# Reconstruction of Land and Marine Features by Seismic and Surface Geomorphology Techniques
## 6 Machine Learning  
Seismic reflection interpretation is usually a time-consuming process (especially with low-quality data) when the interpreter analyzes seismic data at a standard industrial workstation. This long process (potentially months) also influences the result, since the interpreter is a human that could have emotional biases when working on a project for long period of time, which might lead to less objectivity. Machine learning in seismic interpretation (seismic geomorphology, in particular) utilizes applied statistics that build  
Figure 5: Geobody extraction of deep-water channel-lobe system from seismic geomorphology technique. Note that 3D red-green block is pointing toward the north direction. computational models using various machine learning techniques such as random forests, decision trees, support-vector machines, convolutional neural networks, deep neural networks, and generative adversarial networks [124; 125; 126; 127; 128]. This requires input data that will be processed for training using applied statistics with computational algorithms to produce reliable outputs (Figure 6). With today's trend toward digitalization and automation, this process will significantly reduce seismic interpretation to a very short period, possibly hours or minutes depending on the size of the seismic reflection data. Furthermore, the utilization of machine learning in seismic interpretation processes covers most of the subsurface areas, including surfaces, geomorphology and facies interpretation, e.g., [129; 130; 131; 132; 133; 134; 135; 136; 137; 138; 139; 139; 140; 141], faults and fracture interpretation, e.g., [142; 143; 144; 145; 146; 147], and geological volume-global interpretation, e.g., [148; 149; 150]. Machine learning in horizon seismic interpretation is an important aspect on identification geomorphology, faults, reservoir, etc. [PERSON], [PERSON], [PERSON], and [PERSON] [137] introduced that seismic horizons follow the reflector dip, thus having similar instantaneous phase values (the same horizons). Therefore, automatic horizon-interpretation algorithms need to implement the integration between the reflector dip and instantaneous phase attributes [137]. Another type of machine learning on horizon seismic interpretation is utilizing dislocated horizons (faulted, truncated, etc.) where dynamic time warping and unwrapped instantaneous phase-constraint are used to correlate horizon grids in a 3D window [133]. Geomorphological interpretation using 3D seismic reflection data is usually time-consuming, and machine learning with automation processes definitely helps to reduce the working hours of an interpreter. [PERSON] and [PERSON] [134] and [PERSON] and [PERSON] [140] presented unsupervised machine learning (self-organizing maps) on identification of deep-water and volcanic seismic facies, geomorphology, and architectural elements. [PERSON] and [PERSON] [132] presented supervised learning based on an artificial neural network to automate the identification and delineation of mass-transport deposits' geomorphological surfaces and bodies out of 3D seismic reflection data (offshore). Manual seismic facies interpretation makes the results very subjective, depending on the experience and knowledge of the interpreter. [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] [131] presented automatic seismic facies interpretation utilizing deep learning, the convolutional neural network, and encoder-decoder architecture, whereas [PERSON], [PERSON], and [PERSON] [141] utilized Bayesian inference on supervised and semi-supervised deep learning on a shallow marine prograding delta offshore of the Netherlands. Faults and geological volume interpretations take most of the interpreter's time in manual 3D seismic interpretation. [PERSON], [PERSON], [PERSON], and [PERSON] [145] introduced \"FaultSeg3D\", an automatic and machine-learning tool that used an end-to-end convolutional neural network to produce an image-to-image fault segmentation.