospectivity mapping Geological or lithological mapping produces maps showing geological features and geological units. Mineral prospectivity mapping utilizes a variety of datasets such as geological maps and aeromagnetic imagery to produce maps that are specialized for mineral exploration. Geological, lithological, and mineral prospectivity mapping can be carried out by processing data with ML techniques, with the input of spectral imagery obtained from remote sensing and geophysical data. Spectral imaging is also used – the imaging of wavelength bands in the electromagnetic spectrum, while conventional imaging captures three wavelength bands (red, green, blue) in the electromagnetic spectrum.
Random forests and SVMs are some algorithms commonly used with remotely-sensed geophysical data, while simple linear iterative clustering convolutional neural network (SLIC-CNN) and convolutional neural networks (CNNs) are commonly applied to aerial imagery. Large scale mapping can be carried out with geophysical data from airborne and satellite remote sensing geophysical data, and smaller-scale mapping can be carried out with images from unmanned aerial vehicles (UAVs) for higher resolution.
Vegetation cover is one of the major obstacles for geological mapping with remote sensing, as reported in various research, both in large-scale and small-scale mapping. Vegetation affects the quality of spectral images, or obscures the rock information in aerial images.
+Example applications in geological, lithological, and mineral prospectivity mappingObjectiveInput datasetLocationMachine learning algorithms (MLAs)PerformanceLithological mapping of gold-bearing granite-greenstone rocksAVIRIS-NG hyperspectral dataHutti, IndiaLinear discriminant analysis (LDA), random forest,
support vector machine (SVM)SVM outperforms the other MLAs.Lithological mapping in the tropical rainforestMagnetic vector inversion, ternary RGB map, Shuttle Radar Topography Mission (SRTM), false color (RGB) of Landsat 8 combining bands 4, 3 and 2Cinzento Lineament, BrazilRandom forestTwo predictive maps were generated: (1) Map generated with remote sensing data only has a 52.7% accuracy when compared to the geological map, but several new possible lithological units are identified
(2) Map generated with remote sensing data and spatial constraints has a 78.7% accuracy but no new possible lithological units are identifiedGeological mapping for mineral explorationRadford, D. D., Cracknell, M. J., Roach, M. J., & Cumming, G. V. (2018). Geological mapping in western Tasmania using radar and random forests. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(9), 3075-3087.Airborne polarimetric terrain observation with progressive scans SAR (TopSAR), geophysical dataWestern TasmaniaRandom forestLow reliability of TopSAR for geological mapping, but accurate with geophysical data.Geological and mineralogical mappingMultispectral and hyperspectral satellite dataCentral Jebilet, MoroccoSupport vector machine (SVM)The accuracy of using hyperspectral data for classifying is slightly higher than that using multispectral data, obtaining 93.05% and 89.24% respectively, showing that machine learning is a reliable tool for mineral exploration.Integrating multigeophysical data into a cluster mapWang, Y., Ksienzyk, A. K., Liu, M., & Brönner, M. (2021). Multigeophysical data integration using cluster analysis: assisting geological mapping in Trøndelag, Mid-Norway. Geophysical Journal International, 225(2), 1142-1157.Airborne magnetic, frequency electromagnetic, radiometric measurements, ground gravity measurementsTrøndelag, Mid-NorwayRandom forestThe cluster map produced has a satisfactory relationship with the existing geological map but with minor misfits.High-resolution geological mapping with unmanned aerial vehicle (UAV)Ultra-resolution RGB imagesTaili waterfront, Liaoning Province, ChinaSimple linear iterative clustering-convolutional neural network (SLIC-CNN)The result is satisfactory in mapping major geological units but showed poor performance in mapping pegmatites, fine-grained rocks and dykes. UAVs were unable to collect rock information where the rocks were not exposed.Surficial geology mapping
remote predictive mapping (RPM)Aerial photos, Landsat reflectance, high-resolution digital elevation dataSouth Rae Geological Region, Northwest Territories, CanadaConvolutional neural networks (CNN), random forestThe resulting accuracy of CNN was 76% in the locally trained area, while 68% for an independent test area. The CNN achieved a slightly higher accuracy of 4% than the random forest.
Landslide susceptibility and hazard mapping Landslide susceptibility refers to the probability of landslide of a certain geographical location, which is dependent on local terrain conditions. Landslide susceptibility mapping can highlight areas prone to landslide risks, which is useful for urban planning and disaster management. Such datasets for ML algorithms usually include topographic information, lithological information, satellite images, etc., and some may include land use, land cover, drainage information, and vegetation cover according to the study requirements. As usual, for training an ML model for landslide susceptibility mapping, training and testing datasets are required. There are two methods of allocating datasets for training and testing: one is to randomly split the study area for the datasets; another is to split the whole study into two adjacent parts for the two datasets. To test classification models, the common practice is to split the study area randomly; however, it is more useful if the study area can be split into two adjacent parts so that an automation algorithm can carry out mapping of a new area with the input of expert-processed data of adjacent land.
+Example applications in landslide susceptibility and hazard mappingObjectiveInput datasetLocationMachine learning algorithms (MLAs)PerformanceLandslide susceptibility assessmentDigital elevation model (DEM), geological map, 30m Landsat imageryFruška Gora Mountain, SerbiaSupport vector machine (SVM),